United States     Office of the Associate Director
   Environmental Protection Compliance Assurance and Enforcement Division
   Agency       Region 6, Dallas, TX 75202
                 ONTGOMERY
CIS  Screening  Tool T~

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             US EPA Region 6 GIS Screening Tool (GISST) User's Manual
                                   Prepared by
                    U. S. Environmental Protection Agency Region 6
                    Compliance Assurance and Enforcement Division
                         Office of Planning and Coordination
                                    Dallas, TX
S. L. Osowski, G. D. Carney, J. D. Swick, J. A. Danielson, D. A. Parrish, and D. Lueckenhoff.
                                   Version 1.1
                                 November 2005

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Citation Info:
Osowski, S. L., G. D. Carney, J. D. Swick, J. A. Danielson, D. A. Fairish, and D. Lueckenhoff.
2005. US EPA Region 6 GIS Screening Tool (GISST) User's Manual. Version 1.1. US
Environmental Protection Agency Region 6, Dallas, TX.
Additional copies of this report are available
                     via the Internet at http://www.epa.gov/region6/6en/xp/enxp2a3.htm
                     Sharon L. Osowski, Ph.D., (214) 665-7506, osowski.sharon@epa.gov
Cover shows three GISST criteria and cumulative scores for locations in east Texas.

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                                  Table of Contents

Chapter	Page

CHAPTER 1: HOW TO USE GISST	1
      What is it?	1
      How is GISST different from other GIS tools? 	1
      Who can use GISST? 	2
      How does it work?  	2
      What are the benefits? 	3
      What are the drawbacks?	5
      Who do I contact if I have further questions?  	5

CHAPTER 2: BACKGROUND PRINCIPLES AND CONCEPTS  	7
      Introduction	7
      NEPA	8
      Cumulative Impacts Assessment	9
      Watershed-Based Assessments  	11
      Decision Structures  	12
      GIS	13
      Relationship to SAB Report	14

CHAPTERS: CRITERIA DEVELOPMENT  	20
      Overall Structure	20
      Area Criterion	22
      Vulnerability Criteria	22
      Impact Criteria	23
      Criteria Groups 	23
             Water Quality 	24
             Ecological 	24
             Air Quality	25
             Socioeconomic  	25
             Toxicity	25
             CAFOs	26
             Pollution Prevention	26

CHAPTER 4: APPLICATIONS	27
      Introduction	27
      Swine Concentrated Animal Feeding Operation (CAFO) New Source Determination	28
      ffl-69 NAFTA International Corridor  	36
      NEPA Document Preparation and Review	69

LITERATURE CITED  	78

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                                    LIST OF TABLES

Table 1. Relationship of the SAB framework ecological attributes to GISST
              criteria. (P) indicates the GISST criterion is provisional	16
Table 2. Types of Data and GIS coverages and their sources	40
Table 3. Sample GISST output (2 mile buffers) for Aransas National Wildlife
              Refuge, Rancho, Blackjack and Lamar Units	71
                                   TABLE OF FIGURES

Figure 1. Surface Water Quantity criterion scores for Oklahoma	30
Figure 2. Degree of Vulnerability for five subwatersheds in Oklahoma	31
Figure 3. Degree of Impact for five subwatersheds in Oklahoma	32
Figure 4. Degree of Impact for five subwatersheds in Oklahoma	33
Figure 5. Degree of Vulnerability for each swine feedlot (CAFO) facility
       in five subwatersheds in Oklahoma	34
Figure 6. Degree of Impact for each swine feedlot (CAFO) facility
       in five subwatersheds in Oklahoma	35
Figure 7. Proposed National Interstate 69 corridor	37
Figure 8. Comparison of a) IH69 Congressional study area (SIUs)
       and b) IH69 recommended reasonable corridor	38
Figure 9. IH69 Congressional study area (SIUs) corridor: initial
       GISST cumulative result overlay  	50
Figure 10. Comparison of a) IH69 Congressional study area (SIUs)
       and b) IH69 recommended reasonable corridor: population criterion  	51
Figure 11. Comparison of a) IH69 Congressional study area (SIUs)
       and b) IH69  recommended reasonable corridor: minority criterion 	52
Figure 12. Comparison of a) IH69 Congressional study area (SIUs)
       and b) IH69 recommended reasonable corridor: economically-stressed criterion	53
Figure 13. Comparison of a) IH69 Congressional study area (SIUs)
       and b) IH69 recommended reasonable corridor: agriculture criterion  	54
Figure 14. Comparison of a) IH69 Congressional study area (SIUs)
       and b) IH69 recommended reasonable corridor: managed lands criterion	55
Figure 15. Comparison of a) IH69 Congressional study area (SIUs)
       and b) IH69 recommended reasonable corridor: hazardous waste criterion   	56
Figure 16. Comparison of a) IH69 Congressional study area (SIUs)
       and b) IH69 recommended reasonable corridor: stream density criterion  	57
Figure 17. Comparison of a) IH69 Congressional study area (SIUs)
       and b) IH69 recommended reasonable corridor: impaired streams criterion   	58
Figure 18. Comparison of a) IH69 Congressional study area (SIUs)
       and b) IH69 recommended reasonable corridor: wetlands criterion	59
Figure 19. Comparison of a) IH69 Congressional study area (SIUs)

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       and b) IH69 recommended reasonable corridor: floodplain criterion	60
Figure 20. Comparison of a) IH69 Congressional study area (SIUs)
       and b) IH69 recommended reasonable corridor: ozone nonattainment criterion  	61
Figure 21. Comparison of a) IH69 Congressional study area (SIUs)
       and b) IH69 recommended reasonable corridor: TEAP diversity criterion  	63
Figure 22. Comparison of a) IH69 Congressional study area (SIUs)
       and b) IH69 recommended reasonable corridor: TEAP rarity criterion	64
Figure 23. Comparison of a) IH69 Congressional study area (SIUs)
       and b) IH69 recommended reasonable corridor: TEAP sustainability criterion  	65
Figure 24. Comparison of a) IH69 Congressional study area (SIUs)
       and b) IH69 recommended reasonable corridor: TEAP composite criterion 	66
Figure 25. Comparison of a) IH69 Congressional study area (SIUs)
       and b) IH69 recommended reasonable corridor: cumulative results  	67
Figure 26. Map of Aransas National Wildlife Refuge Lamar,
       Blackjack, and Rancho La Bahia units  	74
Figure 27. Sample letter explaining GISST output	75

APPENDIX A: FINALIZED CRITERIA  	  A-l
       Introduction	  A-l
       Water Quality  	  A-2
              Surface Water Use	  A-2
              Water Quality (STORETData) 	  A-3
              Rainfall	  A-4
              Water Releases  	  A-5
              Surface Water Quantity (Stream Density)	  A-6
              Distance to Surface Water	  A-7
              Ground Water Probability  	  A-8
              Ground Water Quality	  A-9
              Unified Watershed Assessment (State Priority Data)	  A-10
              Clean Water Act 303(d) Segments (State Priority Data, TMDLS)  	  A-l 1
              Average Stream Flow  	  A-12
              Sole Source Aquifer	  A-13
              Floodplain	  A-14
              Aquifer/Geology Rating 	  A-15
              Channelization	  A-16
              Individual Well Water	  A-17
              Septic Tank and Cesspool Use	  A-18
              TRI1 Reported Water Releases 	  A-19
              Soil Permeability  	  A-20
       Ecological  	  A-21
              Agricultural Lands  	  A-21
              Wetlands	  A-22
              Wildlife Habitat  	  A-23

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       Wildlife Habitat Quality (Land Use Data)	  A-24
       Habitat Fragmentation	  A-25
       Federally Listed Endangered and Threatened Species	  A-26
       State Listed Endangered and Threatened Species	  A-27
       Endangered Species Act Compliance	  A-28
       Ecologically Significant Stream Segments 	  A-29
       TEAP Diversity	  A-30
       TEAPRarity  	  A-31
       TEAP Sustainability	  A-32
       TEAP Composite/Ecological Importance	  A-34
       Road Density	  A-35
       Watershed/Geographic Area  	  A-36
       Density of Managed Lands	  A-37
Air Quality	  A-38
       Air Quality	  A-38
       Ozone Nonattainment	  A-39
       TRI1 Reported Air Releases	  A-40
Socioeconomic  	  A-41
       Colonias  	  A-41
       High School Education	  A-42
       Educational Achievement Ranking 	  A-43
       Economic	  A-44
       Minority	  A-45
       Age	  A-46
       Children	  A-48
       Older Population	  A-50
       Pregnancy	  A-51
       Population Change	  A-53
       Population Density	  A-54
       Total Population	  A-55
       Houses Lacking Complete Plumbing	  A-56
       Telephone Communications  	  A-57
       Ability to Speak English	  A-58
       Linguistic Isolation	  A-59
       Foreign Born	  A-60
       Cultural Resources	  A-61
Toxicity	  A-62
       Toxicity Weighted TRI Water Releases  	  A-62
       Toxicity Weighted TRI AIR Releases	  A-63
       Toxicity Weighted RCRA-BRS2 Data  	  A-64
       Other Industries, Pollution Sources, or Protected Lands (Hazardous Waste) . .  .  A-65
CAFO	  A-66

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             Livestock Population Density	 A-66
             Lagoon Loading Rate	 A-67
             Lagoon Treatment System Liner	 A-68
             Land Application Technology	 A-69
             Nitrogen Budget	 A-70
             Phosphorus Budget  	 A-71
             Lagoon Storage Capacity	 A-72
             Well Head Protection	 A-73
             Employment in CAFO Industry 	 A-74
             Odor	 A-75
             Transportation near CAFOs	 A-76
             Density of CAFOs	 A-77
             Proximity of CAFOs	 A-78

APPENDIXB: PROVISIONAL CRITERIA  	B-l
       Introduction	B-l
       Criteria 	B-2
             Severity of Ozone Pollution 	B-2
             Employment	B-3
             Age of Homes	B-5
             RCRA Permitted Units	B-6
             RCRA Hazardous Waste Disposal	B-7
             Water Design Flow Data	B-8
             Density of National Historical Places	B-9
             Proximity of National Historical Places	B-10
             Environmental Assessment	B-l 1
             Pollution Prevention	B-12
             Model Energy Code	B-13
             Energy Efficient Office Equipment	B-15
             Energy Efficient (EE) Appliances  	B-16
             Lighting System Upgrade	B-17
             Million Solar Roofs Initiative	B-18
             Federal Energy Management Program 	B-19
             Proximity of Managed Lands	B-20
             Unregulated1 CAFO2 Facilities	B-21
             Presence of Aquifer	B-22
             Landscape Texture	B-23
             Landscape Aggregation	B-24
             Patch Area	B-25

APPENDIX C: GIS PROGRAMMING	  C-l
       Introduction	  C-l

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      AMLs	 C-3
             CRIAAQUI.AML	 C-3
             CRIACENSUS.AML	 C-3
             CRIACLIP.AML 	 C-18
             CRIADELETE.AML	 C-23
             CRIADISTANCE.AML	 C-25
             CRIAENVIRO.AML 	 C-26
             CRIAFLOOD.AML	 C-27
             CRIALANDUSE.AML	 C-30
             CRIALOADTRACKERAML  	 C-36
             CRIAMAIN.AML  	 C-40
             CRIARDS.AML	 C-46
             CRIASTATSGO.AML	 C-47
             CRIATRI.AML	 C-48
             CRIAWATERSHED.AML	 C-50
             CRIAWTRCHAN.AML	 C-51
             CRIAWTRQUAN.AML	 C-52
             CRIAWTRSURFAAML	 C-53

APPENDIX D: PEER REVIEW LOG	 D-l
      Figure D-l. Locations where GISST has been applied	 D-6

APPENDIX E: Additional maps and IH69 Individual SIU 3 maps	E-l
      Figure E-l. Comparison of a) IH69 Congressional study area
             (SIUs) and b) IH69 recommended reasonable corridor:
             wildlife habitat criterion	E-l
      Figure E-2. Comparison of a) IH69 Congressional study area
             (SIUs) and b) IH69 recommended reasonable corridor:
             Federally-listed species criterion	E-2
      Figure E-3. Comparison of a) IH69 Congressional study area
             (SIUs) and b) IH69 recommended reasonable corridor:
             State listed species criterion	E-3
      Figure E-4. Comparison of a) IH69 Congressional study area
             (SIUs) and b) IH69 recommended reasonable corridor:
             ecologically significant streams criterion	E-4
      Figure E-5. IH69 Segment (SIU) 3: initial GISST cumulative result overlay	E-5
      Figure E-6. IH69 Segment (SIU) 3: population criterion	E-6
      Figure E-7. IH69 Segment (SIU) 3: minority criterion	E-7
      Figure E-8. IH69 Segment (SIU) 3: economically-stressed criterion	E-8
      Figure E-9. IH69 Segment (SIU) 3: agriculture criterion	E-9
      Figure E-10. IH69 Segment (SIU) 3: managed lands criterion 	E-10
      Figure E-l 1. IH69 Segment (SIU) 3: hazardous waste criterion	E-l 1

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       Figure E-12. IH69 Segment (SIU) 3: stream density criterion	E-12
       Figure E-13. IH69 Segment (SIU) 3: impaired streams criterion	E-13
       Figure E-14. IH69 Segment (SIU) 3: wetlands criterion	E-14
       Figure E-15. IH69 Segment (SIU) 3: floodplain criterion	E-15
       Figure E-16. IH69 Segment (SIU) 3: ozone nonattainment criterion	E-16
       Figure E-17. IH69 Segment (SIU) 3: TEAP diversity criterion	E-17
       Figure E-18. IH69 Segment (SIU) 3: TEAP rarity criterion  	E-18
       Figure E-19. IH69 Segment (SIU) 3: TEAP sustainability criterion	E-19
       Figure E-20. IH69 Segment (SIU) 3: TEAP composite criterion	E-20
       Figure E-21. IH69 Segment (SIU) 3: final cumulative results	E-21
       Figure E-22. IH69 Segment (SIU) 3: wildlife habitat criterion	E-22
       Figure E-23. IH69 Segment (SIU) 3: Federally-listed species criterion	E-23
       Figure E-24. IH69 Segment (SIU) 3: State listed species criterion	E-24
       Figure E-25. IH69 Segment (SIU) 3: ecologically significant streams criterion	E-25
Citation:
Osowski, S. L., G. D., Carney, J. D. Swick, J. A. Danielson, D. A. Fairish, and D. Lueckenhoff,.
2005. US EPA Region 6 GIS Screening Tool (GISST) User's Manual. Version 1.1. US
Environmental Protection Agency Region 6. Dallas, TX.

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                   CHAPTER 1: HOW TO USE GISST





       This manual will give the user 1) background on some of the principles behind the development




of GISST, 2) the main components of GISST so that those interested can create their own systems, 3)




specific case studies of the application of GISST, and 4) references used in the development of GISST.




                                        What is it?





       GISST is a system that uses GIS coverages and imposes a scoring structure on this data so that




decisions can be made.  The tool is an environmental assessment identification and prioritization tool




developed to provide a more systematic approach to considering single media and cumulative impacts




in making environmentally sound decisions.  It is designed to better understand the potential importance




of single and cumulative effects and to facilitate communication of technical and regulatory data with




industry, the public, and other stakeholders.  The scoring structure consists of criteria, using 1 as low




concern or vulnerability and 5 as high concern or vulnerability, based on available data sets and expert




input. The scoring structure is further discussed in Chapter 3. These individual criterion scores can be




compared among the base units one is interested in (e.g., watersheds, facilities, NEPA alternatives).










                       How is tjmi different from other GIS tools?





       GISST is different from other GIS tools in several ways, the most important of which is the




scoring structure.  Most GIS tools are used as mapping tools in which the user gets a map and then




must decide what constitutes 'greater' or 'lesser'  environmental concerns or vulnerability.  Stakeholders




and agency representatives know up front, what constitutes 'greater' or 'lesser' environmental concern

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(both vulnerable areas and impacts), relatively.  Therefore, GISST becomes an effective communication




tool and can aid to streamline projects or program needs.




       Most GIS tools are identification tools-showing where certain features are on the landscape




like SEF or CrEAM.  GISST is a prioritization tool-that is, given several options, which one has the




least potential impact or is more vulnerable.










                                    Who can use gKSt?




       Nearly anyone can use the GISST concept and apply it to their own program or project.




Criteria can be developed at any time and are based on need and available data.  If there is no criterion




for a certain subject area, it can be created, then peer-reviewed.  The contacts below can help you get




started with this process.










                                     How does it uwrk?





       The GISST equation has three parts, but can be modified, depending on project needs and




data availability:




               environmental vulnerability




               environmental impact




               geographic unit: point, line, or polygon (of the watershed,  grid,project, etc.)




Chapter 3 describes the original algorithm in more detail and Appendix E  describes the GIS




programming necessary to implement GISST. The GISST algorithm has been modified for other

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projects (see Chapter 4). GISST is flexible in that portions of the equation can be used or not, as




appropriate.  For example, a user may only want to determine the relative environmental vulnerability of




two project sites or watershed subunits, or a user may want to know the potential impacts to those




areas in addition to the environmental vulnerability. The user selects the appropriate criteria to use from




Appendix C. Vulnerability criteria are averaged for a score (Dv) and the same occurs with the impact




criteria (D:). The final GISST score is a multiplication of Dy, Dt, and geographic unit.  However, there




are cases where a simple summation of the criteria scores provides a more appropriate assessment




(e.g., cumulative impacts).  The EPA Region 6 developers stress that the individual criterion scores are




the most important in communicating  environmental concerns, rather than final GISST scores.










                                   What are the benefits?





       There are several benefits that users have noted since GISST became available.




              Improved quality of review




              Comments can be compiled earlier, proactively, and are issue specific.  Traditional




              NEPA comment letters can be generic in that they refer to regulations and not to




              information contained in the NEPA document.  Scoping letters, in particular, are




              generic and do not refer to project specific information.




       •      Early actions driven by technological capabilities




              EPA has been criticized for accepting information and analysis from applicants and




              contractors without verifying the information appropriately.




              Wholesale approach

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GISST allows us to serve more customers by getting more focused information to more




people efficiently.




Consistency




GISST can develop into a region-wide capacity for high quality reviews and document




preparation.




Institutional knowledge base




As staff retire or move to different jobs, knowledge of programs and regulations is lost.




GISST criteria and scoring system capture this knowledge and enhance it through




technology




Screening level




GISST is not time or labor intensive, but designed to point out 'red flags' to identify and




prioritize where additional resources might be used or additional information and




analysis is needed.




Transparency




GISST was developed in-house so users know how it works. One can compare this




to purchased software packages that are 'black boxes' where a user enters




information, but has no idea how the 'answer' is calculated.  GISST users have more




information on how each criterion is calculated and how it fits in with other criteria.
 New criteria can be added/changed as needed.

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              GISST can be applied to local projects encompassing one facility or to regional




              projects such as interstate highways.










                                 What are the drawbacks?





       GISST may cause an information overload. For example, if a user had five NEPA alternatives




and used 40 GISST criteria, the resulting matrix can be quite large. This information is accessible




approximately two hours after the GIS program is initiated. The EPA Region 6 developers stress




looking for 'red flags'-criterion scores of '4' or '5' that might indicate an environmental problem or an




accumulation of potential problems. Using GISST may increase workload because it is a wholesale




approach.  It takes approximately two hours to get a wealth of information that previously was not




available or only available after weeks of data collection. Other drawbacks with GISST concern its




reliance on available data, equally weighting data with different levels of QA,  and mixing of data bases




with different coverage accuracy and precision (county-level vs census block information). The GISST




is a screening-level tool only. It does not replace traditional risk assessment or field investigations.  It




can only point the user in the direction of where problems are likely to happen or where resources




should be directed for additional studies.










                       Who do I contact UI have further questions?





       In general, you can contact any of the people listed below. However, each criterion in




Appendices C and D lists specific EPA contacts and their email addresses.




       Rhonda Smith                General               smith.rhonda@epa.gov

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Gerald Carney              Toxicology           carney.gerald@epa.gov




Sharon Osowski             Ecology             osowski.sharon@epa.gov




David Fairish               GIS Coordinator     parrish.david@epa.gov

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       CHAPTER 2: BACKGROUND PRINCIPLES AND




                                   CONCEPTS
Introduction




       The purpose of this handbook is to present EPA Region 6's GIS screening methodology and to




serve as a manual for interested parties to replicate the tool for their own use. The tool is an




environmental assessment tool developed to provide a more systematic approach to considering




cumulative impacts in making environmentally sound decisions. It is designed to better understand the




potential significance of single and cumulative effects and to facilitate communication of technical and




regulatory data with industry, the public, and other stakeholders.  The tool is not a training manual for




impact assessment and users should be familiar with environmental impact assessment (EIA) in order to




appropriately consider the vulnerabilities of and potential impacts on the affected environment. EPA




and others (Costanza and Ruth 1998) are moving toward watershed or geographic approaches to




assessment (TNRCC 1996, Caruso and Ward 1998). Cumulative impact assessments (Canter and




Kamath 1995, Rees 1995, Smit and Spaling 1995, Cox and Piegorsch 1996, Piegorsch and Cox




1996, McCold and Saulsbury 1996, Burris and Canter 1997), use  of GIS technology (Peccol et al




1996, Wang and Yin 1997, Dale et al. 1998, Zhang et al. 1998), watershed-based approaches (Wang




and Yin 1997, Caruso and Ward 1998) and similar decision-making tools (Howard and Bunce 1996,




Parti dario 1996, Laskowski and Kutz 1998) have recently been the subject of journal articles and




included in the agendas at environmental policy and scientific meetings.

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       The original impetus for the development of the tool began as a way for Region 6 NEPA staff




to more objectively evaluate the information submitted by applicants and the potential cumulative




impacts of swine feedlots (CAFOs) in Oklahoma and present this information to the decision-maker,




EPA Regional Administrator, to determine where CAFO concentrations might have constituted a




potential significant adverse impact (See Chapter 5) (Osowski, et al. 2001); however, the tool has been




expanded and applied to a variety of projects since that time.










NEPA




       The National Environmental Policy Act of 1969 (as amended) [42 USC §4321, 4331-4335,




4341-4347, 43724375] (NEPA) is one of the oldest and most comprehensive of our environmental




laws.  The language of the Act itself as well as the accompanying regulations (40 CFR §1500-1508)




stress the importance of NEPA as good planning and as a process for decision-making. Within this




process, analysts prepare Environmental Assessments (EA) or Environmental Impact Statements (EIS)




that document the purpose and need of the project, existing environmental and socioeconomic




conditions, environmental consequences, and alternatives. The discussion of alternatives to the




proposed action is the heart of the NEPA process.  The emphasis of NEPA since 1970 has been on




direct, point sources of environmental impact and away from larger holistic assessments (O'Neill et al




1999).  In developing alternatives, as well as investigating current conditions and environmental




consequences, the NEPA document can be quite lengthy, technical, and may not be written in "plain




language" understandable by the public-at-large.




       McCold and Saulsbury (1996) found few court cases in which an inadequate assessment of

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cumulative impacts resulted in additional analysis being performed or an agency decision overturned.




Overall, NEPA has not been effective in addressing or mitigating cumulative impacts that have




continued to build up and subsequently become significant (McCold and Saulsbury 1996). With the




advent of more powerful computers and GIS, cumulative impacts assessment is becoming easier and




the analysis more objective than in times past.




       Other literature has outlined shortcomings of the NEPA process, such as difficult-to-understand




language (Sullivan et al. 1996), lack of post-assessment monitoring (Canter and Clark 1997), and




uncertainty as to the requirements for assessing cumulative impacts (Burris and Canter 1997). The




GISST helps to focus the Agency's assessment of potential impacts under NEPA and a way to monitor




the effectiveness of project controls and mitigation holistically.  As a screening tool, GISST helps to




focus industry or permitee, Agencies, groups, and the public on a comparison among facilities, NEPA




alternatives, or locations of vulnerable areas. Screening tools help establish better communication




among stakeholders  (Costanza and Ruth 1998).










Cumulative Impacts Assessment




       The word "cumulative" has been defined in several different ways, depending on context.




Words that are similar, even overlapping with cumulative, include "aggregate", "indirect", and




"secondary" impacts. For example, within risk assessment, "aggregate" refers to the amount of one




biologically-available chemical from multiple exposure paths (Moschandreas and Karuchit 2002),




whereas "cumulative" refers to the accumulation of a toxin (or toxic effect) from multiple exposure




routes and multiple contaminants (with a common toxicity) (Moschandreas and Karuchit 2002, Smits

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and Spaling 1995). Traditional risk assessment treats multiple exposures as independent events (US




EPA 1999).




       Within NEPA, "cumulative" refers to past and present actions. These actions could identify a




significant cumulative impact on the environment; however, there is little agreement as to how past and




present actions should be considered in the assessment process, and commonly, past conditions are




included as a definition of the existing or baseline conditions within the assessment process (McCold




and Saulsbury 1996). According to McCold and Saulsbury (1996) using a point in time when the




environmental resource or condition was most abundant is a suitable baseline.  Incorporating past and




present conditions as part of the baseline, negates their contribution towards cumulative effects




(McCold and Saulsbury 1996).




       As NEPA practitioners have discovered, environmental assessments on single projects and the




decisions arising from them do not mean that cumulative effects are assessed or determined to be




insignificant. The traditional single media approach does not address complex environmental




relationships (Mysz et al 2000).  Single projects with minimal impacts may accumulate over time and




space and then may equal a significant impact (Theobald et al 1997) or as Kahn (1966) termed it, the




'tyranny of small decisions made singly.' Cumulative impacts are not often fully addressed due to the




complexity of these potential impacts, the lack of available data on their consequences, and the desire




to limit the scope of environmental analysis. Unfortunately, potential cumulative impacts are rarely




considered in decision-making processes because the methods available (e.g.,statistical, models, etc)




are not practical in a regulatory arena (Abbruzzese and Leibowitz 1997). With the development and




use of GIS, investigators can identify large scale impacts (O'Neill et al 1999) and impacts that were






                                             10

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cumulative (Odum 1982).  Mitigation opportunities are also affected by an inadequate cumulative




impacts assessment (McCold and Saulsbury 1996).  Abbruzzese and Leibowitz (1997) developed a




framework for comparing landscape units by allowing consideration of cumulative impacts, especially in




management decisions; the goal being a general evaluation of a region as a whole. They used four




indices in their evaluation: 1) a function index that measured the amount of a specific ecological




attribute, 2) the value of the ecological attribute or function related to social goals, 3) the functional loss




of the function or attribute (i.e., cumulative impacts on the function/attribute), and 4) the ability to




replace the specific ecological attribute and its function (i.e. replacement potential).










Watershed-Based Assessments




       The holistic nature of watershed level assessments incorporates cumulative impacts in that




multiple stressors (biological, socioeconomic, chemical, etc.) can be analyzed over a large spatial scale




(Serveiss 2002),  either one watershed or the aggregation of several.  With the advent and subsequent




increase in the use of spatial analysis tools  such as GIS, regionally-scaled projects, planning and




processes, such as those that use the ecoregion (Mysz et al 2000), watershed (Dickert and Turtle




1985, Espejel et  al 1999, Steiner et al 2000a, Steiner et al 2000b, Tinker et al  1998, Serveiss 2002),




or other geographic boundary as a base unit, have become more commonplace. Reasons for using the




watershed as the  base unit for landscape-level assessments include functionality, biophysical processes,




naturally-defined area vs politically-defined area, environmental impact assessment, holism,




socioeconomic, and comparability/compatibility with other programs or areas (Steiner et al 2000a,




Tinker et al 1998, Serveiss 2002). These tools have also inspired scientists concerned about landscape






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level patterns and change and their effect on terrestrial and aquatic communities (Jones et al 2001,




Steiner et al 2000a).  For example, Steiner et al (2000a, 2000b) stated that watersheds provide a




framework in which to evaluate hydrological processes on wildlife habitat, land suitability for human




development (residential, commercial, industrial).  Using a watershed approach with risk assessment




can lead to the increased use of monitoring data (Serveiss 2002). Watershed-level assessments are




more holistic than assessments performed locally or those based on political boundaries because of




their ability to relate potentially unrelated factors (Miller et al 1998) and for comparisons at other scales




(e.g. several watersheds can be aggregated) (Montgomery et al  1995).




       The watershed approach has also been used to analyze environmental problems that do not fit




well into traditional programs or assessment methods (e.g. nonpoint source water pollution, regional




studies) (Serveiss 2002, Boughton et al 1999) and those problems needing more holistic or




comprehensive analysis (including decision making).  Watershed-level assessments also lead to




intergovernmental coordination on regulatory and management initiatives (Steiner et al 2000a, Serveiss




2002).










Decision Structures




       Most tools use some sort of criteria or factors to evaluate the data layers used in the assessment




(Steiner et al 2000b,  Karydis 1996, Xiang 2001, Store and Kangas 2001). These ranks or scores




help to simplify the analysis (Serveiss 2002), normalize  disparate data sets onto one nominal scale




(Clevenger et al 2002, Wickham et al 1999), and provide an easily understandable format to




communicate the results to various audiences. These 'scores' are helpful in comparing NEPA






                                              12

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alternatives or other aspects of projects since the 'score' represents the relative value of one alternative




to another (Steiner et al 2000b, Wickham et al 1999, Abbruzzese and Leibowitz 1997). It also




identifies 'red flags' (Theobald et al 2000) or issues that are inadequately addressed or are issues of




concern within the environmental assessment process. These scoring systems may represent the




difference between an ideal state of the environment and reality (Tran et al. 2002).   However, this




simple type of data integration has been criticized (Suter 1993).




       When building an assessment tool, one of the things to consider is whether to weight individual




"criteria" (Clevenger et al 2002, Abbruzzese and Leibowitz 1997) or to consider them all of equal




weight. If weights are chosen, then the importance of the decision increases (Steiner et al 2000b).




       The method that the GISST uses in terms of scoring and ranking could be considered as a multi




criteria evaluation or MCE  (Store and Kangas 2001, Clevenger et al 2002, Smits and Spaling 1995).




MCE can include standardization of criterion scores, multiplication by weighting factor, and/or addition




of all criterion scores (Store and Kangas 2001).










GIS




       GIS is used in the development of assessment and screening tools not only because of its spatial




data visualization abilities (i.e., maps of different data layers, coverages, landscape level, etc.), but also




because of its modeling and analysis functions, including landscape metrics (e.g. FRAGSTATS), and




other calculations (e.g., population density, hydrological functions). Thus, GIS has become a vital




research and assessment tool (Ji and Leeberg 2002, Clevenger et al 2002, Dale et al 1994, Treweek




and Veitch 1996, Iverson et al. 2001, O'Neill et al 1999), although Smits and Spaling (1995) predicted






                                              13

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that GIS would not be broadly used for cumulative impacts assessment.




        Since complicated modeling and analysis tools are less likely to be used in regulatory




processes, Leibowitz et al (2000) suggest six properties of GIS assessment tools. These properties




include 1) simplicity (not needing expert modeling abilities), 2) use of available data (rather than




experimentation), 3) analytical (not needing numerical simulation), 4) approximate (need matches level




of effort), 5) measurable change, and 6) expandable (use in more sophisticated models).










Relationship to SAB Report




        In 2002, the EPA Science Advisory Board Ecological Processes and Effects Committee




released a framework for assessing and reporting on ecological condition.  The purpose of which was




to guide practitioners on designing systems to assess and report ecological conditions.  The framework




also helps investigators to organize and decide what features to measure for a picture of ecological




'health.' Program goals and objectives are used to determine what essential ecological attributes will




be used. There are six broad categories and  several subcategories under each: landscape condition,




biotic condition, chemical and physical characteristics, ecological processes, hydrology/geomorphology,




and natural disturbance regimes. The set of six attributes can be used to determine ecological




indicators, or characteristics of ecological systems, and specific measures and monitoring data used to




determine the indicator or endpoint. It is a hierarchical structure where measures can be aggregated




into indicators and indicators can be aggregated into attributes. The six attributes are independent of




program goals and objectives, but serve as a  stimulus for practitioners to decide what attributes and




subcategories are essential to their project.






                                               14

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       Like the GISST, not every attribute category or subcategory is appropriate in every situation; a




user must select those criteria from the GISST or attributes from the SAB framework that provide the




best measure and analysis of the project objective. Also, GISST is a much broader tool, in that it has




socioeconomic, industry-specific, and other categories in addition to the ecological criteria.  Table 1




shows the SAB ecological attribute categories, subcategories, suggested measure, and what GISST




criterion corresponds. The SAB also suggests that the framework aids in designing the assessment and




subsequent report in that it should "transparently record the decision tree and professional judgements




used to develop it."  Appendix A describes each GISST criteria, the ranking or decision tree, and




definitions and assumptions associated with it.  In addition, the cumulative nature of GISST follows the




SAB framework of aggregating measures and indicators; therefore, both single 'media' and aggregate




or cumulative effects (ecological, socioeconomic, etc.) can be considered.




       The SAB also suggests that reference conditions be defined so that ecological indicators can be




compared and later normalized for aggregation.  This concept is imbedded within GISST as the 1 to 5




ranking structure serves to normalize disparate criteria values.  Even though a 'reference condition' is




not defined in GISST, it is a comparative risk tool in thatNEPA alternatives, transportation alignments,




etc. are compared against each other in a  standard decision framework.




       GISST adheres to the SAB framework in that it, in part, assesses ecological conditions,




allowing users to analyze ecological condition, consequences, and suggest mitigation over watersheds




or ecoregions.  GISST also adheres to the framework by being 1) 'multimedia', useful  to the traditional




EPA programs (air, water, RCRA) as well as holistic programs such as NEPA; 2) interagency, a




repository for coordinating other agency's data; and 3) understandable to non-scientists by using an






                                             15

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intuitive 1 to 5 decision structure. In addition, GISST is 4) interdisciplinary, by incorporating

socioeconomic, toxicity, and regulatory criteria (these are not a part of the SAB framework for

assessing ecological condition).
Table 1. Relationship of the SAB framework ecological attributes to GISST criteria. (P) indicates the

GISST criterion is provisional.
                                LANDSCAPE CONDITION
 Category
Subcategory
SAB example
measure
GISST criterion
 Extent of habitat types
 Landscape condition
 Landscape pattern
                  perimeter-area ratio
                  number of habitat
                  types
                   contagion
                      habitat fragmentation,
                      patch area (P), TEAP
                      Diversity

                      landscape texture (P),
                      wildlife habitat
                      TEAP Diversity,  TEAP
                      Composite

                      aggregation index (P),
                      TEAP Diversity, TEAP
                      Composite
                                  BIOTIC CONDITION
 Ecosystems &
 communities
community extent    extent of successional    TEAP Diversity
                   state                  (Kuchler)
                          community
                          composition
                  presence of focal
                  species
                      Protected habitat (P),
                      TEAP Rarity
                                             16

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Category
Subcategory
SAB example
measure
GISST criterion
Species & populations
Organism condition
                         trophic structure     feeding guilds
                                            predation rate


                         physical structure    tree canopy height
community
dynamics
population size

genetic diversity


population
structure

population
dynamics

habitat suitability
physiological
status

symptoms of
disease

signs of disease
density

degree of
heterozygosity

age structure
dispersal rates


focal species
requirements

hormone levels


tumors, lesions


tissue burden of
contaminants
                      TEAP Rarity (taxonomic
                      richness)

                      NONE
TEAP Sustainability
(Kuchler)

NONE

NONE


NONE


NONE


Combination of GIS
layers

NONE


NONE
                                                                  TRI weighted Air/Water
                                                                  releases
                  CHEMICAL AND PHYSICAL CHARACTERISTICS
Nutrient concentrations     Nitrogen
                   cone of N
                      Water Quality (STORE!
                      data)
Trace inorganic & organic    metals
chemicals
Phosphorus         cone of total P          Phosphorus budget

other nutrients       cone of Ca, K, Si       NONE

                   Cu, Zn in sediment      NONE
                                           17

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Category



Chemical properties




Physical parameters


Subcategory

trace elements
organic
compounds
PH
dissolved Oxygen
salinity
organic matter
other
soil/sediment
air/water

SAB example
measure
Se in water and soil
methyl-Hg
pH in water & soil
DO in streams
conductivity
soil organic matter
buffering capacity
temperature, texture
concentration of
parti culates
GISST criterion

NONE
NONE
NONE
NONE
NONE
NONE
NONE
soil permeability,
aquifer/geology ranking
ozone nonattainment

ECOLOGICAL PROCESSES
Energy flow


Material flow


primary
production
net ecosystem
production
growth efficiency
organic C cycling
N & P cycling
other nutrient
cycling
tree growth
CO2flux
carbon transfer
organic matter quality
N-fixation capacity
input/output budgets
NONE
NONE
NONE
NONE
NONE
NONE
18

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                         HYDROLOGY & GEOMORPHOLOGY
Category
Subcategory
SAB example
measure
GISST criterion
Surface & groundwater
flows
Dynamic structural
characteristics
pattern of surface
flow

hydrodynamics

pattern of
groundwater flows

spatial salinity
patterns

water storage

channel morphology
complexity
water level fluctuations   NONE
water movement

depth to groundwater

surface salinity
gradients

aquifer capacity

length of natural
shoreline
NONE

Groundwater probability

NONE


Aquifer/geology ranking

Water quantity


Sediment & material
transport

dist. of connected
floodplain
aquatic physical
habitat
sediment
movement
particle size
distribution
2yr or lOyr floods
pool-riffle ratio
sediment deposition
distribution of grain
size
100/500 Year Floodplain
NONE
NONE
NONE
NATURAL DISTURBANCE REGIMES




frequency
intensity
extent
duration
recurrence interval

spatial extent
length of event
NONE
NONE
NONE
NONE
                                            19

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            CHAPTER 3: CRITERIA DEVELOPMENT
Overall Structure




       GISST initially considered environmental vulnerabilities and potential impacts by using USGS




watershed subunits called Hydrologic Unit Codes (HUC) (Cederstrand and Rea 1995). This




watershed subunit is created by merging watershed area data and state stream segment information to




form the base analytical unit.  Depending on the state and locality, anywhere from an 8-digit to 14-digit




HUC can be used. Higher level HUCs represent a finer grain than lower numbered HUCs




(Cederstrand and Rea 1995). The mathematical algorithm has been used in several other EPA Region




6 applications and was used in GISST for consistency and ease of use (Osowski et al. 2001). The 1 to




5 scale, which is also consistent with other Regional programs, keeps the ranking system simple, with as




small a number as possible to capture a  sense of 'greater' or 'lesser' environmental concern or




vulnerability (Osowski et al. 2001). Some criteria work as "on/off' buttons because only compliance is




important and there no 'degrees' of legal compliance. However, certain projects may need to use a




different geographical area (e.g., ecoregion, political boundary, transportation corridor). In these




situations, the appropriate geographical  area may not be the watershed or subwatershed level;




however, the single media and cumulative nature of GISST still  apply.




       Criteria are evaluated using a mathematical formula, although different projects have used sums




or averages of criteria. The individual criterion scores are extremely valuable in communicating EPA




concerns. Key components are the total area of known projects in the watershed or appropriate
                                           20

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geographical unit (A:), area of the watershed subunit, larger project area, or other geographical unit




(Aws), degree of environmental vulnerability (Dv), and the degree of industry-specific impacts (D:)




specific to each watershed subunit,larger project area, or other appropriate geographical unit. The




unitless GISST algorithm is as follows: GISST = (S (A:/Aws))x Dy x Dj where




               GISST = potential for significant environmental risk




               Aj = total area of known projects




               Aws = area of watershed subunit, project area, or other geographical unit




               Dy = degree of vulnerability for the watershed subunit, project area, or other




                      appropriate geographical unit (average of all Dy criteria*)




               Dj = degree of impact produced by the project (average of all Dj criteria*)




               individual criteria may also be summed for a grand total, rather than averaged.




        The individual criterion selected, including the area criterion, are dependent on the needs of and




appropriateness to specific projects.  For example, transportation projects use the general corridor




where the road is to be placed rather than a watershed subunit. Other projects may use the county or




other polygon instead of the watershed subunit and Aws.




        The development of criteria force decision-makers to determine the comparative risk of five




options. In principle this is a very difficult process, and scores/criteria may cause disagreements or




controversy. Ultimately, it is a way to systematically assess vulnerabilities and impacts cumulatively.




GISST also makes stakeholders aware of what resources will be evaluated and the associated risk




(score) that environmental assessors are willing to acknowledge.  Screening models such as the GISST




can lead to decisions to prioritize certain aspects of facility or project operations for environmental






                                               21

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review.
Area Criterion




       (Z(AI/AWS)) is the ratio of the cumulative area effected to the total area of evaluated watershed




subunit or appropriate geographical unit, usually expressed as a percentage.










Vulnerability Criteria




       The degree of vulnerability, Dy, is the sum of individual criterion scores divided by the number




of vulnerability factors used (Osowski et al. 2001). The vulnerability criteria are intentionally




unweighted reflecting a decision by GISST development team that the number of criteria used reflects




the nature and purpose of the project for which it is used. In effect, the number of criteria for a certain




environmental resource weights that feature more than an environmental resource with only one




criterion. For example, one might use four water-related criteria, but only one economic criterion;




therefore, the analysis would emphasize water issues. Since GISST is a screening level tool, it uses




data already collected and in an electronic format. The  data were often collected for another purpose;




scientific study, permit application, or enforcement/compliance, for example.  The application of a




criterion is dependent on the availability of data for a particular geographic area. Consequently, a




particular criterion may not be used until a viable data set becomes available.  Many of the criteria




reflect the questionnaire categories in Canter and Kamath (1997), although research was not available




at the time the GISST was developed.
                                              22

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Impact Criteria




       D! is the sum of individual impact criterion scores divided by the number of impact factors used.




They reflect industry specific impacts and not all may be used for a specific project. Many are also




dependent on data and information from the individual facility or entity being evaluated (Osowski et al.




2001). Therefore, stakeholders must have a clear understanding of GISST and a willingness to




participate by providing data concerning their operations.










Criteria Groups




       Criteria, whether impact or vulnerability, can be placed into broad groups: water quality,




ecological, air quality, socioeconomic, toxicity, CAFOs, pollution prevention, and




enforcement/compliance.  Enforcement/compliance criteria are seldom used in the every day uses of




GISST within NEPA and are therefore not included in this user's manual. The individual criterion




descriptions can be found in one of two appendices: Appendix A for finalized criteria and Appendix B




for provisional criteria. There are many reasons why criteria are termed 'provisional.'  Provisional




criteria are those that have not been used, do not have a database to support their use,  or are in the




process of being developed, peer reviewed, and finalized.  In some cases, provisional criteria are




developed anticipating a future need, but no appropriate data are available. Please note that the




underlying data and GIS coverages are dynamic and therefore the criteria may  change as data sources




become available.
                                              23

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Water Quality




        The use of water quality criteria will give the user an overall sense of surface and ground water




quantity and quality. Several different data sources were used (see Appendix A). Depending on the




project, the user may not use all of the water quality criteria available or there may be a gap in which




the user should develop a new criterion to meet his/her needs.










Ecological




        This section includes criteria on wildlife habitat, endangered species, habitat fragmentation, and




land use.  In general, ecological criteria provide the user with what conditions are like for non-human




organisms in the project area or geographical unit. They describe the landscape (large scale) and the




project area (small scale). Several criteria in this section can be "flipped."  For example, we have




chosen large tracts of wildlife habitat as the most vulnerable condition.  Conventional wisdom suggests




that large unbroken tracts of habitat are better able to support large species (e.g., black bear),




migratory species (e.g., bald eagle), and maintain the functioning of communities and ecosystems.




Certain large migratory species may serve as "umbrella species" for smaller, less mobile species (e.g.,




amphibians, insects). This is appropriate given ecological theory; however, our regulations typically




support the opposite. That is, that the most vulnerable condition could be the very small remnant




patches of a particular habitat type.  Without proper connectivity, however, small remnants of habitat




will probably not support certain species.
                                               24

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Air Quality




       The air quality section is one in which there are only a few criteria. Several criteria are under




development (Appendix B) to give the user more choices as compared to other sections.










Socioeconomic




       Socioeconomic criteria are important for a number of reasons, including the requirements to




assess environmental justice, NEPA requirements, and to prepare an effective public involvement




strategy.  Many of the criteria are useful for this last purpose, especially if English is not the primary




language or the literacy level of the community is not high.  Socioeconomic criteria are important in that




an individual's place of residence, diet, exposure to occupational hazards, ability to receive adequate




health care (both preventive and post injury) may be controlled by income and education. For




environmental justice (Title VI complaints),  only 3 criteria need be used (economic, minority, and total




population).  For NEPA assessments,  several others can be used to determine whether the proposed




project will have a beneficial or adverse effect on the local population. Other criteria help EPA staff




prepare an effective public education and involvement campaign.










Toxicity




       Depending on the type of project the user is trying to assess with GISST, these criteria may be




very important. These criteria help to determine what pollution sources are in proximity to the




proposed project and the amount of releases (air and water) from facilities. In assessing cumulative or




aggregate health effects, these criteria become extremely important in the decision whether further field






                                              25

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investigations are needed.










CAFOs




       The CAFO criteria section contains criteria focused on a specific industry. Therefore, these are




all impact criteria designed to assess the environmental and pollution controls likely to be found at




Concentrated Animal Feeding Operations (CAFO). In our usage of GISST, we have not developed




criteria for other specific industrial sectors; however, other users may decide to develop their own




impact criteria based on program needs and priorities.










Pollution Prevention




       Several pollution prevention criteria have been developed, but are only included as provisional




(Appendix B) because inadequate data sources exist. Once data sources become available, these




criteria relating to energy usage, lighting, and auditing will be finalized and applied in appropriate




projects (e.g., Federal facilities program).
                                              26

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                   CHAPTER 4: APPLICATIONS
Introduction




      Three case studies are given as examples of the application of GISST. In addition, a peer




review log/history of GISST activities and a map of the locations where GISST has been used appear in




Appendix D.
                                      27

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Swine Concentrated Animal Feeding Operation (CAFO) New Source Determination




Location: Kingfisher County, OK




Scale: Watershed




       Region 6 does NEPA review for New Source Determinations for NPDES permits in states




where these Federal programs have not been authorized/delegated. Oklahoma does not have the




NPDES permit authority for CAFOs. At the time in 1997, many states had been embroiled in




controversies related to large CAFOs. Supporters of CAFOS argued that their facilities were simply




another agricultural activity, protected in many states by right-to-farm laws, that supported local




economies. Opponents of CAFOs argued that the facilities were under-regulated industrial operations




that resulted in environmental  and public health risks. As such, the public was often divided and EPA




was looked upon as an objective third-party to fairly evaluate these controversial issues.




       This case study shows how GISST assists in the complete NEPA process; from identification




of baseline conditions and potential impacts; avoidance and mitigation of impacts; monitoring of




mitigation commitments; and enforcement of Clean Water Act NPDES violations.




       The environmental issue was that very large (4 million animals/year) swine CAFOs becoming




established in a 1-2 county (watershed) area in Oklahoma. What was the ecological/environmental




protection relationship?  Possible leaching from lagoon and/or land application area causing nitrate




contamination of groundwater which also serves as drinking water for some residents; odor from facility




(lagoons and land application of swine waste); health concerns due to dead animal disposal.




       Using GIS coverages and information in the applicant Environmental Information Document




(EID), GISST showed that several criteria scored high (5, on a 1-5 scale); the amount of nitrate-nitrite






                                            28

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exceedances (STORET), probability of the water table within 6 feet of the surface and exacerbated by




the number of CAFOs in proximity (see Chapter 3) to each other. For example, Figure 1 shows the




surface water quantity criterion for Oklahoma.  The facilities did well on the use of control technologies




(lagoon liner, innovative sprayer technology). Figures 2 to 4 show Dy, Dj, and final CRIA/GISST




scores (CRIA was the acronym of the pilot project), respectively, for five subwatersheds in Oklahoma




where CAFO facilities are located. Dy (FigureS) and Dj (Figure 6) can be calculated for each facility




as well, although this does not show the cumulative effects of all projects on the subwatershed (Figures




2 to 3).




       The Regional Administrator determined that the CAFO would not have their NPDES permit




approved and a FNSI for the EA until a monitoring protocol and schedule could be agreed upon, given




that the GISST had identified groundwater contamination as a potential significant  impact.




       What stakeholders were involved?  EPA, local citizens, CAFOs, agricultural consultants,




ACCORD environmental group, Pork Producers What were the follow up steps  or lessons learned?




Monitoring (well) reports were submitted by the facility quarterly. At least one of these reports showed




nitrate exceedances and possible groundwater contamination.  This information was given to inspectors




and enforcement officers and resulted in enforcement action..
                                             29

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Figure
            30

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Figure 2
           31

-------
Figure 3
           32

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Figure 4
           33

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Figure 5
           34

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Figure 6
           35

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                            ffl-69 NAFTA International Corridor




Introduction




       Interstate 69, or the NAFTA highway, is a congressionally approved transportation project to




facilitate trade among the United State, Canada, and Mexico (Figure 7). The Congressionally-




determined corridor stretched 1600 miles from Windsor, Canada to the US-Mexico border near




Brownsville, Texas. Approximately, 1000 of these miles occur in Texas (Figure 8).  The IH69 corridor




is broken into segments of independent utility (SIU) for further study.  Construction in each SIU can




proceed independently of the others. When IH69 was first approved,  several states already had




Environmental Impact Statements (EIS) in progress for previous transportation needs and were




subsequently incorporated as portions of IH69. Texas has not initiated scoping of the project in 2001.




After IH69 scoping and study began, Texas introduced its Trans  Texas Corridor (TTC) Project. TTC




is a long range (50 years) concept aimed at planning for future transportation needs in Texas.  It




incorporates separate lanes for cars, trucks, high speed rail, freight rail, and utilities, all of which would




be co-located within the same general corridor. IH69 was incorporated into these TTC activities, but




each will have it's own EIS process.




       Due to the broad scope and complexity of the project, the Federal Highway Administration




(FHWA) invited other Federal and State agencies to provide input early in the process. This and




Executive Order 13274, Environmental Stewardship and Transportation Infrastructure Project




Reviews, also referred to as the Executive Order on Environmental Streamlining, brought the agencies




together to discuss relevant issues.  It became apparent that the other  agencies would not have the




resources to address multiple requests for information from






                                             36

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Figure 7
           37

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Figure 8
           38

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each of 14+ SIU contractors, nor would they be able to provide this information in order for FHWA to




meet its deadlines. Each agency provided executive and technical staff who developed a process




manual in which there would be various concurrence points throughout the IH69 process. FHWA also




determined that a tiered NEPA approach, where Tier I assessed broad, corridor-wide alternatives and




potential impacts, and Tier n would be NEPA documents specific to each SIU.  Tier I does not




authorize construction, but provides a method for the transportation agencies to identify corridors for




future preservation. For example, the SIU that encompasses Harris County and Houston, Texas is




fairly broad, but the likelihood of obtaining a one to four mile transportation corridor when it is needed




in 20-50 years, would be very slim without a Tier IEIS. Within this streamlining pilot, EPA suggested




the use of GIS data as a way to make the assessment process more manageable,  and specifically the




GIS Screening Tool (GISST) as a way of helping to assess single and cumulative potential impacts for




the length of the corridor in Texas. Also, a coordinated effort to determine data needs and provide




these data to FHWA would lessen the burden of each agency to respond to individual contractor




requests and the data would be consistent  for the length of the corridor.




       The participating agency technical contacts met to discuss what data needs existed and whether




they could determine a provider.  Table 2 shows this "brainstorming" list of data needs. Tis list was




truncated based on several factors, including the access  or availability of data in electronic format,




consistency  across the entire corridor, resolution, and applicability to the.  EPA presented the GISST




to the groupand highlighted its usefulness in bringing many different data sets together using a  scoring




structure in order to visualize where potential impacts might exist and narrow the Congressionally-




mandated corridor into a size more manageable for further field






                                              39

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Table 2.  Types of Data and GIS coverages considered and their sources. Data in this table were not




necessarily incorporated in GISST, but were considered initially. Ace refers to data access, and Cov




refers to the extent of the coverage for the IH69 project.
Environmental
Feature
Air Quality
Resources

Agricultural
Resources
Aquatic
Resources
Hydrologic Data
Hydrologic Data







Source Database
TCEQ & Nonattainment
EPAR6

USGS National Land
Cover Data
(NLCD)

U.S. National
EPA/US Hydrography
GS Dataset (NHD)
TWDB Reservoirs to
be included in
the 1996
Water Plan





Description Scale &
Accuracy
Ozone County
Nonattainment Level
&Near 1:100,000
Nonattainment
Areas
Agricultural 30 meter
Land resolution
Classification

U.S. 1:100,000
Hydrographic
Dataset
Generally
reservoirs w/
authorized
capacity of
5000+
acre-feet and
authorized
diversion of
water for
consumptive
municipal or
industrial use.
Date Ace Cov
2002 A E

1992 A E

2000 A E
1997 A E







                                              40

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Environmental
Feature








Water Quality







Wetlands

Soils

Source
GLO


Bureau
of
Transpor
tation
Statistics
U.S.
Bureau
of the
Census
TCEQ
EPA






USGS

NRCS

Database
Coastal
Management
Zone
Boundary


National
Waterway
Network

TIGER


Designated
Stream
Segments of
Concern
TMDL






National Land
Cover Data
(NLCD)
State Soil
Geographic
Database
(STATSGO)
Description Scale &
Accuracy
Inland extent
of areas
subject to
regulation
under the TX
Coastal
Management
Program.
Shipping 1:100,000
waterways in
and around
the U.S.

Hydrologic 1:100,000
Data


Impaired 1:63,360 -
waters from 1:250,000
1999303(d)
list.
River 1:100,000
segments,
lakes,
estuaries
designated
under CWA
303d as not
meeting their
designated
use
Wetlands 30 meter
Land resolution
Classification
State Soils 1:250,000
Layer

Date Ace Cov
A C


2001 A E

2000 A E


1999 A E
1998 A E






1992 A E

1994 A E

41

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Environmental
Feature
Terrestrial
Resources
Soils

Vegetation
Vegetation
Managed Lands

Land Use/Land
Cover
Threatened &
Endangered
Species,
Sensitive
Habitats






Source


NRCS

Texas
Tech
Univ.
TPWD
Varied

USGS
TPWD

USFWS

USFWS

EPA

Database


Soil Survey
Geographic
Database
(SSURGO)
GAP
Vegetative
Types of TX
Managed
Lands

National Land
Cover Data
(NLCD)
Biological &
Conservation
Data (BCD)

Potential T&E
Habitat in SE
Texas
Potential T&E
Habitat in
South Texas
Potential
Habitat Index

Description


County Soils
Layer

Vegetation
and Species
Habitat
TX
Vegetation/Ha
bitat
Parks, Forest,
Wildlife
Refuges
Wildlife
Habitat
Quad/County
Level Species
Lists

Potential
Habitat in SE
Texas
Potential
Habitat in
South Texas
Model of
Highly
Sensitive
Habitat
Scale & Date
Accuracy


1:24,000 Varied

30 meter 1998
1:250,000 1982
Varied Varied

30 meter 1992
7.5' 1994
Quadrangle
& County

County Level 2001

County Level 2002

30 Meter 1992,
2002

Ace Cov


A M

A E
A E
A E

A E
A E

L X

L X

A E

42

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Environmental
Feature












Hazardous
Waste &
Brownfields



















Source
GLO





GLO

TPWD



U.S.
EPA

U.S.
EPA

U.S.
EPA


TCEQ



TCEQ

TCEQ


TXDOT


Database
Priority
Protection
Habitat Areas
(Upper &
Lower Coast)

Bird Rookeries

Ecological
Stream
Segments of
Concern
Envirofacts


Toxic Release
Inventory

Superfund
Sites


Hazardous
Waste Sites


Radioactive
Waste Sites
Landfills


TXDOT
Maintenance
Facilities
Description
Areas along
coast of
Sensitive
Coastal
Habitats or
Species
Bird Rookeries
along coast
Ecological
Significant
River/Stream
Segments
EPA Permitted
Facilities

Toxic Release
Sites

Federal &
State
Superfund
Sites
Federal &
State
Hazardous
Waste Sites
Radioactive
Waste Sites
Municipal
Solid Waste
Landfills
TXDOT
Maintenance
Facilities
Scale & Date
Accuracy
1:24,000 1995,
1998




1:24,000

1:100,000 1995



Point Data - Varied
Varied
Accuracy
Point Data - 2000
Varied
Accuracy
Point Data - 2002
Varied
Accuracy

Point Data - 2002
Varied
Accuracy

1:24,000 2000

Point Data - 1996
Varied
Accuracy
1:2,000,000?? 2000


Ace Cov
A C





A C

L E



A E


A E


A E



A E



A E

A E


A E


43

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Environmental
Feature
Historic,
Archeological
& Cultural
Resources
Managed Lands
(4(f) potential)



Archeological


Archeological
Cultural
Cultural


Cultural
Cultural
Cultural

Geology

Source


Varied



THC/TX
DOT


THC
TNRIS
&
TIGER
USGS


THC
THC
TXDOT

BEG

Database


Managed
Lands



Archeological
Site
Distribution in
the 1-69
Corridor
THC Atlas
Indian
Reservation
Boundaries
GNIS
(Geographic
Names
Information
System)
Historic
Markers
Historic
National
Register
Properties
Historic
Off-System
Bridges
Geologic Data

Description


National
Parks, Forest,
and Refuges;
State Parks
and Wildlife
Areas
Density Map
Derived From
Known
Distribution of
Sites
Archeological
Data
Indian
Reservation
Boundaries
Physical &
Geographical
feature names


Historic
Roadway
Signs
Historic
National
Register
Properties
Historic
Off-System
Bridges
BEG Geology
of South
Texas
Scale &
Accuracy


Varied



1:24,000


1:24,000
1:24,000
1:24,000


Point
Data-Varied
Accuracy
Point
Data-Varied
Accuracy
1:24,000

1:250,000

Date Ace Cov


Varied A E



Varied R E


Varied R E
2000?
1981 A E


2002 A E
2002 A E
2001 A E

A M

44

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Environmental
Feature
Topography



Groundwater/
Aquifers





Watersheds



Floodplains

Social/
Economic/EJ








Miscellaneous


Source
uses



TNRIS/
TWDB


U.S.
EPA
Reg 6
USGS



FEMA

U.S.
Bureau
of the
Census
U.S.
Bureau
of the
Census


USGS/T
OPP

Database
National
Elevation
Database
(NED)
Major/Minor
Aquifers


Sole Source
Aquifers

8-digit
Hydrologic
Units

Q3 Flood Data

PL94-171



SF3A





DOQQ


Description Scale &
Accuracy
Elevation Data 30 meter
resolution


Major & 1:250,000
Minor
Aquifers of
TX
TXSole 1:100,000
Source
Aquifers
8-digit 1:250,000
Hydrologic
Units of the
US
100yr/500yr 1:24,000
Flood Plains
Population & Block Level
Minority Data


Population, Block Group
Housing, Levei
Income Data
(2000 should
be available
summer 2002)
Digital 1 meter
Orthophoto
Quarter-Quad
Date Ace Cov
Varied A E



A E



1996


1995 A E



Varied A M

1990/2 A E
000


1990 A E





Varied A E


45

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Environmental Source
Feature
uses


U.S.
Bureau
of the
Census
TXDOT

Texas
Water
Develop
ment
Board
TXDOT




NASA

TIGER



U.S.
Bureau
of the
Census
U.S.
Bureau
of the
Census
EPA
Region 6


Database
Digital Raster
Graphic

TIGER



County
Boundaries
Colonias




TXDOT
District
Boundaries
Aerial Photos

Landsat

TIGER



TIGER



TIGER



Schools



Description
7.5'
Topographic
Maps
Urbanized
Areas


County
Boundaries
Locations of
Colonias



District
Boundaries

B&W Aerial
Photos
Satellite
Imagery
State &
Federal
Congressional
Districts
Pipelines/Utilit
ies


Railroads



Schools -
Address
Matched
using TEA
listing
Scale &
Accuracy
1:24,000


1:100,000



1:24,000

Point Data -
Varied
Accuracy


1:24,000




30 meter
resolution
1:100,000



1:100,000



1:100,000



100m



Date Ace Cov
Varied A E


2000 A E



2000 A E

1996 A E




1994 A E


2001 P

1996 A E

2000 A E



2000 A E



2000 A E



2002 A M



46

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A=widely available, C=Coastal area only, E=Coverage for the entire IH69 Corridor, L=Limited
access, M=Coverage for a majority of the IH69 Corridor, P=Paper or image only, R=Restricted
acess, X=Limited coverage for the IH69 corridor
investigation.  In addition, EPA stressed the flexibility of GISST and that further criteria could be

developed.  This initially led to the development of four new criteria based upon data needs, Table 2,

and a checklist of issues that FHWA must address in its EIS (FHWA Environmental Guidebook,

www.fhwa.dot.gov/environment/guidebook). These criteria can be found in Appendices A.

       In addition, other "spin-off projects ensued.  One of these, called the Texas Environmental

Resource Stewards (TERS) was an informal workgroup of State and federal resource agency

executives who committed to meet semi-annually to discuss current issues. The most pressing issues

were related to the IH69  project and the potential for environmental impacts and also for opportunities

for mitigation.  In order to explore areas for collaboration and streamlining the executives tasked the

technical staff from each  agency to develop a map/tool from electronic data indicating locations that

were "ecologically important." EPA Region 6 offered a tool that had been used successfully in Region

5 (i.e., CrEAM) for this purpose.  The TERS executives agreed that the CrEAM fit Texas' needs and

thus the application of CrEAM in Texas became known as the Texas Ecological Assessment Protocol

(TEAP). The results of TEAP would be used as part of FHWA's analysis and incorporated as new

criteria in the GISST.
                                             47

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Methods




       The method described in the GISST User's Manual needed to be modified for the IH69




project. Roughly, GISST multiples area, vulnerability, and impact scores to obtain a cumulative score




for the geographic area of interest.  The method needed to be modified for IH69, since it would not be




beneficial to get one "cumulative" number for the entire length of the corridor, nor would it be beneficial




to have one score per SIU.  What was needed were scores for defined geographical areas within each




SIU.  Therefore, the GISST methodology was modified to calculate scores for each 1km square within




the corridor. Eventually, other areas outside of the corridor were included for analysis. In addition, the




method was modified to exclude the area and impact portions of the equation and multipliers and to use




a straight summation of the appropriate "vulnerability" criteria for each 1 km square. As explained




above, the interdisciplinary technical team decided to use 20 criteria. The GISST user's manual lists




about 100 criteria, however, using all of these criteria can lead to an information overload and wasted




effort when certain criteria are not needed. The use of the grid also lent itself to a type of least-cost




path analysis of potential road alignments. By using the 1km squares with lower scores (either the




cumulative GISST score or the individual criteria), analysts  could decide where the path of least




environmental impact occurred.  They could use that information along with traditional transportation




engineering and safety factors to generate a potential reasonable corridor and then ultimately road




alignment alternatives.




       New criteria were added in order to address floodplains, wetlands, prime farmlands, etc.




Eventually, when TEAP was complete, this information was used to generate new criteria that replaced




other criteria, such as wildlife habitat.






                                               48

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Results and Discussion




       Figures 9 show the results of the GISST for the proposed IH69 corridor.  As the GISST




developers stress, the cumulative score is an initial guide to help assessors evaluate the underlying




individual criteria, and should not be used as a final decision. The cumulative and individual criteria




scores should aid FHWA determine recommended reasonable corridor alternatives for the EIS.




       In general, there are greater numbers of people (Figure 10) in the southern portion of IH69




(SIUs 14, 12, 7, 8, 9, 10) compared to the rest of the corridor with the exception of the Houston area




(SIU 4).  Environmental justice must also be considered in the NEPA process.  Similar to population, a




higher minority percentage occurs in the southern portion of the corridor (Figure 11). Economically-




stressed communities occur throughout the length of the corridor (Figure 12).




       Several land use types must also be addressed including prime farm lands (Figure 13), public or




managed lands (Figure  14), and locations of hazardous waste (Figure 15).  The potential impacts to




water are large issues to address in a project of this size, particularly the amount of streams (Figure  16),




the number that are already impaired to some degree (Figure 17), and the number of wetland areas




(Figure 18).  Additionally, for safety and engineering problems, as well as environmental issues,




floodplains are identified and locations prioritized (Figure 19).  Potential impacts to air, in the form of




distance to ozone nonattainment areas must be analyzed in the EIS (Figure 20).




       After the initial  GISST analysis was performed and FHWA continued their evaluation of data




for the Tier I EIS, the TERS TEAP report became a final document.  The interagency group conducted




an internal peer review and each agency concurred on the final report.  Since the TEAP represented




better ecological information, Some of the initial GISST criteria were dropped because they were






                                             49

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Figure 9
           50

-------
Figure 10
           51

-------
Figure 11
            52

-------
Figure 12
           53

-------
Figure 13
           54

-------
Figure 14
           55

-------
Figure 15
          56

-------
Figure 16
           57

-------
Figure 17
           58

-------
Figure 18
            59

-------
Figure 19
           60

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Figure 20
           61

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incorporated into the TEAP or the TEAP had better information (e.g., species location data from Texas




Parks and Wildlife Department). Therefore, the GISST was modified and performed again.  The




following criteria were replaced by TEAP results: wildlife habitat (Figure El), Federally-listed species




(Figure E2), state-listed species (Figure E3), and ecologically significant streams (Figure E4). Figures




21-24 show the results of the TEAP criteria.




       The cumulative result (Figure 25) shows that areas in southern Texas (SIUs 14, 13, 11) have




fewer potential cumulative impacts compared to areas in the northern portion of IH69 (SIUs 3, 4, 5).




       Once the GISST was performed, FHWA used it, along with other information, to delineate the




proposed reasonable corridor ("b" in Figures 9-25). EPA was asked to review the proposed




reasonable corridor and provide any comments.  EPA used an overlay of the GISST analysis for the




IH69 corridors, including additional areas outside of the Congressionally-determined corridor.  EPA




concluded that the proposed reasonable corridor had avoided or minimized impacts (where possible)




to nearly all of the areas identified as highly vulnerable by GISST (Figure 25, deep red color).  Further




review the underlying criteria showed that the proposed reasonable corridor had also avoided or




minimized potential impacts ("b" in Figures 9-25). Even though several criteria were replaced by




TEAP information and not included in the final cumulative GISST score, they are provided in Appendix




E for comparison  (Figures E1-E4). In addition, Appendix E shows an individual SIU in east Texas




(Figures E5-E25)  so that the reader can view a close up of the GISST information, Congressional




Corridor, and proposed reasonable corridor.
                                             62

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Figure 21
           63

-------
Figure 22
           64

-------
Figure 23
           65

-------
Figure 24
           66

-------
Figure 25
           67

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Conclusions




       The IH69 project is still a work-in-progress and has not reached its conclusion, but the GISST




has provided a tool for environmental assessors and reviewers to aid in visualizing and prioritizing




potential impacts so that alternatives can be developed that try to avoid or minimize impacts to these




resources. EPA anticipates that the need for geospatial tools such as GISST will become greater given




the more complicated time we live in.  GISST has been a successful tool for transportation projects and




EPA hopes to continue to use it and refine it.  EPA and FHWA entered into a technology transfer




agreement to develop a "GISST Toolbar" for ArcGIS 9 producst (ESRI).  For those that use GIS as




an analysis tool, the "GISST Toolbar" means that the GISST information for anywhere in Region 6 is




only a few mouse clicks away and a few hours computation time. EPA has used the IH69 as an




anecdotal validation process for GISST in that if GISST can identify/prioritize potential impacts and




FHWA can avoid or minimize them, then perhaps environmental damage to sensitive or important




resources will have been averted.  This is the ultimate goal of NEPA and the environmental assessment




process.
                                             68

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NEPA Document Preparation and Review




Location: Regionwide




Scale: Varies from small overpass replacement to multi-county pipeline corridor










Background: Typically, requests for information were answered with a form letter of sorts: a 10-12




page letter that reiterated CEQ regulations and traditional NEPA checklists of what to include in an EA




or EIS. No project-specific information was included.










EPA Programs involved or impacted: NEPA










What was the environmental issue? Unknown, until GISST was performed.  The results of the GISST




showed 'red flags' for each issue. These were then communicated to the agencies requesting the




information.










What was the ecological/environmental protection relationship? Multiple and depended on the project.










How did the tool aid in the resolution of the problem?  The problem here is an administrative one-how




does EPA provide information to those entities preparing NEPA documents early and specific enough




that they can incorporate the information into their draft EA or EIS, or perform further investigations




(e.g., field work or analysis of data)? The results of GISST point out areas of concern that should be




further analyzed in the NEPA documents. This not only aids the preparer, but also EPA, in that the






                                            69

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reviewer can cross-check GISST with the information and analysis contained in the NEPA document




and determine whether the concerns were adequately addressed.










What management decision was made? None, GISST results are for the EA/EIS preparers to




incorporate into their draft documents. EPA may not see the final draft document for 1-2 years.










What GPRA goals were addressed? 9










What stakeholders were involved?  Different Federal agencies and their contractors (if applicable).










What were the follow up steps or lessons learned?  Due to limited resources, GISST is only performed




as a courtesy to sister Federal agencies, not to contractors or private citizens. Thus, a Federal agency




supervising a contractor to prepare assessments must contact EPA Region 6 and request information or




GISST. Or if contacted by a contractor, a Federal  contact must be provided. For the first few




requests for information, EPA sent the GISST output in the form of a spreadsheet (Table A-3) and a




letter indicating where the criteria could be found on the EPA website.  For people not familiar with




GISST or the output, this was not very user friendly. Therefore, a new letter was developed that




summarized the issues that scored '4' or '5-indicating a high concern to EPA (Figure B-12). Other




information on how the GISST results might be used were also provided.
                                             70

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Table 3. Sample GISST output (2 mile buffers) for Aransas National Wildlife Refuge, Rancho,
Blackjack and Lamar Units.
 Criterion Name
Rancho
Blackjack
Lamar

Surface Water Use (% of streams
meeting designated use)
Water Quality (STORE!
Exceedances per square mile)
Annual Average Rainfall (inches
per year)
Unified Watershed Assessment
(State Priorities)
Average surface water flow
(cubic feet per second)
Average aquifer geology score
Score for the Distance to surface
water
Ozone nonattainment score by
county
Sole Source Aquifer Score
Surface water quantity
(stream/shoreline miles per sq
mile)
Percent of area that is surface
water
Average Soil Permeability Score
Average Ground water probability
score
Percent wildlife habitat
Land Use/Land Cover average
Raw Score Raw value
value
0 30

0 1 0

41.1 4 41

3

1315.7 2 0

4
3

1

1
1.715723 5 1.446415


32 4 33

1.4
1.6

90.986191 5 92.562683
4.8 5 4.9
Score
3

1

4

3

5

4
1

1

1
4


4

3.4
3.0

5
5
Raw Score
value
0 3

0 1

35.5 3

3

43.2 4

4
3

1

1
1.112762 2


32.7 4

3.5
3.2

94.800003 5
4.9 5
 ranking
                                            71

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Criterion Name
Rancho
Blackjack
        Lamar
                                   Raw        Score   Raw value    Score   Raw        Score
                                   value                                     value
Percent Agricultural Land

Percent Wetland

Percent within 100 year flood plain

Percent within 500 year flood plain

Road density (road mile per sq
mile)

Channelization (channel/canal
miles per square mile)

Number of other sites around the
facility

Percent of Population without a
High School Degree

Educational Achievement Score

Percent of households that are
economically stressed

Percent of population that is
considered a minority

Percent of population that is under
7 years of age

Percent of population that is over
55 years of age

Percent of population that is under
1 year of age (natality)

Percent of population over  16 that
is unemployed

Population density (persons per
square mile)

Total Population
8.360503    1        7.338628     1       1.250755     1

43.846172   4        21.881397    2       21.212193    2

77.910126   5        42.812851    4       70.91584     5

85.366623   5        52.961918    5       83.556107    5

1.43507     2        1.006213     1       2.203287     4
0.302875    1
34.9282     2
12.3967     1
1.2397      1
52.0661     1
1.2397      1
5.9091
5.831836    1
242
0.052736     1
29.2373
6.5359
1
10.1307      1
47.7124      1
0.9804
1.5873
1.506599     1
306
        0
            1
        26.5625      1
            2.3                   2                    1

26.1905     1        34.3066      2       35.124       2
5.8407      1
        7.4336       1
        52.5664      1
        0.708
        1.0163       1
        23.782158    1
        565
                                              72

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Criterion Name
Rancho
Blackjack
Lamar

Percent of population does not
speak English well/none
Percent of households that are
linguistically isolated
Percent of population that is
foreign born
Score for Age of houses
Cumulative chemical releases to
Raw
value
0

0

0


0
Score Raw value Score Raw
value
1 0.3584 1 0

10 10

1 3.9216 1 3.8938

1.9 1.6
1
Score
1

1

1

1.4

air from TRI

Cumulative chemical releases to
water from TRI

Cumulative chemical releases to
land from TRI

Toxicity weighted releases to air

Toxicity weighted releases to
water
0

0
                                             73

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Figure 26
           74

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Figure 27.  Sample letter explaining the GISST output.
             UNITED  STATES  ENVIRONMENTAL  PROTECTION  AGENCY
         Xs                                REGION 6
                                   1445 ROSS AVENUE, SUITE (200
                                     DALLAS TEXAS 75202-2733
RE: Comprehensive Conservation Plan/Environmental Assessment for the Aransas National
Wildlife Refuge Complex

Dear:

       The Environmental Protection Agency (EPA) has reviewed the information concerning
the Comprehensive Conservation Plan/Environmental Assessment for the Aransas National
Wildlife Refuge (NWR) Complex and included the results of our GIS Screening Tool (GISST).
The output of this GIS tool is provided to assist the US Fish and Wildlife Service with the
Environmental Assessment (EA) of the area. The GISST is a screening level assessment tool
only and does not replace the need for field investigations, it merely points out what could exist
in the project area.

       The GISST uses GIS coverages and Hydrologic Unit Codes (HUC) for watersheds, then
uses a decision structure to score criteria for a wide variety of concerns.  The scores for each
criterion range from 1, lowest environmental concern, to 5, highest potential concern or
vulnerability. This scoring  system is performed with a 2 mile buffer around each NWR unit area
and at 4 miles around each NWR unit. These 2 buffers should give you a sense of direct effects
(2 mile buffer) and indirect effects (4 mile buffer).  Further details on the nature of the criteria
can be found at www.epa.gov/ earthIr6/6en/xp/cria.pdf. In order to keep this letter to a
manageable size, we have not included specific details of this tool (-30 page document), but we
have enclosed a help sheet.

       Additionally, EPA is concerned that two issues be adequately assessed in the EA: 1)
cumulative effects and 2) environmental justice. Several EAs that EPA Region 6 has reviewed in
the past have not adequately addressed these two concerns. Please feel free to contact us if you
need assistance with these areas in the preparation of your NEPA documents.

       Thank you for this opportunity to comment. If there are any questions please contact Dr.
Sharon L.  Osowski of my staff at osowski.sharon@epa.gov or (214) 665-7506.
                                          75

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CIS SCREENING TOOL (GISST) HELP SHEET

The enclosed GISST printout includes the following descriptions:

Column 1: Unique factor/criterion identifier.
Column 2-4: Criterion values/scores
Column 5: Criteria descriptions

Rows 1-8: Location information
Rows 9-21: ANWR subunits and sub water shed level criteria
Rows 22-43: Environmental vulnerability  criteria for 4 miles around location project
Rows 44-68: Socio-economic criteria for 4 miles around project
Rows 69-78: Toxicity criteria for 4 miles around project
Rows 79-100: Environmental vulnerability criteria for 0.5 miles around location project
Rows 101-125: Socio-economic criteria for 0.5 miles  around project
Rows 126-135: Toxicity criteria for 0.5 miles around project

Other information:

Many of the criterion identifiers are paired; 1) one identifier for the actual value as determined by
GIS and 2) one identifier for the score that the value received under the GISST scoring system.
For example, Row 9 shows the surface water use identifier (SURWTRUSE) and shows that
18.3% of streams are meeting their designated use within this particular subwatershed (based on
the USGS HUC system). One interpretation of this is that the majority of streams (81.7%) in this
subwatershed are not meeting their designated use under Clean Water Act Section 303d.  The
identifier SURWTRUSES (Row 10) shows the score or ranking of this surface water use value
under GISST. In this example, surface water use scores the highest value, 5, indicating a high
level of vulnerability and concern to EPA. Criteria are ranked using a 1 to 5 scale, with 1
representing low concern and 5 representing high concern. Scores of "4" or "5" are highlighted
on the enclosed table and should be investigated further.

       Socioeconomic criteria can be used as a starting point to assess environmental justice
issues and to prepare communications strategies for scoping meetings or public meetings (e.g.,
number of children, high school education, English ability, etc.).  Toxicity criteria can be used as
a starting point to determine whether pollution sources may impact the proposed project site.
Environmental criteria can be used as a starting point to determine and prioritize traditional
"NEPA" issues.

The following scored "high" for the proposed site and should be further investigated:
       •      Rainfall. The Rancho and Blackjack Units receive more rainfall on average than
             the Lamar Unit. Rainfall is important in calculating potential runoff and other
             pollution events.
       •      Average surface/stream flow. The Blackjack Unit and Lamar Units may have low

                                         76

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              surface water or stream flow. The less average stream flow the greater the
              concern for contaminant loading in a water body.  This criteria is evaluated with
              data addressing the potential for pollutants being released to streams (see toxicity
              criteria).
       •      Aquifer geology score. Based on the geological formations, aquifers or
              groundwater are likely to be present for all three NWR units.
       •      Percent surface water (2 and 4 mile buffers). This criterion indicated that there is
              a high proportion of surface water in all three Units within 4 miles and within 2
              miles of the Rancho and Blackjack Units.
              Percent Wildlife habitat (2 and 4 mile buffers). Using land cover GIS  coverages,
              there is a high percentage of habitat that could potentially be used by wildlife
              (wetlands, rangelands, forest lands, woodlands, and/or bottomlands).  This is to be
              expected for these locations.
              Land Use ranking (2 and 4 mile buffers).  Each land use type in the GIS coverage
              is judged as to wildlife habitat quality. A score of "5" indicates wildlife habitat
              defined as rangeland, wetlands, forest lands, woodlands, herbaceous uplands,
              shrublands, open water.  This is to be expected for these locations.
              Percent Wetlands (2 mile buffer). The Rancho Unit has a high percentage of
              wetland areas based on the GIS land cover coverage.
       •      Percent area within 100 year floodplain (2 and 4 mile buffer).  All three Units are
              likely to reside in the  100 year floodplain (2 mi).
              Percent area within 500 year floodplain (2 and 4 mile buffer).  This indicates that
              a high proportion of each ANWR unit occurs in the 500 year floodplain.
              Road  density (2 mile buffer). (Lamar Unit only) High road density is often an
              indicator of habitat fragmentation, potential traffic congestion, or safety issues.
              Number of other sites near project area (4 mile buffer). These are other industries,
              pollution sources, or protected lands that could cumulatively affect the Blackjack
              Unit.

NOTE: GISST is a screening-level analysis only and is not a substitute for field investigations or
ground verification of existing data.
                                           77

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                           LITERATURE CITED
Abbruzzese, B. and S. G. Leibowitz. 1997. A synoptic approach for assessing cumulative impacts to




       wetlands. Environmental Management 21:457-475.










Boughton, D. A., E. R. Smith, and R. V. O'Neill. 1999. Regional vulnerability: a conceptual




       framework. Ecosystem Health 5:312-322.










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                                            86

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 APPENDIX A
Finalized Criteria

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              APPENDIX A: FINALIZED CRITERIA







Introduction




      Appendix A contains criteria that have been peer-reviewed and used in one or more




projects. These criteria also have an adequate data source that has been 'quality




assured/controlled.' The criteria are grouped loosely by topic, although overlap with another




group can occur. The descriptions of these groups as well as the general GISST mathematical




formula are given in Chapter 3. Published references are italicized, whereas internal documents,




letters, or other unpublished references are underlined.  Additionally, the score boxes for each




criterion reflect a continuous distribution without any gaps among the 1-5 ranking. For example,




a criterion that shows 2-4% as rank 1 and  5-7% as rank 2 is calculated without decimal places




and thus produces integer ranks only.
                                        A-l

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Water Quality
Dv Criterion: Surface Water
Supporting Designated Use
> 99%
98-76%
no data
75-50%
< 50%
Use
Score
1
2
3
4
5
Databases:
U.S. EPA.  1994. Clean Water Act, Section 305 (b): Oklahoma State Water Quality Inventory
Reports, 303 (d) List Region 6, US EPA, Dallas, TX.

National Hydrography Database.  http://oaspub.epa.gov/waters/w305b_report.region?p_region=6

National Water Quality Standards Database, http://www.epa.gov/wqsdatabase/index.html

References:
Spooner, C. 1994. Watershed Agricultural Impact Task Force, W.A.I.T. Report, Research Triangle Institute (RTI),
US EPA, North Carolina.

Definitions, Assumptions, Limitations, Uncertainties:
1.      CWA 305(b) reports, with data manipulation, describe the surface water quality for 8
       digit HUCs.  The NHD also displays such data in the WATERS database.
2.      Assessed water bodies are likely to be lower quality segments.  Stream segments with no
       data may or may not be good quality.
3.      Designated uses are defined by state water quality programs. The most recent
       compilation of these is the National Water Quality Standards Database (WQSDB), a
       single point of access to EPA and state water quality standards (WQS) information.
4.      This criterion may be calculated for the most appropriate geographic area and scale (e.g.,
       watershed subunits, transportation corridors, or project areas).
5.      The area of analysis may be broken into 1 km grid cells for GISST criteria computation.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6, Dallas, TX, 75202),carney.gerald@epa.gov
Mike Bechdol (U.S. EPA Region  6, Dallas, TX, 75202), bechdol.michael@epa.gov
Paul Koska, (U.S. EPA Region 6, Dallas, TX, 75202), koska.paul@epa.gov
Charles Spooner, (RTI), EPAHQ, Washington, D.C. spooner.charles@epa.gov
Contractor Support:  Jeff Danielson (Lockheed Martin, EPA Region 6 support),
danielson.jeff@epa.gov
                                          A-2

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Dv Criterion: Water Quality (STORET Data)
# STORET Exceedances/Area (ft2) Score
< 5.00 X ID'12
5.00 X ID'12 < value < 5.00 X 10'11
5.00 X ID'11 < value < 5.00 X 1040
5.00 X ID'10 < value < 5.00 X 10'9
>5.00X1Q-9
1
2
3
4
5
Databases:
U.S. EPA. 1996. STORET Database, Office of Water, US EPA, Washington, DC.

US EPA Website.  Surf Your Watershed/ IWI 1995/6 305(b).http://www.epa.gov/surf/iwi

Definitions, Assumptions, Limitations, Uncertainties:
1.      Assessed Safe Drinking Water Act (SOWA) contaminants (22 volatile organic
       compounds, 35 organics/pesticides, 17 inorganics/metals,  and trihalomethane) can
       adversely impact public health and surface water quality.
2.      National primary drinking water standards, established under SDWA, are compared to
       STORET ambient water data.  Comparisons for 65 SDWA contaminants were matched to
       surface (i.e. stream, lake, reservoir) and ground water (well and springs) STORET
       data.
3.      Exceedances are defined as STORET sampling station data reporting chemical
       concentration greater than the SDWA MCLs (Maximum Concentration Levels).  Sixteen
       years of data were evaluated.
4.      Exceedances are based on 0.5 MCL for lOCs, VOCs, SOCs, RADS, and nitrates for the
       data years,  1990-1996.
5.      Eight digit  HUCs were evaluated to determine the scores.  The ranking values were the
       quotients of the number of exceedances in specific HUCs  divided by the area in square
       feet of the associated HUC.
6.      This criterion may be calculated for the most appropriate geographic area and scale (e.g.,
       watershed subunits, transportation corridors, or project areas).
7.      The area of analysis may be broken into 1 km grid cells for GISST criteria computation.

EPA Contacts:
Mike Bechdol (U.S. EPA Region 6, Dallas, TX,  75202), bechdol.michael@epa.gov
Paul Koska, (U.S.  EPA Region 6, Dallas, TX, 75202), koska.paul@epa.gov
Gerald Carney (U.S. EPA Region 6, Dallas, TX, 75202), carney.gerald@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                         A-3

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Dv Criterion: Rainfall
Rainfall (in/vr)
< 12.5
12.6-25
26-37.5
37.6-49
>50

Score
1
2
3
4
5
Databases:
Blacklands Research Center, 1995. Humus - Hydrologic Unit Modeling for the United States,
USDA/NRCS, USDA/ARS, and Texas A&M University, College Station, TX.

References:
U.S. EPA, 1991. Regional Assessment of Aquifer Vulnerability and Sensitivity in the Conterminous United States.
EPA/600/2-91/043, Office of Research and Development, Washington, D.C.

Definitions, Assumptions, Limitations, Uncertainties:
1.      The greater the annual rainfall, the more infiltration relative to factors such as slope and
       soil type to the groundwater.

2.      The greater the annual rainfall, the more water available for runoff to surface water.

3.      All known facilities in a project area receive a comparable amount of annual rainfall.

4.      This  criterion may be calculated for the most appropriate geographic area and scale (e.g.,
       watershed subunits, transportation corridors, or project areas).

5.      The area of analysis may be broken into 1 km grid cells for GISST criteria computation.

EPA Contacts:
Mike Bechdol  (U.S. EPA Region 6, Dallas, TX, 75202), bechdol.michael@epa.gov
Tom Nelson (U.S. EPA Region 6, Dallas, TX, 75202),  nelson.thomas@epa.gov
Gerald Carney (U.S. EPA Region 6, Dallas, TX, 75202), carney.gerald@epa.gov
Joe Swick (U.S. EPA Region 6, Dallas, TX, 75202), swick.joseph@epa.gov

Contractor  Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                          A-4

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Dv Dj Criterion:
Ibs released to area
< 300,000
299,999-1,000,000
1,000,001-2,000,000
2,000,001-5,000,000
> 5,000,000
Water Releases
Score
1
2
3
4
5
Databases:
U. S. Environmental Protection Agency.  2000.  Toxic Release Inventory.  TRI Data: SARA
Community Right-to-know. Washington, D.C. [updated annually]

Steeves, P. and D. Nebert.  1994. Hydrologic Unit Maps of the Conterminous U.S.,U.S.
Geological Survey., Reston, VA.

References:
U. S. Environmental Protection Agency. 1989. Toxic Chemical Release Inventory Risk Screening Guide Volumes 1
and 2. US EPA Office of Pesticides and Toxic Substances. EPA 560/2-89-002.

Definitions, Assumptions, Limitations, Uncertainties:
1.      US EPA requires manufacturing industries to estimate their annual releases of specific
       hazardous chemicals to water. The releases are reported in pounds per year.
2.      Chemical releases to waters within the project area can have a negative impact upon the
       environment and human health around the release point and downstream.
3.      Chemical releases occur over a one year time period and not as a one time event.
4.      TRI releases are estimates. There are other data sets which can be used to determine the
       cumulative chemical release.
5.      TRI releases may not represent all the industrial chemical releases to water.  Other source
       data will be included in this criteria (i.e., state and municipal data).
6.      This criterion may be calculated for the most appropriate geographic area and scale (e.g.,
       watershed subunits, transportation corridors, or project areas).
7.      The area of analysis may be broken into 1 km grid cells for GISST criteria computation.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6, Dallas, TX, 75202), carney.gerald@epa.gov
Mort Wakeland (U.S. EPA Region 6, Dallas, TX, 75202), wakeland.morton@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                          A-5

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                           Dv Criterion: Surface Water Quantity

                          mi /mi2 shore or stream length	Score
                          < 0.917
                          0.917-1.15
                          1.16-1.43
                          1.44-1.7
                          > 1.7
1
2
3
4
5
Databases:
U.S. Census Bureau, 2001.  TIGER/Line Files, Census 2000. Washington, D.C.

National Resource conservation Service (NRCS), State Soil Geographic Database (STATSGO),
1/250,000 scale, variable dates for data.

USGS, 1999. National Hydrography Dataset. USGS, Reston, VA.

References:
U.S. Army Corps of Engineers, Section 10 Rivers and Harbors Act of 1899.

U.S. EPA. Clean Water Act, Section 401 and 404, Regulations and Guidance.

Definitions, Assumptions, Limitations, Uncertainties:
1.     Surface waters are calculated for segment and shoreline distances for streams, rivers, and
       lakes. Scaling scores (rankings) are derived from total miles in a watershed or project
       area divided by the area in square miles of associated HUCs.
2.     River and lake surface water areas and depths are not considered.
3.     The more surface water area present, the higher potential for ecological impacts.
4.     Shoreline is of considerable interest because of the sensitivity of associated ecological
       communities.
5.     This  criterion may be calculated for the most appropriate geographic area and scale (e.g.,
       watershed subunits, transportation corridors, or project areas).
6.     The area of analysis  may be broken into 1 km grid cells for GISST criteria computation.

EPA Contacts:
Joe Swick (U.S. EPA Region 6, Dallas, TX, 75202), swick.joseph@epa.gov
Sharon Osowski (U.S. EPA Region 6, Dallas, TX, 75202) osowski.sharon@epa.gov
Tom Nelson (U.S. EPA Region 6, Dallas, TX, 75202), nelson.thomas@epa.gov
David Parrish,  (U.S. EPA Region 6, Dallas, TX 75202), parrish.david@epa.gov
                                          A-6

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                                Dv Criterion: Distance to Surface
                                            Water
                              Distance (ft)
Score
                              > 8,100
                              8,100-2,700
                              2,699-900
                              899-301
                              <300
  1
  2
  3
  4
  5
Databases:
U.S. Census Bureau, 2001. TIGER/Line Files, RF3 Data. Census 2000. Washington, D.C.

U.S. Geological Survey, 1999. National Hydrography Dataset. Reston, VA.

Definitions, Assumptions, Limitations, Uncertainties:
1.      Vulnerable surface waters for this criteria are only those in the U.S. Census Bureau,
       TIGER 2001 Database.

2.      The closest surface water is defined to be the closest surface water down gradient from
       Federal facility pollution sources.

3.      Distance to surface water is measured as straight line distance from the outer boundary of
       the facility with no buffer zone(incorporation of drainage distances are future
       enhancements).

EPA Contacts:
Gerald Carney (U.S. EPA Region 6, Dallas, TX, 75202), carney.gerald@epa.gov
Tom Nelson (U.S. EPA Region 6, Dallas, TX, 75202), nelson.thomas@epa.gov
Angel Kosfiszer (U.S. EPA Region 6, Dallas, TX 75202), kosfiszer.angel@epa.gov
Mike Bechdol (U.S. EPA Region 6, Dallas, TX, 75202), bechdol.michael@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                         A-7

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                           Dv Criterion: Ground Water Probability
Probability1
                                                  Score
                         <2.5%
                         2.6- 5%
                         5.1-10%
                         10.1-20%
                         > 20%
                           1
                           2
                           3
                           4
                           5
                          Probability of ground water being within 6-8 ft. of surface.
Databases:
Oklahoma Water Resources Board. 1993. Statistical Summary of Groundwater Quality Data:
1986-1991 for the Major Groundwater Basins in Oklahoma, FY 93 106 Groundwater Task 400,
Planning and Management, Oklahoma City, OK.

National Resource Conservation Service. 1996. Downloadable ten acre grid soils data files from
NRCS, Oklahoma City, OK.

National Resource Conservation Service. 1996. Oklahoma STATSGO Database, 1:250,000 soil
data. U. S. Department of Agriculture, Washington, D.C.

References:
U.S. EPA. 1987. Drastic: A Standardized System for Evaluating Ground Water Pollution Potential Using
Hydrogeologic Settings. EPA/600/2-87/035. Environmental Research Laboratory. Ada, OK.

Definitions, Assumptions, Limitations, Uncertainties:
1.      Area of the facility is represented as the facility area plus a ten acre buffer around each site.
2.      Only those 10 acres with a >20% probability of ground water being within six to eight feet
       of the surface(scaling score of 5) were used for the criteria site percentage estimate.
3.      The six to eight foot soil profile estimates the probability of ground water vulnerability
       beneath the facility and buffer area.
4.      The higher the probability of ground water beneath the facility the more vulnerable the
       resource.

EPA Contacts:
Mike Bechdol (U.S. EPA Region 6, Dallas, TX, 75202), bechdol.michael@epa.gov
Tom Nelson (U.S. EPA Region 6, Dallas, TX, 75202), nelson.thomas@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                          A-8

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                            Dv Criterion: Ground Water Quality

                 Mean Nitrate-Nitrite concentration (mg/L)	Score
                 no aquifer or < 3                                  1
                 3-4.5                                             2
                 4.6-6                                             3
                 6.1-7.4                                           4
                 >7.5                                             5
Databases:
Oklahoma
Water Resources Board, 1993. Statistical Summary of Groundwater Quality Data: 1986-1991 for
the Major Groundwater Basins in Oklahoma, FY 93 106 Groundwater Task 400, Planning and
Management, Oklahoma City, OK.

National Resource Conservation Service.  1996. Oklahoma STATSGO Database, 1:250,000 soil
data. U. S. Department of Agriculture, Washington, D.C.

References:
U.S. EPA. 1991. Protecting the Nation's Ground Water: EPA's Strategy for the 1990 's (part D: Agency Policy on
EPA's Use of Quality Standards in Ground Water Prevention and Remediation Activities). 21Z-1020. Office of the
Administrator (WH-550G). Washington, D.C.

Definitions, Assumptions, Limitations, Uncertainties:
1.      The Maximum Contaminant Level (MCL) for nitrate in ground water is 10 mg/L established
       under the Safe Drinking Water Act. Nitrate is assumed to be the major ground water contaminant
       ofconcern.
2.      Phosphates and other nutrients are not included in this criteria. Nutrients will be covered in
       separate criteria (i.e. Surface Water Quality)
3.      Oklahoma ground water quality data is presented at the county and aquifer level. Approximation
       of sampling locations were derived from combining aquifer, watershed, river, and county
       location data.
4.      Where counties include more than one aquifer, the watershed or project area that incorporated a
       certain river was assumed to be associated with the aquifer with the same name as the river.
5.      This criterion reflects the acute, non-chronic condition.
6.      This criterion may be calculated for the most appropriate geographic area and scale (e.g.,
       watershed subunits, transportation corridors, or project areas).
7.      The area of analysis may be broken into 1 km grid cells for GISST criteria computation.

EPA Contacts:
Gerald Carney  (U.S. EPA Region 6, Dallas, TX, 75202), carney.gerald@epa.gov
Clay Chesney (U.S. EPA Region 6, Dallas, TX, 75202), chesney.claybourne@epa.gov
David Parrish, (U.S.  EPA Region 6, Dallas, TX, 75202), parrish.david@epa.gov
Contractor Support: Jeff Danielson (Lockheed Martin, EPA Region 6 support),
danielson.jeff@epa.gov
                                            A-9

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                               Dv Criterion: Unified Watershed
                             Assessment (State Priority Data)
                           Supporting Designated Use
Score
                          Low State Priority
                          Medium State Priority
                          High Priority or no data
Databases:
U.S. EPA.  1994. Clean Water Act, Section 305 (b), State Water Quality Inventory Reports, 303
(d) List. Dallas, TX.

National Hydrography Database. http://oaspub.epa.gov/waters/w305b_report.region?p_region=6

National Water Quality Standards Database,  http://www.epa.gov/wqsdatabase/index.html

References:
Spooner, C. 1994. Watershed Agricultural Impact Task Force, W.A.I.T. Report, Research Triangle Institute (RTI),
US EPA, North Carolina.

Definitions, Assumptions, Limitations, Uncertainties:
1.      CWA 305(b) reports, with data manipulation, describe the surface water quality for 8
       digit HUCs. The NHD also displays such data in the WATERS database.

2.      Assessed water bodies are likely to be lower quality segments.  Stream segments with no
       data may or may not be good quality.

3.      Designated uses are defined by state water quality programs. The most recent
       compilation of these is the National Water Quality Standards Database (WQSDB), a
       single point of access to EPA and state water quality standards (WQS) information.

4.      This criterion may be calculated for the most appropriate geographic area and scale (e.g.,
       watershed subunits, transportation corridors, or project areas).

5.      The area of analysis may be broken into 1 km grid cells for GISST criteria computation.

EPA Contacts:
Tom Nelson (U.S. EPA Region 6, Dallas, TX, 75202), nelson.thomas@epa.gov
Paul Koska, (U.S. EPA Region 6, Dallas, TX, 75202), koska.paul@epa.gov
Charles Spooner, (RTI), EPA HQ, Washington, D.C. spooner.charles@epa.gov
Angel Kosfiszer (U.S. EPA Region 6, Dallas, TX 75202), kosfiszer.angel@epa.gov
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                           Dv Criterion: Clean Water Act 303(d)
                            Segments (State Priority Data)
                         Present in Grid Cell
Score
                         No
                         Yes
   1
   5
Databases:
TCEQ, 2001. Stream Segments 2000. TCEQ, Austin, TX.

EPA, 2003. Texas Interstate 69 Baseline Analysis Grid. EPA, Region 6, Dallas, TX.

References:
EPA. Clean Water Act 303(d) Regulations & Guidance.

Texas Water Quality standards.

Definitions, Assumptions, Limitations, Uncertainties:
1.      CWA 303(d) assessments are done by States and approved by EPA.

2.      TMDL= Total Maximum Daily Load.

3.      Segments listed as impaired in the file are used in this criteria.  Impaired segments
       receive a score of 5.

4.      Stream segments with no data are assumed to be good quality.

5.      Designated uses are defined in the State Water Quality Standards.

6.      This criterion may be calculated for the most appropriate geographic area and scale (e.g..
       watershed  subunits, transportation corridors, or project areas).

7.      The area of analysis may be broken into 1 km grid cells for GISST criteria computation.

EPA Contacts:
Dominique Lueckenhoff (U.S. EPA Region 6, Austin, TX), lueckenhoff.dominique@epa.gov
David Parrish, (U.S. EPA Region 6, Dallas, TX 75202), parrish.david@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
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                           Dv Criterion: Average Stream Flow

                          Mean  Surface Water Flow (ft3/sec) Score
                          > 10,000
                          9,999-1,000
                          999-100
                          99-0.1
                          0 or no data
1
2
3
4
5
Datasets:
U.S. EPA. 1996. STORE! Database, Office of Water, US EPA, Washington, DC.

References:
US EPA Website.  Surf Your Watershed/ IWI1995/6 305(b).http://www.epa.gov/surf/iwi

Definitions, Assumptions, Limitations, Uncertainties:
1.      Average cubic feet per second stream flow was calculated over a one year period for
       existing stream flow monitors operated by States.

2.      The less average stream flow the greater the concern for contaminant loading in a water
       body. This criteria is evaluated with data addressing the potential for pollutants being
       released to streams in the evaluated watershed or project area.

3.      There are significant data gaps.  Storet data does not monitor all stream segments in
       Region 6.

4.      This criterion may be calculated for the most appropriate geographic area and scale (e.g..
       watershed subunits, transportation corridors, or project areas).

5.      The area of analysis may be broken into 1 km grid cells for GISST criteria computation.

EPA Contacts:
Gerald Carney, (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Dan Parker, STORET data manager, HQ EPA, parker.dan@epa.gov
Paul Koska, (U.S. EPA Region 6, Dallas, TX, 75202), koska.paul@epa.gov
David Parrish, (U.S. EPA Region 6, Dallas, TX, 75202), parrish.david@epa.gov
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                         Dv criteria: Sole Source Aquifer (SSA) l
                    SSA is beneath site2
Score
                    No SSA is beneath site       1
                    SSA is beneath site	5_
                     Sole Source Aquifer (> 50% of drinking water supply to area).
                     Aquifer or recharge area by data set overlay in GIS.
Databases:
U. S. EPA 1996 Sole source aquifer GIS layer. US EPA Region 6, Dallas, TX


References:
US EPA, 2000. U.S. Environmental Protection Agency Designation of Sole Source Aquifers, Fact Sheet,
http://www.epa.gov/earthlr6/ 6wq/swp/ssa/ssafacts.htm, Region 6 Ground Water / UIC Section.

Federal Registers: Edwards Underground Reservoir  (40 FR 58344,12/16/75), Chicot Aquifer System (53 FR
20893, 06/07/88), Austin-Area Edwards Aquifer (53 FR 20897, 06/07/88), Southern Hills Aquifer System (53 FR
25538, 07/07/88), Arbuckle-Simpson Aquifer (54 FR 39230, 09/25/89).

Louisiana Geological Survey. 1994. Chicot Aquifer boundaries delineated in part from the Geologic Map of
Louisiana Department of Natural Resources.

Mississippi Geological Survey. 1969. Southern Hills boundaries delineated in part from Geologic Map of
Mississippi

Slagle, Ardis, and Slade 1986 Edwards Aquifer-Austin Area boundaries delineated from the map Recharge Zone of
the Edwards Aquifer Hydrologically Associated with Barton Springs in Austin Area. Texas. 1:48,000.

Definitions, Assumptions, Limitations, Uncertainties:
1.      A Sole Source Aquifer is an aquifer designated by EPA as the "sole or principal source"
       of drinking water for a given service area (supplies 50% or more).
2.      There may be many aquifers which could be designated by EPA to be Sole Source
       Aquifer but are not.
3.      Designation are by petition from any person, individual, corporation, State, or
       Municipality.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Clay Chesney (U.S. EPA Region 6, Dallas, TX, 75202), chesney.claybourne@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                            A-13

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                                 Dv, Dj Criterion:  Floodplain

                            % of area	Score
                            No data
                            < 20%
                            20-29%
                            30-39%
                            40-49%
                            > 50%
0
1
2
3
4
5
Databases:
Federal Emergency Management Agency. Q3 Flood Data (mid-90's data).

References:
Executive Order 11988, 1977. Flood Plain Management.

Definitions, Assumptions, Limitations, Uncertainties:
1.      Floodplains are digitized from FEMA FIRMR maps.

2.      Percent coverage is quantitative only. No decisions as to floodplain quality were made.

3.      Floodplains are defined as the areas where the zone = A (100 year flood plain) or the
       zone = X500 (500 year flood plain).

4.      Changes in upstream hydrology will affect future flooplain extent.

5.      This criterion may be calculated for the most appropriate geographic area and scale (e.g.,
       watershed subunits, transportation corridors, or project areas).

6.      The area of analysis may be broken into 1 km grid cells for GISST criteria computation.

EPA Contacts:
David Parrish (U.S. EPA Region 6, Dallas, TX, 75202), parrish.david@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                         A-14

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                            Dv Criterion: Aquifer/Geology Rating

                      Aquifer Media	Score
                      No aquifer or
                        massive shale/metamorphic/igneous
                      Weathered/glacial till
                      Sandstone/ limestone
                      Sand/gravel
                      Basalt/karst limestone
1
2
3
4
5
Databases:
US Geological Survey Digital Data Series DDS-11. Geology of the coterminous United States at
1:2,500,000 scale- a digital representation of King, P. B., and H. M. Beikman map 1974.

US Geological Survey, 1994. Hydrologic unit maps of the coterminous United States.

US EPA, 1987, DRASTIC Typical Ratings, EPA/600/2-87/035.

References:
Allen, E., C. Abshire, M. Bechdol, A. Noell, D. Reazin, J. Torres, and K. Williams, 1997. Region 6Interim Source
Water  Vulnerability Assessment, Source Water Protection Branch, US EPA, Dallas, TX.

US EPA, 1991. Regional Assessment of Aquifer Vulnerability and Sensitivity in the Conterminous United States.
EPA/600/2-91/043.

Definitions, Assumptions, Limitations, Uncertainties:
7.      Ratings are a combination of aquifer and geology rankings (Allen et al. 1997), using
       USGS, EPA DRASTIC, and Aquifer Vulnerability data.
8.      The Region 6 methodology (Allen et al.1997) uses an algorithm to combine the
       aquifer/geology rating and the watershed and aquifer areas (area weighting).
9.      Aquifer media  ratings are (from lowest to highest rating): massive shale,
       metamorphic/igneous, weathered, glacial till, bedded sandstone-limestone and shale
       sequences, massive sandstone, massive limestone, sand and gravel, basalt, karst
       limestone.
EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Mike Bechdol (U.S. EPA Region 6, Dallas, TX, 75202), bechdol.michael@epa.gov
Clay Chesney (U.S.  EPA Region 6, Dallas, TX, 75202), chesney.claybourne@epa.gov
David Parrish, (U.S. EPA Region 6, Dallas, TX, 75202), parrish.david@epa.gov
Contractor Support:  Jeff Danielson (Lockheed Martin, EPA Region 6 support),
danielson.jeff@epa.gov
                                          A-15

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Dv Criterion: Channelization
Channels in watershed (mi/mi2)
0.0
0.1-0.515
0.5161.400
1.401-4.060
> 4.061
Score
1
2
3
4
5
Databases:
U.S. Census Bureau, 2001.  TIGER/Line Files, Census 2000.  Washington, D.C.

References:
Good, W. 1998. Coast 2050: Toward a Sustainable Coastal Louisiana Report, Louisiana Department of Natural
Resources, Coast 2050 Planning Management Team, [incomplete citation]

Definitions, Assumptions, Limitations, Uncertainties:
1.      Channelization refers to canals, ditches, aqueducts and is not specific to channelization of
       a specific use or size.
2.      Channelization disrupts the natural water flow in an area allowing salt water intrusion and
       land loss to accelerate.
3.      Channelization is a cause of habitat fragmentation.
4.      There could be many canals constructed since 1992 which are not captured by the
       database.
5.      There may be canals which are labeled as streams in dataset.
6.      Watersheds vary in size, shape, water quantity, and flow characteristics.
7.      Boat traffic on canals and runoff into ditches contribute chemical contaminants to the
       water ecology.
8.      This criterion may be calculated for the most appropriate geographic area and scale (e.g.,
       watershed subunits,  transportation corridors, or project areas).
9.      The area of analysis may be broken into 1 km grid cells for GISST criteria computation.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Bill Klein (U.S. COENew Orleans, LA), william.p.klein.jr@mvn02.usace.army.mil

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                          A-16

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                           Dv Criterion: Individual Well Water 1

                    % population with individual water source	Score
                    < 10                                               1
                    10-19                                              2
                    20- 29                                             3
                    30-39                                             4
                    > 40%                                             5

                     Source of water to household is not a public system or a private
Databases:
Census 2000 Summary File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau,
2002.

References:
U.S. EPA, Region 6, March 2000. Environmental Education Targeting Study: Border Report, Analysis of Counties
Within the US/Mexico 100 Km Border Buffer, Gerald Carney, Office of Planning and Coordination, Dallas, TX
75202. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.       "Individual" water source is defined as sources other than public or private suppliers.
2.       Data is gathered at the block group level and must be modified to apply to watershed or other
        geographic area.
3.       Assessments by watershed or other geographic area use the "area weighting" technique for block
        groups bisected by geographic boundaries.
4.       It is assumed that "individual" water sources are most likely ground water sources accessed and
        maintained by the resident.  In the Region 6 U.S./Mexico Border, and areas of Louisiana
        (wetlands) and New Mexico (desert) "individual" exist as a variety of sources (i.e., surface, rain
        collection systems).
5.       It is assumed that the "individual" systems are monitored for quality less often, receive anti-
        microbial treatment sporadically or not at all, be seasonal in quantity and quality, require
        secondary transport containers, need to be stored without treatment, and therefore more likely to
        become contaminated.
6.       It is assumed that untreated water from "individual" sources will be used for cooking, washing,
        and cleaning.
7.       This criterion may be calculated for the most appropriate geographic area and scale (e.g.,
        watershed subunits, transportation corridors, or project areas).
8.       The area of analysis may be broken  into 1 km grid cells for GISST  criteria computation.

EPA Contacts: Gerald Carney (U.S. EPA Region 6 Dallas, TX),  carney.gerald@epa.gov
Contractor Support: Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                             A-17

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                       Dv Criterion: Septic Tank and Cesspool Use 1

                  %population with septic tank/cesspool    Score
                  16-25
                  26-35
                  36-45
                  > 45%
1
2
3
4
5
                   Wastewater disposal at residence is either a septic tank or cesspool.
Databases:
Census 2000 Summary File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau,
2002.

References:
U.S. EPA, Region 6, March 2000. Environmental Education Targeting Study: Border Report, Analysis of Counties
Within the US/Mexico 100 Km Border Buffer, Gerald Carney, Office of Planning and Coordination, Dallas, TX
75202. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.       Septic tanks are below ground open systems.  Cesspools are above ground open waste disposal
        systems.
2.       Data is gathered at the block group level and must be modified to apply to watershed  or other
        geographic area.
3.       Assessments by watershed or other geographic area use the "area weighting" technique for block
        groups bisected by geographic boundaries.
4.       It is assumed that other than closed public waste disposal is maintained by the resident. It is also
        assumed that there are many areas in Region 6 where closed, public waste disposal system are
        lacking (Border communities (colonias), rural farm  sites, mountain, wetland, and desert areas.
5.       It is assumed that the septic tank and cesspool have  a higher failure rate than public sewage
        systems, are monitored for quality less often, receive disinfection treatment sporadically or not at
        all, are seasonal in efficiency, often require periodic cleaning and waste transport, are breeding
        areas for disease vectors, and are more likely to cause contamination to residents.
6.       It is assumed that runoff and percolation to ground water can result in contamination of drinking
        water sources.
7.       This criterion may be calculated for the most appropriate geographic area and scale (e.g.,
        watershed subunits, transportation corridors, or project areas).
8.       The area of analysis may be broken into 1 km grid cells for GISST criteria computation.

Contacts: Gerald Carney (U.S. EPA Region 6 Dallas, TX), carney.gerald@epa.gov
Contractor Support: Jeff Danielson (Lockheed Martin, EPA Region 6 support),
danielson.jeff@epa.gov
                                             A-18

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                           j Criterion: TRI1 Reported Water
                                      Releases
                       Ibs released to water
Score
                       < 300,000                         1
                       300,000 < Ibs < 1,000,000           2
                       1,000,000 < Ibs < 2,000,000         3
                       2,000,000 < Ibs < 5,000,000         4
                       > 5,000,000                        5

                        1998 Toxic Release Inventory Data
Databases:
U. S. Environmental Protection Agency. 2000.  Toxic Release Inventory. TRI Data: SARA
Community Right-to-know. Washington, D.C. [updated annually]

References:
U. S. Environmental Protection Agency.  1989. Toxic Chemical Release Inventory Risk Screening Guide Volumes 1
and 2. US EPA Office of Pesticides and Toxic Substances. EPA 560/2-89-002.

Definitions, Assumptions, Limitations, Uncertainties:
1.      US EPA requires manufacturing industries to estimate their annual releases of specific
       hazardous chemicals to water. The releases are reported in pounds per year.

2.      Chemical releases to water can have a negative impact upon the environment and human
       health around the release point.

3.      Chemical releases occur over a one year time period and not as a one time event.

4.      TRI releases are estimates.

5.      TRI releases may not represent all the industrial chemical releases to water.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202 ), carney.gerald@epa.gov
Mort Wakeland (U.S. EPA Region 6, Dallas, TX, 75202), wakeland.morton@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                          A-19

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                              Dv Criterion: Soil Permeability
                            Rating1 (in/hr)
Score
                            <0.02
                            0.02-0.6
                            0.61-2.0
                            2.01-5.99
                            >6.0
  1
  2
  3
  4
  5
                            Permeability ratings are by 10 acre grids. The average of the grids,
                            inside or touching the facility boundary is ranked 1-5. In addition a
                            site is scored a 5 if the facility area and adjacent buffer is > 6.0 in./hr.
Databases:
National Resource Conservation Service.  1996. Downloadable ten acre grid soils data files from
NRCS, Oklahoma City, OK.

National Resource Conservation Service.  1996. Oklahoma STATSGO Database, U. S.
Department of Agriculture, Washington, D.C.

References:
U.S. EPA. 1987. Drastic: A Standardized System for Evaluating Ground Water Pollution Potential Using
Hydrogeologic Settings. EPA/600/2-87/035. Environmental Research Laboratory. Ada, OK.

Definitions, Assumptions, Limitations, Uncertainties:
1.      Area of the facility is represented as the facility area plus a ten acre buffer around each site.

2.      Only those 10 acres with a >20% probability of ground water being within six to eight feet
       of the surface (scaling score of 5) were used for the criteria site percentage estimate.

3.      The six to eight foot soil profile estimates the probability  of ground water vulnerability
       beneath the facility and buffer area.

4.      The higher the probability of ground  water beneath  the facility the more vulnerable the
       resource.

EPA Contacts:
Mike Bechdol (U.S. EPA Region 6, Dallas, TX, 75202), bechdol.michael@epa.gov
Tom Nelson (U.S. EPA Region 6, Dallas, TX, 75202), nelson.thomas@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                           A-20

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Ecological
Dv, Dj Criterion:
Agricultural
Lands
% of Area
< 20%
20-39%
30-39%
40-49%
> 50%
Score
1
2
O
4
5

Databases:
U.S. Geological Survey. 2000 National Land Cover Database. Compiled from Landsat satellite
TM imagery (circa 1992) with a spatial resolution of 30 meters.

Definitions, Assumptions, Limitations, Uncertainties:
1.      Agricultural lands are represented by the lands classified as Orchards/Vineyards/Other,
       Pasture/Hay, Row Crops, Small Grains, and Fallow (NLCD Codes 61 and 81-84).

2.      Percent coverage is quantitative only.  No decisions as to agricultural land quality were
       made.

       A higher percentage of agricultural land cover within an area may indicate a greater
       potential for concerns under the Prime Farmland Act.

4.      For Dj, it is assumed that farmlands are affected if they are located within the project or
       geographic boundaries.

       For Dj, the farmlands affected reflect the percentage of wetland area within the project or
       geographic boundary.

6.      This criterion may be calculated for the most appropriate geographic area and scale (e.g.,
       watershed subunits, transportation corridors, or project areas).

7.      The area of analysis may be broken into 1 km grid cells for GISST criteria computation.

EPA Contacts:
David Parrish (U.S. EPA Region 6, Dallas, TX, 75202), parrish.david@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
5.
                                          A-21

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Dv, Dj Criterion:
% of Area
< 20%
20-39%
30-39%
40-49%
> 50%
Wetlands
Score
1
2
O
4
5
Databases:
U.S. Geological Survey. 2000 National Land Cover Database. Compiled from Landsat satellite
TM imagery (circa 1992) with a spatial resolution of 30 meters.

Definitions, Assumptions, Limitations, Uncertainties:
1.      Wetlands are represented by the lands classified as Woody Wetlands (NLCD code 91)
       and Emergent Herbaceous Wetlands (NLCD Code 92).

2.      Percent coverage is quantitative only. No decisions as to wetland quality were made.

3.      The EPA will conduct a separate review with the U.S. Corps of Engineers and/or the U.S.
       Natural Resources Conservation Service, as necessary, to document compliance with
       Section 404 of the Clean Water Act.

4.      This criterion may be calculated for the most appropriate geographic area and scale (e.g.,
       watershed subunits, transportation corridors, or project areas).
5.
       The area of analysis may be broken into 1 km grid cells for GISST criteria computation.

6.      For Dj, it is assumed that wetlands are affected if they are located within the project or
       geographic boundaries.

7.      For Dj, the wetlands affected reflect the percentage of wetland area within the project or
       geographic boundary.

EPA Contacts:
David Parrish (U.S. EPA Region 6, Dallas, TX, 75202), parrish.david@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                         A-22

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Dy,^

% of Area
< 20%
20-39%
30-39%
40-49%
> 50%
Criterion: Wildlife
Habitat
Score
1
2
3
4
5
Databases:
U.S. Geological Survey. 2000 National Land Cover Database. Compiled from Landsat satellite
TM imagery (circa 1992) with a spatial resolution of 30 meters.

Definitions, Assumptions, Limitations, Uncertainties:
1.      Habitats are represented by Forest Lands, Shrublands, Grasslands, Wetlands, and open
       Water (NLCD Codes 11,41-43, 51, 71, 91-92).

2.      Percent coverage is quantitative only.  No decisions as to wildlife habitat quality were
       made.
3.      There is no association between this vulnerability score for wildlife habitats and the
       potential effect, if any, on listed Federal Endangered and Threatened Species, subject to
       the requirements of the ESA.
4.      The EPA will conduct a separate review with the U.S. Corps of Engineers and/or the U.S.
       Natural Resources Conservation Service, as necessary, to document compliance with
       Section 404 of the Clean Water Act.
5.      For Dj, it is assumed that wildlife habitat is affected if it is located within project or
       geographic boundaries.
6.      For Dj, the wildlife habitat affected reflects the percentage of habitat area within project
       or geographic boundary.
7.      This criterion may be calculated for the most appropriate geographic area and scale (e.g.,
       watershed subunits, transportation corridors, or project areas).
8.      The area of analysis may be broken into 1 km grid cells for GISST criteria computation.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202, 75202), carney.gerald@epa.gov
Sharon Osowski (U.S.EPA Region ,6, Dallas, TX, 75202), osowski.sharon@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                          A-23

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                  Dv Criterion: Wildlife Habitat Quality (Land Use Data)

                 Cumulative1 Land Use Ranking  Score
                < 1                               1
                1.1-2                             2
                2.1-3                             3
                3.1-4                             4
                >4                                5
                1 Each land use is judged as to wildlife habitat quality (1-5 scale with 5 the
                highest value). The percent of the habitat in the watershed is multiplied
                times the rank value. Values are summed. Five is the highest value possible.
Databases:
U.S. Geological Survey. 2000 National Land Cover Database. Compiled from Landsat satellite
TM imagery (circa 1992) with a spatial resolution of 30 meters.

References:
Anderson, J. 1978. A Land Use and Land Cover Classification System for Use with Remote Sensor Data,
Department of the Interior.

Definitions, Assumptions, Limitations, Uncertainties:
1.      Wildlife Habitats are given a rank score of 5 and are represented by wetlands, rangelands,
       forest lands, woodlands, open water, shrubland, herbaceous uplands.
2.      A 1 to 5 scaled ranking of habitats based on land use descriptors (NRCS, 1995 Landuse
       Data set) were determined. The ranking are: 1  = industrialized/transportation/
       commercial areas;  2 = high intensity residential; 3 = low intensity residential, urban
       recreational grasses, bare rocks, sand, and clay, transitional areas; 4 = agricultural; 5 =
       wildlife habitat defined as  rangeland, wetlands, forest lands, woodlands, herbaceous
       uplands, shrublands, open  water. The higher the rank, the more valued the habitat. The
       greater the area for each, the more weighting (e.g., if 10% of area is a 3 ranking and 90 %
       is a 5 ranking, then the weighting is calculated:  [0.1X3 + 0.9 X 5] = [0.3 + 4.5] = 4.8
       becomes the area weighted ranking. The higher the value score the more concern.
       Percent coverage is quantitative only. No decisions as to wildlife habitat quality were
       made. No association to listed Federal Endangered and Threatened Species, subject to
       the requirements of the ESA.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202, 75202),carney.gerald@epa.gov
Sharon Osowski (U.S.EPA Region 6, Dallas, TX, 75202), osowski.sharon@epa.gov

Contractor Support: Jeff Danielson (Lockheed Martin,  EPA Region 6 support),
danielson.jeff@epa.gov
                                           A-24

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PAR
0-0.1
0.2-0.3
0.4-0.5
0.6-0.8
0.9-1
Dv Criterion: Habitat
Fragmentation
Score
1
2
O
4
5
Databases:
U.S. Geological Survey. 2000 National Land Cover Database. Compiled from Landsat satellite
TM imagery (circa 1992) with a spatial resolution of 30 meters.

References:
White, M. et al. 2002. Draft Landscape Atlas of Ecosystem Health in EPA Region 5. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1 .      Perimeter to area ratio (PAR) is one measure of habitat fragmentation and landscape pattern.
2.      A perfect circle has the shortest perimeter to area, making it the most idealized condition.
3.      Area to perimeter is used here to identify the less fragmented geographic areas as an indicator of
       landscapes to be protected.  Perimeter to area calculations are used in linear project analyses to
       facilitate comparisons between alternative routes.
       The perimeter-to-area ratio has the formula: [P/(Aideal) / P/(Areal) ]  = [(2 * pi *  (a^/pi)0 Vareal) /
       (preal/a^)].
       The PAR calculated here is a relative measure and calculates how closely a real landscape
       matches with the ideal (a perfect circle).  Unity equals a perfect circle and a value of zero equals
       a patch that is long and narrow (i.e., very different from the ideal condition).
       The results of the calculation of PAR may be normalized using log base 10.
       The habitat fragmentation criteria is associated with the percent habitat in the  watershed, wildlife
       and non-wildlife areas, habitat value, and anthropological activities.
       Habitat fragmentation may cause aquatic habitat changes, animal range disruption, disruption of
       natural barriers, migration routes, dispersal patterns for plants, channelization, impacts of oil
       leaks, noise, diesel and gasoline engine emissions.
       Wildlife habitats include open water, flood plains, wetlands, bottomland hardwoods, rangelands,
       upland forests and grasslands.
       The creation of "edge" terrestrial habitats from human activities is recognized but not accounted
       for.
EPA Contacts:
Sharon Osowski (U.S. EPA Region 6, Dallas, TX, 75202), osowski.sharon@epa.gov
Mary White (U. S. EPA, Region 5, Chicago, IL), white.mary@epa.gov
4.
5.
9.
10
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                                Dj Criterion: Federally Listed
                                 Endangered and Threatened
                                           Species
                             Present in area
Score
                             No
                             Yes
Databases:
TPWD, 2002. Biological
Conservation Database (points). TPWD, Austin, TX.

EPA, 2003. Texas Interstate 69 Baseline Analysis Grid. EPA, Region 6, Dallas,
TX.

References:
U.S. Department of Interior. 1973. Endangered Species Act. US Fish and
Wildlife Service, Washington DC (as amended).

U. S. EPA. 1970. "Implementation Regulations for the National Environmental
Policy Act", Washington, DC.

Definitions, Assumptions, Limitations, Uncertainties:
1.      Those elemental occurrences of species that have a federal status of Endangered or
       Threatened.
2.      Areas subject to the requirements of the Endangered Species Act. Consultation with U.S.
       FWS is indicated.

3.      This criterion may be calculated for the most appropriate geographic area and scale (e.g.,
       watershed subunits, transportation corridors, or project areas).

4.      The area of analysis may be broken into 1 km grid cells for GISST criteria computation.
EPA Contacts:
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
David Parrish (U.S. EPA Region 6, Dallas, TX, 75202), parrish.david@epa.gov
Dominique Lueckenhoff (U.S. EPA Region 6, Austin, TX), lueckenhoff.dominique@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
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                                  Dj Criterion: State Listed
                                 Endangered and Threatened
                                           Species
                             Present in area
                             No
                             Yes
Score
Databases:
TPWD, 2002. Biological Conservation Database (points). TPWD, Austin, TX.

EPA, 2003. Texas Interstate 69 Baseline Analysis Grid. EPA, Region 6, Dallas,
TX.

Definitions, Assumptions, Limitations, Uncertainties:
1.      Those elemental occurrences of species that have a State status of Endangered or
       Threatened.

2.      Areas subject to the requirements state requirements protecting endangered and
       threatened species. Consultation with the State wildlife department is indicated.

3.      This criterion may be calculated for the most appropriate geographic area and scale (e.g..
       watershed subunits, transportation corridors, or project areas).

4.      The area of analysis may be broken into 1 km grid cells for GISST criteria computation.
EPA Contacts:
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
David Parrish (U.S. EPA Region 6, Dallas, TX, 75202), parrish.david@epa.gov
Dominique Lueckenhoff (U.S. EPA Region 6, Austin, TX), lueckenhoff.dominique@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support),  danielson.jeff@epa.gov
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                              Dj Criterion: Endangered Species
                                      Act Compliance

                             Section 7 Compliance1     Score
                            Yes
                            No
1
5
                             Section 7 of Endangered Species Act of 1977
Databases:
Information supplied by facility.

References:
U.S. Department of Interior. 1977. Endangered Species Act. US Fish and Wildlife Service, Washington DC.

U. S. EPA.  1970.  Implementation Regulations for the National Environmental Policy Act, Washington, DC.
[incomplete citation]

Definitions, Assumptions, Limitations, Uncertainties:
1.     Federal non-compliance constitutes potential significant adverse impacts on listed
       endangered and threatened species.

2.     Section 7 decision based on consultation with and advice of the US Fish and Wildlife
       Service.

EPA Contacts:
Joe Swick (U.S. EPA Region 6, Dallas, TX, 75202), swick.joseph@epa.gov
Sharon Osowski (U.S.EPA Region 6, Dallas, TX, 75202), osowski.sharon@epa.gov
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                                Dv Criterion: Ecologically
                               Significant Stream Segments

                              Presence in area   Score
                              No                   1
                              Yes                  5
Databases:
TPWD, 2000-2001. Ecologically Significant Stream Segments, TPWD, Austin,
TX.

EPA, 2003. Texas Interstate 69 Baseline Analysis Grid. EPA, Region 6, Dallas,
TX.

Definitions, Assumptions, Limitations, Uncertainties:
1.      In accordance with the TWDB's rules, the following criteria are to be used when recommending
       a river or stream segment as being of unique ecological value:
       •      Biological Function: Segments which display significant overall habitat value including
              both quantity and quality considering the degree of biodiversity, age, and uniqueness
              observed and including terrestrial, wetland, aquatic, or estuarine habitats;
       •      Hydrologic Function: Segments which are fringed by habitats that perform valuable
              hydrologic functions relating to water quality, flood attenuation, flow stabilization, or
              groundwater recharge and discharge;
       •      Riparian Conservation Areas: Segments which are fringed by significant areas in public
              ownership including state and federal refuges, wildlife management areas, preserves,
              parks, mitigation areas, or other areas held by governmental organizations for
              conservation purposes under a governmentally approved conservation plan;
       •      High Water Quality/Exceptional Aquatic Life/High Aesthetic Value: Segments and
              spring resources that are significant due to unique or critical habitats and exceptional
              aquatic life uses dependent on or associated with high water quality;
       •      Threatened or Endangered Species/Unique Communities: Sites along segments where
              water development projects would have significant detrimental effects on state or
              federally listed threatened and endangered species, and sites along segments that are
              significant due to the presence of unique, exemplary, or unusually extensive natural
              communities.

EPA Contacts:
David Parrish (U.S. EPA Region 6, Dallas, TX, 75202), parrish.david@epa.gov
Dominique Lueckenhoff (U.S. EPA Region 6, Austin, TX), lueckenhoff.dominique@epa.gov

Contractor Support: Jeff Danielson (Lockheed Martin, EPA Region 6 support),
danielson.jeff@epa.gov
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DRAFT Dv Criterion: TEAP
Percent in 1km2
lowest 5 1-1 00%
26-50%
11-25%
2-10%
top 1% most diverse polygons
Diversity
Score
1
2
3
4

Databases:
USGS. 2000. Texas National Land Cover Data Set (circa 1992),
http://landcover.usgs.gov/natllandcover.asp.
TPWD. 1995. Ecological Stream Segments of Concern
Fire Sciences Laboratory, Rocky Mountain Research Station, 2001, Kuchler's Potential Natural
Vegetation Groups, Version 2000, Missoula, MT.

References:
Osowski, S. L., J. E. Danielson, S. Schwelling, D. German, S. Gilbert, D. Lueckenhoff, D. Parrish, A. K. Ludekeand
J. Bergan. 2004. Texas Environmental Resource Stewards (TERS) Texas Ecological Assessment Protocol (TEAP)
Results, Pilot Project Report. Report Number EPA-906-C-05-001. US Environmental Protection Agency Region 6,
Dallas, TX.

Ktichler, A. W. 1975. Potential natural vegetation of the conterminous United States. 2d ed. Map 1:3,168,000.
American Geographical Society.

Definitions, Assumptions, Limitations, Uncertainties:
1.      Because the TEAP was calculated using a 1km2 grid developed by Texas Parks and Wildlife
       Department, the scores for this criteria may be up to 0.5 km2 off from the original 1km2 grid
       developed by EPA Region 6 for the GISST calculation for IH69.
2.      The diversity layer consists of four sub-layers: appropriateness of land cover, contiguous size of
       undeveloped area, Shannon land cover diversity, and ecologically significant stream segments.
3.      The overall diversity layer was calculated by taking the mean of the four diversity sub-layers and
       rescaling on a 0-100 scale. Higher scores indicate a higher level of diversity. The values of the 30
       m pixels that made up each 1 km2 (one kilometer square) grid cell were averaged to determine
       the Diversity Index score for each cell.
4.      A US EPA program, ATTiLA was used to calculate Shannon land cover diversity.
5.      Further details on TEAP calculations can be found in the TEAP Results Report.

EPA Contacts:
Sharon Osowski (U.S. EPA Region 6 Dallas,  TX, 75202), osowski.sharon@epa.gov
David Parrish (U.S. EPA Region 6, Dallas, TX, 75202), parrish.david@epa.gov
Contractor Support: Jeff Danielson (Lockheed Martin, EPA Region 6 support),
danielson.jeff@epa.gov
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                           DRAFT  Dv Criterion: TEAP Rarity

                        Percent in 1km2	Score
                        lowest 51-100%
                        26-50%
                        11-25%
                        2-10%
                        top 1% rarity
                                                     1
                                                     2
                                                     3
                                                     4
                                                     5
Databases:
USGS, 2000, Texas National Land Cover Data Set, http://landcover.usgs.gov/natllandcover.asp.
TPWD TXBCD & Natural Heritage data

References:
Osowski, S. L., J. E. Danielson, S. Schwelling, D. German, S. Gilbert, D. Lueckenhoff, D. Parrish, A. K. Ludekeand
J. Bergan. 2004. Texas Environmental Resource Stewards (TERS) Texas Ecological Assessment Protocol (TEAP)
Results, Pilot Project Report. Report Number EPA-906-C-05-001. US Environmental Protection Agency Region 6,
Dallas, TX.

Definitions, Assumptions, Limitations, Uncertainties:
1.
2.
3.
4.
Because the TEAP was calculated using a 1km2 grid developed by Texas Parks and Wildlife
Department, the scores for this criteria may be up to 0.5 km2 off from the original 1km2 grid
developed by EPA Region 6 for the GISST calculation for IH69.
The rarity layer consists of four sub-layers: vegetation rarity, natural heritage rank, taxonomic
richness, and rare species richness.
The overall rarity layer was calculated by taking the mean of the four Rarity layer sub-layers and
rescaling on a 0-100 scale. Higher scores indicate a higher level of rarity. The values of the 30 m
pixels that made up each 1 km2 grid cell were averaged to determine the Rarity Index score for
each cell.  Overall rarity was calculated by receding rarity ranks using an exponential growth
function 0-250  to produce a statewide land cover rarity data set. Data were scaled 0-250, due to
machine processing of 8-bit data. Because the input data sets for Texas were large, rescaling the
data from 1-250 (8-bit) allowed for much faster machine processing without any significant loss
of granularity.  Exponential scaling was chosen to give appropriate weight to rarer features. The
statewide land cover rarity data set and the land cover rarity by ecoregion data set were input into
an averaging model to compute the mean value of each grid cell for the combined data sets.
Further details on TEAP calculations can be found in the TEAP Results Report.
EPA Contacts:
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
David Parrish (U.S. EPA Region 6, Dallas, TX, 75202), parrish.david@epa.gov
Contractor Support: Jeff Danielson (Lockheed Martin, EPA Region 6 support),
danielson.jeff@epa.gov
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DRAFT Dv Criterion: TEAP
Sustainability
Percent in 1km2
lowest 5 1-1 00%
26-50%
11-25%
2-10%
top 1% most sustainable polygons

Score
1
2
3
4
5
Databases:
USGS, 2000, Texas National Land Cover Data Set, http://landcover.usgs.gov/natllandcover.asp.
Fire Sciences Laboratory, Rocky Mountain Research Station, 2001, Kuchler's Potential Natural
Vegetation Groups, Version 2000, Missoula, MT.
U.S. Bureau of the Census, 2000, TIGER/Line Files. Census Bureau, Washington, D.C.
U.S. EPA, 2003, National Priority List Database. EPA Region 6, Dallas, TX.
TCEQ, 2003, State Superfund Sites. Austin, TX.
U.S. EPA, 2003, RCRA TSD database. EPA Region 6, Dallas, TX.
U.S. EPA, 2003, Corrective Action database. EPA Region 6, Dallas, TX.
TCEQ, 2003, Voluntary Cleanup Program database. TCEQ, Austin, TX.
Bureau of Transporation Statistics, 2002, U.S. Airport Database. BTS, Washington, D.C.
U.S. EPA, 2003, Ozone Nonattainment Areas. EPA Region 6, Dallas, TX
TCEQ, 2003, State Near Nonattainment Areas. TCEQ, Austin, TX.
TCEQ, 2002, Dam Dataset. TCEQ, Austin, TX.
TCEQ, 2000, 303d Stream  Segments of Concern.  TCEQ, Austin, TX.

References:
Osowski, S. L., J. E. Danielson, S. Schwelling, D. German, S. Gilbert, D. Lueckenhoff, D. Parrish, A. K. Ludekeand
J. Bergan.  2004. Texas Environmental Resource Stewards (TERS) Texas Ecological Assessment Protocol (TEAP)
Results, Pilot Project Report.  Report Number EPA-906-C-05-001. US Environmental Protection Agency Region 6,
Dallas, TX.

Definitions, Assumptions, Limitations, Uncertainties:
1.      The sustainability layer describes the state of the environment  in terms of stability, that is,
       how resistant to disturbance an area is, and how capable is the area in returning to its pre-
       disturbance state, that is, resilience (Begon et al. 1986).  Sustainable areas are those that
       can maintain themselves into the future without human management.
2.      Because the TEAP was calculated using a 1km2 grid developed by Texas Parks and
       Wildlife Department, the scores for this criteria may be up to 0.5 km2 off from the
       original 1km2 grid developed by EPA Region 6 for the GISST calculation for IH69.
3.      The sustainability layer consists of eleven measures that can be loosely grouped into
       fragmentors: contiguous land cover type, regularity of ecosystem boundary,
                                         A-32

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       appropriateness of land cover, waterway obstruction, road density and stressors: airport
       noise, Superfund National Priority List and State Superfund Sites, water quality, air
       quality,  RCRA, Treatment-Storage-Disposal sites, Corrective Action and State Voluntary
       Cleanup Program Sites, and urban/agricultural disturbance.
4.      The overall sustainability layer was calculated by taking the mean of the eleven sub-layers
       and reseating on a 0-100 scale.  Higher scores indicate a higher level of sustainability.
       The values of the 30 m pixels that made up each 1 km2 (one kilometer square) grid cell
       were averaged to determine the Sustainability Index score for each cell.
5.      Further details on TEAP calculations can be found in the TEAP Results Draft Report.

EPA Contacts:
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
David Parrish (U.S. EPA Region 6, Dallas, TX, 75202), parrish.david@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
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              DRAFT Dv Criterion: TEAP Composite/Ecological
                                 Importance
          Percent in 1km2
                                                       Score
          lowest ecological importance (lowest 51-100% of scores)   1
          26-50%                                               2
          11-25%                                               3
          2-10%                                                4
          top 1% most ecologically important location              5
Databases:
Diversity, Rarity, and Sustainability Data created in TEAP

References:
Osowski, S. L., J. E. Danielson, S. Schwelling, D. German, S. Gilbert, D. Lueckenhoff, D. Parrish, A. K. Ludekeand
J. Bergan. 2004. Texas Environmental Resource Stewards (TERS) Texas Ecological Assessment Protocol (TEAP)
Results, Pilot Project Report.  Report Number EPA-906-C-05-001. US Environmental Protection Agency Region 6,
Dallas, TX.

Definitions, Assumptions, Limitations, Uncertainties:
1.     The composite layer is composed of the three main layers: Diversity, Rarity, and
       Sustainability calculated for TEAP.
2.
Because the TEAP was calculated using a 1km2 grid developed by Texas Parks and
Wildlife Department, the scores for this criteria may be up to 0.5 km2 off from the
original 1km2 grid developed by EPA Region 6 for the GISST calculation for IH69.

The composite layer was calculated by taking the sum of the three main layers and
reseating on a 0-300 scale. Higher scores indicate a higher level of ecological importance.
The values of the 30 m pixels that made up each 1 km2 (one kilometer square) grid cell
were averaged to determine the score for each cell.

Further details on TEAP calculations can be found in the TEAP Results Draft Report.
EPA Contacts:
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
David Parrish (U.S. EPA Region 6, Dallas, TX, 75202), parrish.david@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
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                               Dv Criterion: Road Density

                            Road density (mi/mi2)     Score
                            <1.2                      1
                            1.2-1.8                     2
                            1.9-2.2                     3
                            2.3-2.5                     4
                            > 2.6 mi./sq.mi. watershed   5
Databases:
U.S. Census Bureau, 2001. TIGER/Line Files, Census 2000. Washington, D.C.

U.S. Geological Surey, 2000, National Hydrography Dataset, Reston, VA.

Definitions, Assumptions, Limitations, Uncertainties:
5.      There can be many more roads in a defined geographic area than those documented in the
       1992 Census information.
6.      An increased relative number of roads in a defined geographic area and associated traffic
       is an indicator of air, land, and water pollution (inorganics and hydrocarbons), human
       health, ecological, and economic concerns to include (noise, urbanization,
       industrialization, increased probability of traffic accidents, habitat fragmentation,
       ecological stress, wetland destruction).
7.      Traffic capacity for roads are not considered. A residential street and a paved rural road
       have approximately the same methodology weighting as a six lane freeway.  This
       limitation has more impact in rural areas (may have a relatively short but very busy
       highway through the watershed).
8.      All roads in TIGER are equal in significance (two lane, four lane, rural, urban).
9.      Number of bridges, overpasses, road grade, terrain, or landuse information is not
       considered in this criteria.
10.    All roads contribute to restricted wildlife movement, habitat fragmentation, nutrient
       loading and increased stream sedimentation, and unnatural water runoff (contaminated
       and non contaminated waters).
11.    The "defined geographic area" can be watershed subunits, transportation corridors,
       project areas, etc.
12.    The area of analysis may be broken into 1 km grid cells for GISST criteria computation.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Tom Nelson (U.S. EPA Region 6, Dallas, TX, 75202), nelson.thomas@epa.gov
Angel Kosfiszer (U.S.  EPA Region 6, Dallas,  TX  75202), kosfiszer.angel@epa.gov
Mike Bechdol (U.S. EPA Region 6, Dallas, TX, 75202), bechdol.michael@epa.gov
Contractor Support:  JeffDanielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
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                   Dv Criterion: Watershed/Geographic Area l
                   Watershed or Geographic Area
Score
                   <  5% of the geographic area is occupied by facilities      1
                   5-9%                                                  2
                   10-14
                   > 15%                                                 4

                   1 [ZA, / Aws] is the ratio of the cumulative area occupied by the facility
                   (Z A,) to the area of the watershed or geographic area (Aws). Multiplied by
                   100 is the percent of the geographic area impacted.
Databases:
Facility
boundary data submitted by facility (received upon request or taken from EPA RCRA, NPDES,
NEPA, or other regulatory files).

Steeves, P. and D. Nebert. 1994. Hydrologic Unit Maps of the Conterminous U.S.,U.S.
Geological Survey., Reston, VA.

References:
U.S. EPA. 1992. A Synoptic Approach to Cumulative Impact Assessment: A Proposed Methodology. EPA/600/R-
92/167. Office of Research and Development. U. S. Environmental Protection Agency. Washington, D.C.

US EPA Website. Surf Your Watershed/ IWI1995/6 305(b).http://www.epa.gov/surf/iwi

Definitions, Assumptions, Limitations, Uncertainties:
1.      One  square mile = 27,878,400 sq.ft.

2.      The potential for negative environmental impact increases as the percentage of watershed
       subunits (HUC) or other geographic area occupied by facilities increases.

3.      Potential cumulative impacts can be measured by assessing the additive activities of
       regulated and non regulated industries.  These activities include amount of land and water
       occupied by these industries. Facilities include, defense facilities, agriculture operations,
       municipal works, private industry, state and local government operations.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX,75202), carney.gerald@epa.gov
Joe Swick (U.S. EPA Region 6, Dallas, TX, 75202), swick.joseph@epa.gov
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                                   Dj Criterion: Density of
                                      Managed Lands

                               Presence in project area    Score
                               No                        1
                               Yes                        5
Databases:
EPA, 2003. Consolidated Managed Land for Texas. EPA, Region 6, Dallas, TX.

Definitions, Assumptions, Limitations, Uncertainties:
1.      Managed lands include National Park Service Lands, National Forest Service Lands, U. S.
       Fish & Wildlife Service Lands, State Parks and Wildlife Areas, City Parks, County Parks,
       and other lands used for conservation/recreation. Managed lands also may include other
       large properties owned/managed by the Federal Government such as Military Bases,
       BLM Lands, and DOE Lands.
2.      The more managed lands in a project area, the greater the potential for negative impacts.
3.      Five mile radius is used to be comparable with other Region 6 risk index analyses (e.g.
       Human Health Risk Index, Environmental Justice Index).
4.      The majority of managed lands are assumed to be in the same watershed, but there is the
       possibility that managed lands can be in different HUCs.
5.      Locations of managed lands may be used to avoid or minimize impacts, as well as for
       resource enhancement and compensation issues.
6.      This criterion may be calculated for the most appropriate geographic area and scale (e.g.,
       watershed subunits, transportation corridors, or project areas).
7.      The area of analysis may be broken into 1 km grid cells for GISST criteria computation.

EPA Contacts:
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
David Parrish (U.S. EPA Region 6, Dallas, TX, 75202), parrish.david@epa.gov
Dominique Lueckenhoff (U.S. EPA Region 6, Austin, TX), lueckenhoff.dominique@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
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Air Quality
                               Dv Criterion: Air Quality

                      Distance from nonattainment area1  Score
                      > 10 miles
                      6-10 miles
                      2-5 miles
                      < 2 miles
                      0 miles
1
2
3
4
5
                        For any of the criteria air pollutants: ozone, lead,
                      particulates, CO, SOx, NOx.
Databases:
U. S. EPA. 2003. Ozone nonattainment GIS layer created from Ozone Nonattainment
Greenbook.

References:
CFR Part 81 Clean Air Act.

U. S. EPA. Ozone Nonattainment Greenbook. www.epa.gov/oar/oaqps/greenbk

Definitions, Assumptions, Limitations, Uncertainties:
1.     For any of the criteria air pollutants; ozone, lead, particulates, CO, SOX, NOX.

2.     Air nonattainment areas were used to calculate this score.

3.     Not stratified by pollutant since lead, CO, and SOX, are not high concerns in Region 6.

EPA Contacts:
Mark Sather (U.S. EPA Region 6 Dallas, TX, 75202), sather.mark@epa.gov
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov

Contractor  Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
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                             Dv Criterion: Ozone Nonattainment

                          Project Location	Score
                          outside nonattinment area
                          near nonattainment area
                          inside nonattainment area
Databases:
Census, 2001. TIGER Counties. Coverage by EPA, Region 6, Dallas, TX.

TCEQ, 1998. Nonattainment Areas. TCEQ, Austin, TX.

References:
CFR Part 81 Clean Air Act. Http://www.epa.gov/airs/nonattn.html

U.S. EPA, 2002. Green Book - Nonattainment Areas for Criteria Pollutants.
www.epa.gov/oar/oaqps/greenbk

Definitions, Assumptions, Limitations, Uncertainties:
1.      Nonattainment Areas (from EPA Green Book)-These are designations of 1 hour ozone
       nonattainment areas. It reflects the current status of 1-hour nonattainment areas and does
       not reflect the 8-hour standard. EPA has not yet designated areas for the 8-hour standard.

2.      Near Nonattainment Areas (from TCEQ metadata)-Near nonattainment means an area is
       very close to falling into non compliance with the NAAQS. These counties have been
       designated by the TCEQ Office of Policy and Regulatory Development for planning
       reasons. These counties either have an ozone monitor or are part of a MSA that has an
       ozone monitor. It is very uncertain at this point which near nonattainment counties, if any,
       will ultimately be designated by the EPA as nonattainment.

EPA Contacts:
Dominique Lueckenhoff (U.S. EPA Region 6, Austin, TX), lueckenhoff.dominique@epa.gov
David Parrish,  (U.S. EPA Region 6, Dallas, TX 75202), parrish.david@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
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                             Dj Criterion: TRI1 Reported Air Releases2
                            Ibs released to air
Score
                            < 300,000
                            299,999- 1,000,000
                            1,000,001-2,000,000
                            2,000,001-5,000,000
                            > 5,000,000
  1
  2
  3
  4
  5
                             2000 Toxic Release Inventory Data
                             Fugitive and stack emissions (annual estimate data)
Databases:
U. S. Environmental Protection Agency.  2000.  Toxic Release Inventory.  TRI Data: SARA
Community Right-to-know. Washington, D.C. [updated annually]

References:
U. S. Environmental Protection Agency. 1989. Toxic Chemical Release Inventory Risk Screening Guide Volumes 1
and 2. US EPA Office of Pesticides and Toxic Substances. EPA 560/2-89-002.

Definitions, Assumptions, Limitations, Uncertainties:
3.     Information supplied by facility. US EPA requires manufacturing industries to estimate
       their annual releases of specific hazardous chemicals to air.  The releases are reported in
       pounds per year.
4.     Air releases are from stack and fugitive emissions.
5.     Chemical releases to air can have a negative impact upon the environment and human
       health around the release point.
6.     Chemical releases occur over a one year time period and not as a one time event.
7.     TRI releases are estimates.
8.     TRI releases may not represent all the industrial chemical releases to air.
9.     TRI has added new chemicals over the years and the industries included may have
       changed.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6, Dallas, TX, 75202), carney.gerald@epa.gov
Mort Wakeland (U.S. EPA Region 6, Dallas, TX, 75202), wakeland.morton@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
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Socioeconomic
Dv Criterion: Colonias 1
Total Number of Colonias/countv Score
0
<25
26-50
51-125
> 125
(Texas and New Mexico only)
1
2
3
4
5

Databases:
Colonias dataset, NM made from New Mexico State University, TX made from Texas Water
Development Board.

References:
U.S. EPA, Region 6, March 2000. Environmental Education Targeting Study: Border Report, Analysis of Counties
Within the US/Mexico 100 Km Border Buffer, Gerald Carney, Office of Planning and Coordination, Dallas, TX
75202. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.      Colonias are typically unincorporated residential areas where municipal services are lacking
       (garbage disposal, sewage disposal, drinking water plumbing to home.
2.      Texas Water Development Board defined colonias are locations in the Economically Distressed
       Area Program.
3.      These data are point locations which are applied to county,  watershed, or other boundary.
4.      Data is used for Texas and New Mexico at this time. Other Region 6 states have areas which
       would meet the Texas or New Mexico "colonias" definition (other than location along the
       U.S./Border).
5.      It is assumed that colonia unincorporated communities have self maintained and dug well
       systems, surface or other source of water for drinking, cooking, bathing, and cleaning. It is also
       assumed that septic tanks, cesspools, or other sewage disposal system is used as well as other
       than public means of garbage disposal.
6.      It is assumed that wells and surface water are more easily contaminated than public systems, that
       septic tank and cesspools have a higher failure rate than public sewage  systems, and with
       individual resident garbage locations are breeding areas for disease vectors.
7.      Colonias by definition are residential areas. The lack of public services in these populated areas
       increases the chance of environmental contamination and resulting disease.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX), carney.gerald@epa.gov

Contractor Support: Jeff Danielson (Lockheed Martin, EPA Region 6 support),
danielson.jeff@epa.gov
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Dv Criterion: High School Education
% population without High School Diploma
< the State average
State avg-1.33 x State avg
1.34 x State avg- 1.66 x State avg
1.67 x State avg-2 x State avg
> 2 x State avg
Score
1
2
O
4
5
Databases:
Census 2000 Summary File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau,
2002.

References:
U.S. EPA, Region 6, March 2000. Environmental Education Targeting Study: Border Report, Analysis of Counties
Within the US/Mexico 100 Km Border Buffer, Gerald Carney, Office of Planning and Coordination, Dallas, TX
75202. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.      Education data is captured by block group in the census.
2.      Assessments by watershed or other geographic area use the "area weighting" technique
       for block groups bisected by geographic boundaries.
3.      It is assumed that the higher the education level of a population the better prepared that
       community is for environmental understanding, danger avoidance, and responsible
       actions leading to environmental protection.
4.      It is assumed that the higher the education level the more able a population is to protect
       its members from harmful  exposures and to  discover and articulate community concerns.
5.      It is assumed that watershed boundaries are as appropriate or better than other boundaries
       (county lines, city limits) to evaluate environmentally based issues.
6.      The education criteria should not be used alone but should be considered with other
       socioeconomic criteria (income, age, population density, language barriers).
7.      This criterion may be calculated for the most appropriate geographic area and  scale (e.g.,
       watershed subunits, transportation corridors, or project areas).
8.      The area of analysis may be broken into 1 km grid cells for GISST criteria computation.

EPA Contacts:
Gerald Carney (U.S. EPA Region  6 Dallas, TX), carney.gerald@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin,  EPA Region 6 support), danielson.jeff@epa.gov
                                          A-42

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Dv Criterion: Educational Achievement Ranking
Cumulative Score for Education Achievement1 Score
College Degree
Some College (No Degree)
High School Degree (or GED)
9th to 12th Grade (No Degree)
< 9th Grade
1
2
3
4
5
Databases:
Census 2000 Summary File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau,
2002.

References:
U.S. EPA, Region 6, March 2000. Environmental Education Targeting Study: Border Report, Analysis of Counties
Within the US/Mexico 100 Km Border Buffer, Gerald Carney, Office of Planning and Coordination, Dallas, TX
75202. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.      Education data is captured by block group in the census.
2.      The educational attainment score is calculated by multiplying a weighted factor for each
       educational level by the percentage of persons at that level, then summing the level
       scores to arrive at a single score of 1 through 5. [{(% with College Degree) /100} * 1] +
       [{(% Some College) /100} * 2] + [{(% High School Degree) /100} * 3] + [{(% 9-12
       grade) /100} * 4] +[{(% < 9 grade) /100} * 5].
3.      Assessments by watershed or other geographic area use the "area weighting" technique
       for block groups bisected by geographic boundaries.
4.      It is assumed that the higher the education level of a population the better prepared that
       community is for environmental understanding, danger avoidance, and responsible
       actions leading to environmental protection.
5.      It is assumed that the higher the education level the more able a population is to protect
       its members from harmful exposures and to discover and articulate community concerns.
6.      It is assumed that geographic boundaries are as appropriate or better than other
       boundaries (county lines, city limits) to evaluate environmentally based issues.
7.      The education criteria should not be used alone but should be considered with other
       socioeconomic criteria (income, age, population density, language barriers).

EPA Contacts: Gerald Carney (U.S. EPA Region 6 Dallas, TX), carney.gerald@epa.gov
Contractor Support: Jeff Danielson (Lockheed Martin, EPA Region 6 support),
danielson.jeff@epa.gov
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Dv Criterion: Economic (Environmental
Justice)
% Economically stressed Score
< State avg
State avg- 1.3 3 x State avg
1.34 x State avg-1.66x State avg
1.67 x State avg- 1.99 times the State avg
> 2 x State avg
1
2
3
4
5
Databases:
Census 2000
Summary File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau, 2002.

References:
Lavelle, M., and M. Coyle. 1992. Unequal Protection: The Racial Divide in Environmental Law. National Law
Journal 15:2-12.

U.S. EPA.  1992.  Environmental Equity: Reducing Risk for All Communities. EPA230-R-92-008. Office of Policy,
Planning, and Evaluation (PM-221), U. S. Environmental Protection Agency, Washington, D.C.

U.S. EPA.  1995.  Computer Assisted Environmental Justice Index Methodology. Office of Planning and Analysis,
Enforcement Division, Region 6 EPA, Dallas, TX. [unpublished]

U.S. EPA.  1994.  Executive Order 12898: Federal Actions to Address Environmental Justice inMinority
Populations and Low-Income Populations.  59 Federal Register Notice 7629 (1994).

Definitions, Assumptions, Limitations, Uncertainties:
1.     Demographic data will be 2000 SF3 Census coverage.
2.     The economic analysis calculated for a four mile radius (minimum 50 square miles) from
       the boundary of the facility.
3.     It is assumed that there are different social-economic factors specific to each Region 6
       state which justifies using state averages for comparisons. Factors include: availability of
       insurance and health care benefits for residents, education opportunities, public
       transportation systems, infrastructure stress related to language differences, state income
       tax, ethnic differences, employment rate and stability of industrial - business base,
       housing and utility costs, use of land, presence of rural and urban areas, availability  of
       natural resources.
4.     Economically stressed households are those that earn an income of < $15,000 for the
        1990 Census and < $20,000 for the 2000 Census.

EPA Contacts:
Gerald Carney  (U.S. EPA Region 6 Dallas, TX,75202), carney.gerald@epa.gov
Contractor Support: Jeff Danielson (Lockheed Martin, EPA Region 6 support),
danielson.jeff@epa.gov
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                        Dv Criterion:  Minority (Environmental Justice)

                 % minority	Score
                 < State avg
                 State avg-1.3 3 x State avg
                 1.34 x State avg-1.66 x State avg
                 1.67 x State avg-1.99 x the State avg
                 > 2 x State avg
1
2
3
4
5
Databases:
Census 2000
Redistricting Data (Public Law 94-171) Summary File - (AR, LA, NM, OK, TX) [machine-
readable data files] / prepared by the U.S. Census Bureau, 2001.

References:
U.S. EPA.  1995.  Computer Assisted Environmental Justice Index Methodology. Office of Planning and Analysis,
Enforcement Division, Region 6 EPA, Dallas, TX. [unpublished]

U.S. EPA. 1994. Executive Order 12898: "Federal Actions to Address Environmental Justice in Minority
Populations and Low-Income Populations". 59 Federal Register Notice 7629 (1994).

U.S. EPA. 1992. Environmental Equity: Reducing Risk for All Communities. Office of Policy, Planning, and
Evaluation (PM-221), EPA230-R-92-008, Washington, D.C.

Lavelle, M., and M. Coyle. 1992. Unequal Protection: The Racial Divide in Environmental Law. National Law
Journal 15:2-12.
Definitions, Assumptions, Limitations, Uncertainties:
1.     Demographic data will be 2000 PL94-171 Census coverage.
2.     The minority analysis calculated for a four mile radius (minimum 50 square miles) from
       the boundary of the facility.
3.     It is assumed that there are different social-economic factors specific to each Region 6
       state which justifies using state averages for comparisons.  Factors include: availability of
       insurance and health care benefits for residents, education opportunities, public
       transportation systems, infrastructure stress related to language differences, state income
       tax, ethnic differences, employment rate and stability of industrial - business base,
       housing and utility costs, use of land, presence of rural and urban areas, availability of
       natural resources.
4.     In New Mexico, the minority population makes up the majority of the residents in that
       state.  Therefore, a score of "5" is statistically not possible.

EPA Contacts: Gerald Carney (U.S. EPA Region 6 Dallas, TX,75202), carney.gerald@epa.gov
Contractor Support: Jeff Danielson (Lockheed Martin, EPA Region 6 support),
danielson.jeff@epa.gov
                                            A-45

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                           Dv Criterion: Age 1 (7 > Age > 55 Years old)
                          % 7 > age >55 vrs old	
                          < State average
                          State average-1.3 3 x State avg
State average-1.3 3 x State avg
1.34 x State avg-1.66 x State avg
1.67 x State avg-2 x State avg
      itdtf^ Q\/rr
                           1.67x 	  ,
                           > 2 x State avg
Score
   1
   2
   3
   4
   5
                           Vulnerable ages are assumed to be < 7 y/o and > 55 y/o
Databases:
Census 2000 Summary File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau,
2002.

References:
U.S. EPA.  1995. Computer Assisted Environmental Justice Index Methodology. Office of Planning and Analysis,
Enforcement Division, Region 6 EPA, Dallas, TX. [unpublished]

U.S. EPA. March 1999. Region 6 Program Plan for Protecting Children from Environmental Health Risks and
President Clinton's Executive Order 13045, April 21. 1997, to Protect Children from Environmental Health Risks
and Safety Risks, [unpublished]

Bearer, Cynthia F. September 1995. "Environmental Health Hazards: How Children are Different From Adults", in
Environmental Health Perspectives, Vol. 103, Supplement 6. [incomplete citation]

Goldman, Lynn R. and Sudha Koduru. June 2000. "Chemicals in the Environment and Developmental Toxicity to
Children: A Public Health and Policy Perspective" in Environmental Health Perspectives, Vol. 108, Supplement
3.[incomplete citation]

Definitions, Assumptions, Limitations, Uncertainties:
1.       The definition of children being (< 7 years old) is partly based upon organ development,
        education, and physical size considerations.
2.       Age 55 representing "older" individuals is partly based upon organ reserve, physical
        ability, cumulative health risk considerations (i.e., consequences of smoking, diet, life
        style, occupational exposures,and other factors).
3.       Demographic data will be 2000 SF3 Census coverage, changing to year 2000 data the
        summer of 2001.
4.       The "Age" analysis is a comparison to the state average and can be calculated for many
        different areas (block groups, tracts, counties, of radii around  a point location).  Region 6
        EPA enforcement, education and  health risk targeting demographic evaluations (i.e., age,
        income, ethnicity, education) are often for 0.56 and 4 mile radii.
5.       Children and older individuals are more susceptible to environmental health risks.
6.       Children and the older population are not susceptible to the same environmental
        pollutants  or conditions (i.e., ultra-violet light, carbon monoxide) or have the same
                                            A-46

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       reactions to environmental stressors (i.e., asthma, cardio-respiratory disease).
7.      It is assumed that there are different social-economic factors specific to each Region 6
       state which justifies using state averages for comparisons. Factors include: availability of
       insurance and health care benefits for residents, education opportunities, public
       transportation systems, infrastructure stress related to language differences, state income
       tax, ethnic differences, employment rate and stability of industrial - business base,
       housing and utility costs, use of land, presence of rural and urban areas, availability of
       natural resources.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX,75202), carney.gerald@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                           A-47

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                      Dv Criterion: Children l (population < 7 Years
                                            old)
                    %< 7 vrs old
Score
                    < State average                             1
                    State average-1.3 3 x State avg               2
                    1.3 4 x State avg-1.66 x State avg            3
                    1.67 x State avg- 2 x State avg               4
                    > 2 x State avg                             5
                      Vulnerable age for children is assumed to be < 7 y/o
Databases:
Census 2000 Summary File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau,
2002.

References:
U.S. EPA.  1995. Computer Assisted Environmental Justice Index Methodology. Office of Planning and Analysis,
Enforcement Division, Region 6 EPA, Dallas, TX. [unpublished]

U.S. EPA. March 1999. Region 6 Program Plan for Protecting Children from Environmental Health Risks and
President Clinton's Executive Order 13045, April 21. 1997, to Protect Children from Environmental Health Risks
and Safety Risks, [unpublished]

Bearer, Cynthia F. September 1995. "Environmental Health Hazards: How Children are Different From Adults", in
Environmental Health Perspectives, Vol. 103, Supplement 6. [incomplete citation]

Goldman, Lynn R. and Sudha Koduru. June 2000. "Chemicals in the Environment and Developmental Toxicitv to
Children: A Public Health and Policy Perspective" in Environmental Health Perspectives, Vol. 108, Supplement 3.
[incomplete citation]

Vaughan, V., editor. 1975. Nelson Textbook of Pediatrics, 10th edition. W.B. Saunders Company, Philadelphia, PA.

Definitions, Assumptions, Limitations, Uncertainties:
1.       The definition of children being (< 7 years old) is partly based upon organ development,
        education, and physical size considerations.
2.       Demographic data will be 2000 SF3 Census coverage, changing to year 2000 data the
        summer of 2001.
3.       The "Age" analysis is a comparison to the state average and can be calculated for many
        different areas (block groups, tracts, counties, of radii around a point location). Region 6
        EPA enforcement, education and health risk targeting demographic evaluations (i.e., age,
        income, ethnicity, education) are often for 0.56 and 4 mile radii.
4.       Children and older individuals are more susceptible to environmental health risks.
5.       Children are susceptible to all environmental pollutants or conditions (i.e., ultra-violet
        light, lead, second hand smoke, pesticides, industrial air emissions) often with age
                                             A-48

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       specific disorders (i.e., asthma, neurological impairment, immune and hormone system
       disorder, leukemia and other childhood cancers). For these reasons it is important to
       know where high number of children populations may be in Region 6 EPA and to
       correlate this information with chemical release data and socio-economic factors (i.e.,
       language, education, poverty, ethnicity).
6.      It is assumed that there are different social-economic factors specific to each Region 6
       state which justifies using state averages for comparisons.  Factors include: availability of
       insurance and health care benefits for residents, education opportunities, public
       transportation systems, infrastructure stress related to language differences, state income
       tax, ethnic differences, employment rate and stability of industrial - business base,
       housing and utility costs, use of land, presence of rural and urban areas, availability of
       natural resources.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX,75202), carney.gerald@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                          A-49

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                      Dv Criterion: Older Population l (> 55 Years old)
                    % > 55 vrs old
Score
                    < State average                               1
                    State average-1.33 x State avg               2
                    1.34 x State avg-1.66 x State avg            3
                    1.67 x State avg-2 x State avg
                    > 2 x the State avg                           5

                      Vulnerable older population age is assumed to be > 55 y/o.
Databases:
Census 2000 Summary File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau,
2002.
References:
U.S. EPA. 1995. Computer Assisted Environmental Justice Index Methodology. Office of Planning and Analysis,
Enforcement Division, Region 6 EPA, Dallas, TX. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.       Age 55 representing "older" individuals is partly based upon organ reserve, decreases in mental and
        physical abilities, cumulative health risk considerations (i.e., consequences of smoking, diet, life style,
        occupational exposures, loss of homeostatis, decreased immune system function, neurological disease, loss
        of cognitive functions, and other factors).
2.       Demographic data will be 2000 SF3 Census coverage, changing to year 2000 data the summer of 2001.
3.       The "Age" analysis is a comparison to the state average and can be calculated for many different areas
        (block groups, tracts, counties, of radii around a point location). Region 6 EPA enforcement, education and
        health risk targeting demographic evaluations (i.e., age, income, ethnicity, education) are often for 0.56 and
        4 mile radii.
4.       Older individuals have an increased susceptibility to environmental health risks. It is important to know
        where high numbers of older populations may be in Region 6 EPA and to correlate this information with
        chemical release data and socio-economic factors (i.e.,  education, poverty, language, ethnicity).
5.       Older individuals are susceptible to all environmental pollutants and to specific chemicals or conditions
        (i.e., carbon monoxide and cardio-respiratory disease, heavy metal poisoning and decreased kidney and
        liver function, solvent exposure and liver disease).
6.       It is assumed that there are different social-economic factors specific to each Region 6 state which justifies
        using state averages for comparisons. Factors include:  availability of insurance and health care benefits for
        residents, education opportunities, public transportation systems, infrastructure stress related to language
        differences, state income tax, ethnic differences, employment rate and stability of industrial - business base,
        housing and utility costs, use of land, presence of rural  and urban areas, availability of natural resources.

EPA Contacts: Gerald Carney (U.S. EPA Region 6 Dallas, TX,75202), carney.gerald@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region  6 support), danielson.jeff@epa.gov
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                 Dv Criterion: Pregnancy l (population < 1 Years old)

        % < 1 vr old	Score
        < State average                            1
        State average-1.3 3 x State avg                     2
        1.3 4 x State avg-1.66 x State avg           3
        1.67 x State avg-2 x State avg                     4
        > 2 x State avg                            5
         Pregnancy in the population is measured by the number of children less than 1 year of age.
Databases:
Census 2000 Summary File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau,
2002.

References:
Klaassen, Curtis D., editor. 1998. Casarett andDoull's Toxicology, 5th edition. USA, McGraw-Hill.

Vaughan, V., editor. 1975. Nelson Textbook of Pediatrics, 10th edition. W.B. Saunders Company, Philadelphia, PA.

Goldman, Lynn R. and Sudha Koduru. June 2000. "Chemicals in the Environment and Developmental Toxicity to
Children: A Public Health and Policy Perspective" in Environmental Health Perspectives, Vol. 108, Supplement 3.
[incomplete citation]

Definitions, Assumptions, Limitations, Uncertainties:
1.     The definition of "pregnancy" for this criteria is children less than 1 year of age. The
       assumption being that a pregnancy existed near this time period.
2.     Demographic data will be 2000 SF3  Census coverage, changing to year 2000 data the
       summer of 2001.
3.     The "Pregnancy" analysis is a comparison to the state average and can be calculated for
       many different areas (block groups, tracts, counties, of radii around a point location).
       Region 6 EPA enforcement, education and health risk targeting demographic evaluations
       (i.e., age, income, ethnicity, education) are often for 0.56 and 4 mile  radii.
4.     Pregnant women and their fetuses are subject to increased risks from environmental
       pollution and conditions.
5.     The blood - placental barrier between the mother and fetus is not adequate to prevent
       pollutants entering the maternal blood from crossing to the fetal blood (Casarett and
       Doull's, 5th edition). Therefore the mother and fetus are at risk.
6.     It is important to know where high number of children populations may be in Region 6
       EPA and to correlate this information with chemical release data and socio-economic
       factors (i.e., language, education, poverty, ethnicity).
7.     It is assumed that there are different  social-economic factors specific to each Region 6
       state which justifies using state averages for comparisons.  Factors include: availability of
       insurance and health care benefits for residents,  education opportunities, public

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       transportation systems, infrastructure stress related to language differences, state income
       tax, ethnic differences, employment rate and stability of industrial - business base,
       housing and utility costs, use of land, presence of rural and urban areas, availability of
       natural resources.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX,75202, 6EN-XP), carney.gerald@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
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                               Dv Criterion: Population Change

                          % Population Change	Score
                          % change is a negative number
                          0-5
                          6-10
                          11-15
1
2
3
4
5
Databases:
Census 2000 Redistricting Data (Public Law 94-171) Summary File - (AR, LA, NM, OK, TX)
[machine-readable data files] / prepared by the U.S. Census Bureau, 2001.

Census 2000 Summary File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau,
2002.

References:
U.S. EPA, Region 6, March 2000. Environmental Education Targeting Study: Border Report, Analysis of Counties
Within the US/Mexico 100 Km Border Buffer, Gerald Carney, Office of Planning and Coordination, Dallas, TX
75202. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.      Population change can be numerically negative or positive.  It is assumed that a positive change
       can result in added stress to the natural environment and possible increase in air, land, and water
       pollution.
2.      Increase in population can lead to stimulation of economic growth resulting in financial and
       health benefits to the population.  These possible benefits are not captured in the "Population
       Change" criteria.
3.      The "Population Change" criteria can be calculated for the city, county, or state level only
       between 1980-1990 or 1980-2000. Change between 1990 and 2000 can be done at any level
       (e.g., census blocks, block groups, tracts, counties, etc).
4.      Region 6 EPA enforcement, education and health risk targeting demographic evaluations (i.e.,
       age, income, ethnicity, education) are often for 0.56 and 4 mile radii.
5.      It is assumed that increased population can cause the  increase demand for land, increased vehicle
       traffic, more construction sites, housing units, landscape and water alterations, areas for landfills,
       and water treatment facilities.  The result can be wildlife habitat destruction, urban runoff
       concerns, and air pollution.

EPA Contacts: Gerald Carney (U.S. EPA Region 6 Dallas, TX,75202, 6EN-XP), carney.gerald@epa.gov
Contract Support: Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
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Dv Criterion: Population Density (persons per sq.mi.)
Population Density (persons/mi2)
0
1-200
201-1,000
1,001-5,000
> 5,000
Score
1
2
3
4
5
Databases:
Census 2000 Redistricting Data (Public Law 94-171) Summary File - (AR, LA, NM, OK, TX)
[machine-readable data files] / prepared by the U.S. Census Bureau, 2001.

Census 2000 Summary File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau,
2002.

References:
U.S. EPA.  1995. Computer Assisted Environmental Justice Index Methodology. Office of Planning and Analysis,
Enforcement Division, Region 6 EPA, Dallas, TX. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.      Demographic data will be 200 SF3 Census coverage or PL-94-171. Data will change to year
       2000 data the summer of 2001.
2.      Population density is a key component of the Environmental Justice (EJ) and the Health Risk
       Index (HPJ) methodologies. In those evaluations the ranking scale is 0 to 4. For other
       evaluations the scale is 1 - 5.  The scales can be changed depending on the analysis focus (the EJ
       and HRI are exclusively human health based).
3.      It is assumed that total  population risk increases with the greater number of individuals impacted.
4.      It is assumed that the more densely populated areas of Region 6 carry more environmental
       impacts. Increased population can cause the increase demand for land, increased vehicle traffic,
       more construction sites, housing units, landscape and water alterations, areas for landfills, and
       water treatment facilities. The result can be wildlife habitat destruction, urban runoff concerns,
       and air pollution.
5.      Many different areas can be evaluated for population density  (census blocks, block groups, tracts,
       counties, of radii around a point location). Region 6 EPA enforcement, education and health risk
       targeting demographic  evaluations (i.e., age, income, ethnicity, education) are often for 0.56 and
       4 mile radii.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX,75202),  carney.gerald@epa.gov

Contractor Support: Jeff Danielson (Lockheed Martin, EPA Region 6 support),
danielson.jeff@epa.gov
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                                   Dv Criterion: Total Populationl

                 Total Population	Score
                0                                          1
                1-200                                     2
                201-1,000                                 3
                1,001-5,000                               4
                > 5,000                                    5

                 The population of an area is dependent upon the defined borders of that area (polygon).
Databases:
Census 2000 Redistricting Data (Public Law 94-171) Summary File - (AR, LA, NM, OK, TX)
[machine-readable data files] / prepared by the U.S. Census Bureau, 2001.

Census 2000 Summary File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau,
2002.

References:
U.S. EPA.  1995. Computer Assisted Environmental Justice Index Methodology. Office of Planning and Analysis,
Enforcement Division, Region 6 EPA, Dallas, TX. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.      Demographic data will be 2000 SF3 Census coverage or PL-94-171. Data will change to year
       2000 data the summer of 2001.
2.      Total Population and Population Density criteria are key components of the Environmental
       Justice (EJ) and the Health Risk Index (HPJ) methodologies.
3.      In the EJ and HRI evaluations the  ranking scale is 0 to 4. For other evaluations the scale is 1 - 5.
       The scales can be changed depending on the analysis focus (the EJ and HRI are exclusively
       human health based).
4.      It is assumed that total population  risk increases with the greater number of individuals impacted.
5.      It is assumed that the more highly  populated areas of Region 6 carry more environmental
       impacts.  Increased population can cause the increase demand for land, increased vehicle traffic,
       more construction sites, housing units, landscape and water alterations, areas for landfills, and
       water treatment facilities. The result can be wildlife habitat destruction, urban runoff concerns,
       and air pollution.
6.      Many different areas can be evaluated for total population  (census blocks, block groups, tracts,
       counties, of radii around a point location). Region 6 EPA enforcement, education and health risk
       targeting demographic evaluations (i.e., age, income, ethnicity, education) are often for 0.56 and
       4 mile radii.

EPA Contacts: Gerald Carney (U.S. EPA Region 6 Dallas, TX,75202), carney.gerald@epa.gov
Contractor Support: Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                            A-55

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                    Dv Criterion: Houses Lacking Complete Plumbing 1

                   % Housing Units Lacking Complete Plumbing    Score
                   < 1.5                                           1
                   1.6-3                                           2
                   3.1-6                                           3
                   6.1-7.5                                          4
                   >7.5                                           5

                    Drinking water supply and sewage system.
Databases:
Census 2000
Summary File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau, 2002.

References:
U.S. EPA, Region 6, March 2000. Environmental Education Targeting Study: Border Report, Analysis of Counties
Within the US/Mexico 100 Km Border Buffer, Gerald Carney, Office of Planning and Coordination, Dallas, TX
75202. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.      Data is gathered at the block group level and must be modified to apply to watershed or
       other non political boundary.
2.      Assessments by watershed or other geographic area use the "area weighting" technique
       for block groups bisected by geographic boundaries.
3.      It is assumed that plumbing refers to public or private furnished drinking water and
       sewage removal systems.
4.      It is assumed that the lack of complete plumbing systems would make these households
       more likely to use individually dug wells or surface water, and to use septic tanks or
       cesspools.
5.      It is assumed that systems used by homes without complete plumbing are monitored for
       quality less often, receive anti-microbial treatment sporadically or not at all, will be
       seasonal in quantity and quality, require secondary transport containers, and water may be
       stored without treatment. Therefore the water is more likely to become contaminated.
6.      This criteria can be calculated for several different areas (block groups, tracts, counties, of
       radii around a point location).  Region 6 EPA enforcement, education and health risk
       targeting demographic evaluations (i.e., age, income, ethnicity, education) are often for
       0.56 and 4 mile radii.
EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX), carney.gerald@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                          A-56

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                        Dv Criterion: Telephone Communications 1
                      % Housing Units Lacking Telephones    Score
                      < 12
                      13-15
                      16-20
                      21-25
                      >25
1
2
3
4
5
                       'Telephone communications are important for distribution of
                       environmental information to residents and from
                       communities.
Databases:
Census 2000 Summary File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau,
2002.

References:
U.S. EPA, Region 6, March 2000. Environmental Education Targeting Study: Border Report, Analysis of Counties
Within the US/Mexico 100 Km Border Buffer, Gerald Carney, Office of Planning and Coordination, Dallas, TX
75202. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.      Data is gathered at the block group level and must be modified to apply to watershed or
       other non political boundary.
2.      Assessments by watershed or other geographic area use the "area weighting" technique
       for block groups bisected by geographic boundaries.
3.      It is assumed that telephones are essential for community organization, government
       outreach to communities, resident's contact with local, state, and federal environmental
       agencies, and for notification to residents concerning industry accidental releases, and
       natural disasters.
4.      This criteria can be calculated for several different areas (block groups, tracts, counties, of
       radii around a point location).
5.      Does not include cellular phones.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX),  carney.gerald@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                           A-57

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Dv Criterion:
% population not able to
<5.5
5.6-10.5
10.6-16
17-25
> 25 %
Ability to Speak English
speak English well Score
1
2
3
4
5
Databases:
Census 2000 Summary File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau,
2002.

References:
U.S. EPA, Region 6, March 2000. Environmental Education Targeting Study: Border Report, Analysis of Counties
Within the US/Mexico 100 Km Border Buffer, Gerald Carney, Office of Planning and Coordination, Dallas, TX
75202. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.      Persons age 18 to 64 who speak another language and speak English "no well" or "not at
       all".
2.      Data is gathered at the block group level and must be modified to apply to watershed or
       other non political boundary.
3.      Assessments by watershed or other geographic area use the "area weighting" technique
       for block groups bisected by geographic boundaries.
4.      It is assumed that not speaking English is a barrier to taking full advantage of written and
       verbal communications, environmental, educational, and health benefits within the
       infrastructure of municipal government.
5.      It is assumed that not being able to speak English puts these individuals at an increased
       environmental, economic, and health risk.
6.      This criteria can be calculated for several different areas (block groups, tracts, counties, of
       radii around a point location).

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX), carney.gerald@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                          A-58

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                                Dv Criterion: Linguistic Isolation

                         % Households Linguistically Isolated1    Score
                         9-15
                         16-22
                         23-35
                         >35
                           Households where no one speaks English
1
2
3
4
5
Databases:
Census 2000 Summary
File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau, 2002.

References:
U.S. EPA, Region 6, March 2000. Environmental Education Targeting Study: Border Report, Analysis of Counties
Within the US/Mexico 100 Km Border Buffer, Gerald Carney, Office of Planning and Coordination, Dallas, TX
75202. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.      "Linguistically Isolated" refers to household where no one speaks English

2.      Data is gathered at the block group level and must be modified to apply to watershed or
       other non political boundary.

3.      Assessments by watershed or other geographic area use the "area weighting" technique
       for block groups bisected by geographic boundaries.

4.      It is assumed that not speaking English is an impediment to taking full advantage of
       written and verbal communications, environmental, educational, and health benefits
       within the infrastructure of municipal services.

5.      It is assumed that not being able to speak English puts these individuals at an increased
       environmental, economic, and health risk.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX), carney.gerald@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                          A-59

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                               Dv Criterion: Foreign Born 1

                          % population that is foreign born  Score
                          <6                                1
                          7-10                               2
                          11-20                              3
                          21-30                              4
                          >30                               5
                          Excluding those from Puerto Rico or the Virgin
                          Islands. Data is captured at the census block
                          group level.
Databases:
Census 2000 Summary
File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau, 2002.

References:
U.S. EPA, Region 6, March 2000. Environmental Education Targeting Study: Border Report, Analysis of Counties
Within the US/Mexico 100 Km Border Buffer, Gerald Carney, Office of Planning and Coordination, Dallas, TX
75202. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.      "Foreign Born" are individuals who are not born in the United States, Puerto Rico, U.S.
       Virgin Islands, or other U.S. territories. Individuals born abroad of U.S. parents are not
       counted as Foreign Born.
2.      Data is gathered at the block group level and must be modified to apply to watershed or
       other boundary.
3.      Assessments by watershed or other geographic areause the "area weighting" technique for
       block groups bisected by geographic boundaries.
4.      It is assumed that not being U.S.  born could be an impediment to taking full advantage of
       written and verbal communications, participation in the political process, environmental,
       educational, or health benefits within the infrastructure of municipal services due to
       language, cultural differences, or other reasons.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX), carney.gerald@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support),  danielson.jeff@epa.gov
                                           A-60

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                              Dj Criterion: Cultural Resources

                            Section 106 Compliance1   Score
                            Yes                        1
                            No                         5
                             Section 106 of National Historic Preservation Act
Databases:
Information supplied by facility.

References:
National Historic Preservation Act of 1966, as amended, 16 U. S. C. Section 470-470w-6.
U. S. EPA.  1970. Implementation Regulations for the National Environmental Policy Act, Washington, DC.
[incomplete citation]

Definitions, Assumptions, Limitations, Uncertainties:
1.      Federal non-compliance constitutes potential significant adverse impacts on cultural
       resources or historic properties.

2.      Section 106 decision is based on consultation with and the advice of the State Historical
       Preservation Office.

EPA Contacts:
Sharon Osowski (U.S.EPA Region 6, Dallas, TX, 75202), osowski.sharon@epa.gov
Joe Swick (U.S.EPA Region 6, Dallas, TX, 75202), swick.joseph@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                          A-61

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Toxicity
Dj Criterion: Toxicity Weighted TRI Water Releases
HRI1 Score for TRI2 Water Releases Score
< 1,000,000
1,000,001-2,500,000
2,500,001-5,000,000
5,000,001-10,000,000
> 10,000,000
HRI - Region 6 Health Risk Index methodology
TRI - 2000 Toxic Release Inventory data
1
2
3
4
5


Databases:
U. S. Environmental Protection Agency. 2000.  Toxic Release Inventory. TRI Data: SARA
Community Right-to-know. Washington, D.C. [updated annually]

U.S. EPA, 2002. Emergency Planning and Community Right-to-Know Act (EPCRA), Section
313, Toxic Release Inventory (TRI) 2000 chemical release data.  TRIS website download. Office
of Pollution Prevention and Toxics, Washington, D.C. 20460

References:
U.S. EPA, 1998 Health Risk Index (HRI) GIS screening methodology. Office of Planning and Coordination,
Compliance Assurance & Enforcement Division, Region 6 EPA, Dallas, TX 75202. [unpublished]

U.S. EPA, 1997. Toxic Release Inventory Relative Risk - Based Environmental Indicators Methodology, Office of
Pollution Prevention and Toxics, Washington, D.C. 20460. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.      Exposure to surrounding populations is assumed to occur from water pollutants released by regulated
       industries.
2.      TRI chemical releases are annual estimates. The releases could be over a very short time or over a several
       month period.  It is assumed that these releases can cause human health and ecological impacts.
3.      TRI reported pounds of chemical released to water are multiplied by an average of the oral toxicity factor
       (scaled from 1 to 5) and a bioaccumulation factor (BCF) (scaled 1 to 5). Algorithm: # chemical x (average
       of BCF & Oral Tox value). Each chemical's toxicity numbers are then summed.  This analysis is
       performed for each TRI facility in a targeted geographic area. The toxicity numbers of all facilities are
       summed resulting in a final number which is scaled from 1 to 5.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Mort Wakeland (U.S. EPA Region 6, Dallas, TX, 75202), wakeland.morton@epa.gov

Contractor Support: Jeff Danielson (Lockheed Martin, EPA Region 6 support),
danielson.jeff@epa.gov
                                             A-62

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                        Dj Criterion: Toxicity Weighted TRI AIR Releases

                 HRI1  Score for TRI2 Air Releases	Score
                 < 1,000,000                                1
                 1,000,001-2,500,000                        2
                 2,500,001-5,000,000                        3
                 5,000,001-10,000,000                       4
                 > 10,000,000                               5

                   HRI - Health Risk Index methodology, unitless number (pounds X toxicity factors).
                   TRI -1998 Toxic Release Inventory data
Databases:
U. S. Environmental Protection Agency. 2000.  Toxic Release Inventory. TRI Data: SARA
Community Right-to-know. Washington, D.C. [updated annually]

References:
U.S. EPA, 1998. Health Risk Index (HRI) GIS screening methodology. Office of Planning and Coordination,
Compliance Assurance & Enforcement Division, Region 6 EPA, Dallas, TX. 75202. [unpublished]

U.S. EPA, 1997. Toxic Release Inventory Relative Risk - Based Environmental Indicators Methodology, Office of
Pollution Prevention and Toxics, Washington, D.C. 20460._[unpublished]

U.S. EPA, 2002. Emergency Planning and Community Right-to-Know Act (EPCRA), Section 313, Toxic Release
Inventory (TRI) 2000 chemical release data.  TRIS web site download. Office of Pollution Prevention and Toxics,
Washington, D.C. 20460

Definitions, Assumptions, Limitations, Uncertainties:
1.      Exposure to surrounding populations is assumed to occur from air pollutants released by
       regulated industries.
2.      TRI chemical releases are annual estimates. The releases could be over a very short time or over
       a several month period. It is assumed that these releases can cause human health and ecological
       impacts.
3.      TRI reported pounds of chemical released to air are multiplied by an inhalation toxicity factor
       (scaled from 1 to 5). Algorithm:  # chemical x Tox value. Each chemical's  toxicity numbers are
       then summed. This analysis is performed for each TRI facility in a targeted geographical area.
       Toxicity numbers of all facilities are summed resulting in a final number which is scaled from 1
       to 5.
EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Mort Wakeland (U.S. EPA Region 6, Dallas, TX, 75202), wakeland.morton@epa.gov
Contractor Support: Jeff Danielson (Lockheed Martin, EPA Region 6 support),
       danielson.jeff@epa.gov
                                             A-63

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                     D! Criterion: Toxicity Weighted RCRA-BRS2 Data

                    RCRA1 facility waste (tons)	Score
                    < 1 or nonreported                  1
                    2-100                              2
                    101-1,000                          3
                    1,001-100,000                      4
                    > 100,000                          5

                     RCRA (Resource Conservation and Recovery Act)
                     BRS (Biennial Report System), modified tons
Databases:
U.S. EPA, 2000.
Biennial Report System (BRS). State data stored in EPA's BRS system.
Washington, D.C. 20460
References:
U.S. EPA, 1998. Health Risk Index (HRI) GIS screening methodology. Office of Planning and Coordination,
Compliance Assurance & Enforcement Division, Region 6 EPA, Dallas, TX. 75202. [unpublished]

U.S. EPA, 1997. Toxic Release Inventory Relative Risk - Based Environmental Indicators Methodology, Office of
Pollution Prevention and Toxics, Washington, D.C. 20460 [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.      Hazardous waste chemicals being used, transported, or stored on an industrial site can be
       released as fugitive emission, through spills, or cause damage to surrounding areas and residents
       through explosions or fire.
2.      The more reported BRS RCRA waste on a site, the more potential for environmental harm.
3.      RCRA BRS data is a reporting system for RCRA waste either generated or received. Data
       collected by states and then entered into EPA's BRS.
4.      RCRA BRS chemicals reported pounds (modified tons) are compared to TRI chemicals. TRI
       chemicals were matched with RCRA chemicals or surrogates were found for each RCRA
       chemical. HRI chemical toxicity factors were matched to each RCRA chemical or TRI surrogate
       representing a RCRA chemical.  There is significant uncertainty in the use of surrogates.
5.      RCRA - BRS chemical data are by biennial reporting. Associated chemical releases to the
       environment could be over a very short time or over a several month period.  It is assumed that
       these releases can cause human health and ecological impacts.
EPA Contacts:
Gerald Carney (U.S. EPA Region  6 Dallas, TX, 75202), carney.gerald@epa.gov
Joel  Dougherty (U.S. EPA Region 6 Dallas, TX, 75202), dougherty.joel@epa.gov
Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                           A-64

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                              Dv, Dj Criterion: Other Industries,
                            Pollution Sources, or Protected Lands

                          Number within a 2 mi buffer	Score
                          0 industries or land areas            1
                          1 industries or land areas            2
                          2 industries or land areas            3
                          3 industries or land areas            4
                          > 4 industries or land areas          5
Databases:
U.S. EPA, 2002. Envirofacts Database, www.epa.gov/enviro
U.S. EPA, 2002. Toxic Release Inventory.
U.S. EPA, 2002. National Priority List sites.
TCEQ, 1996. Permitted Industrial & Hazardous Waste Sites
TCEQ, 1996. Municipal Solid Waste Landfills
TCEQ, 2000. Radioactive Waste  Sites
TCEQ, 2002. Superfund Sites

Definitions, Assumptions, Limitations, Uncertainties:
1.      Other industries or pollution sources are defined as solid waste landfills, water supply
       intake points, RCRA Sites, Indian Reservations, Superfund (NPL) sites, Federal
       Facilities, radioactive sites, and Toxic Release Inventory (TRI) sites.
2.      Project areas (using the appropriate geographic scale) can be negatively effected or
       become more environmentally vulnerable by the cumulative impacts or proximity of
       manufacturing industries,  agriculture, defense facilities, or environmentally important
       land areas.
3.      Subj ect areas within two miles of existing facilities are factors in the assessment of
       cumulative environmental impacts.
4.      All environmentally important locations or sources of stress are not accounted for.
5.      This criterion may be calculated for the most appropriate geographic area and scale (e.g..
       watershed subunits, transportation corridors, or project areas).
6.      The area of analysis may be broken  into 1 km  grid cells for GISST criteria computation.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas,  TX), carney.gerald@epa.gov
David Parrish (U.S. EPA Region 6, Dallas,  TX, 75202), parrish.david@epa.gov

Contractor Support:
Jeff Danielson (Lockheed Martin, EPA Region 6 support), danielson.jeff@epa.gov
                                          A-65

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CAFO
                              Dj Criterion: Livestock Population
                              Density (Animal Units/CAFO Total
                                           Acres)

                            Livestock Pop. Density (LPD)1   Score
                            <10                              1
                            11-20                             2
                            21-25                             3
                            26-30                             4
                            >30                              5

                            1 Animal Units/CAFO Acres (LPD of 10 is 25 swine
                            per acre)
Databases:
Information from facility.

References:
Agri-Waste Technology, Inc., HUC Cumulative Risk Index Analysis Swine Producer Group, October 26, 1996,
Raleigh, NC. [unpublished]

U.S. EPA. 1995. Guide Manual on NPDES Regulations for Concentrated Animal Feeding Operations. EPA 833-
B-95-001. U. S. Environmental Protection Agency, Office of Water (4203). Washington, D.C.

Definitions, Assumptions, Limitations, Uncertainties:
1.      0.4 animal unit is assigned to each hog weighing more than 55 Ibs. Two thousand, five
       hundred swine over 55 Ibs. each equals  1000 animal units. For piglets, 0.2 is considered
       an equivalent animal unit.

2.      The fewer the number of animal units per facility acre the less potential for impacts.

3.      CAFO acres is the total acreage and includes buildings, treatment facilities, and
       application areas.

EPA Contacts:
Joe Swick (U.S. EPA Region  6 Dallas, TX, 75202), swick.joseph@epa.gov
Chris Ruhl (U.S. EPA Region 6 Dallas, TX, 75202), ruhl.christopher@epa.gov
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
                                          A-66

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                          Dj Criterion: Lagoon Loading Rate

                         % Lagoon Loading Rate (LLR)   Score
                         < 100%
                         101%-110%
                         111%-120%
                         121%-130%
                         > 130%
1
2
3
4
5
Databases:
Information from facility.

References:
Agri-Waste Technology, Inc., HUC Cumulative Risk Index Analysis Swine Producer Group, October 26, 1996,
Raleigh, NC. [unpublished]

U.S. Department of Agriculture, Natural Resource Conservation Service.  Agricultural Waste Management System
Component Design, Figure 10-22 Anaerobic Lagoon Loading Rate, [incomplete citation]

Watson, H. 1991.  Lagoons for Animal Waste Disposal. Alabama Cooperative Extension Service. Auburn
University, AL.

Definitions, Assumptions, Limitations, Uncertainties:
1.      NRCS has developed a map that suggests the appropriate lagoon design volume (pounds
       of volatile solids per 1000 cubic feet of lagoon per day). This design is exclusive of
       sludge storage and waste storage.

2.      Permitted facilities are not expected to exceed the 100% Lagoon Loading Rate whereas
       non-permitted facilities may exceed 100%.

EPA Contacts:
Joe Swick (U.S. EPA Region 6 Dallas, TX, 75202), swick.joseph@epa.gov
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
                                          A-67

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Databases:
Information from facility.
                                 Dj Criterion: Lagoon Treatment
                                           System Liner

                              % Hydraulic Conductivity Rate1   Score
                              < 100%                            1
                              101%-105%                        2
                              106%-110%                        3
                              111%-115%                        4
                              >115%                            5
                              ^PA NPDES General Permit for CAFOs (1993)
                              defines the maximum acceptable hydraulic
                              conductivity as 1 X 10"7 cm/sec.
References:
Agri-Waste Technology, Inc., HUC Cumulative Risk Index Analysis Swine Producer Group, October 26, 1996,
Raleigh, NC. [unpublished]

U.S. Department of Agriculture, Soil Conservation Service. Agricultural Waste Management System Component
Design, Figure 10-22 Anaerobic Lagoon Loading Rate, [incomplete citation]

U.S. EPA. 1993. 40 CFR 122. NPDES General Permit for Discharges from Concentrated Animal Feeding
Operations. U. S. Environmental Protection Agency, Washington, D.C.

Watson, H. 1991. Lagoons for Animal Waste Disposal. Alabama Cooperative Extension Service. Auburn
University, AL.

Definitions, Assumptions, Limitations,  Uncertainties:
1.     The design, construction and operation of lagoons determine their effectiveness.

2.     Permitted facilities are not expected to exceed the 100% Hydraulic Conductivity Rate
       whereas non-permitted facilities may exceed 100%.

EPA Contacts:
Joe Swick (U.S. EPA Region 6 Dallas, TX, 75202), swick.joseph@epa.gov
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
                                           A-68

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                             Dj Criterion: Land Application Technology

                             Land Application Systems1	Score
                             Innovative Technology
                             Desirable
                             Conventional
                             Poor
                             None
                              Technology systems described below.
1
2
3
4
5
Databases:
Information from facility.

References:
Dendy, D. and M. Ladd 1996. Comments on Draft Cumulative Risk Analysis, ACCORD Agriculture, Inc.,
Farnsworth, TX. [unpublished]

U.S. EPA. 1996. Swine CAFO Odors: Guidance for Environmental Impact Assessment. U. S. Environmental
Protection Agency Region 6, Lee Wilson and Associates, Santa Fe, MM. EPA Contract No. 68-03-0142.  Dallas,
TX.

Miner, J. R.  1995. An Executive Summary: A Review of the Literature on the Nature and Control of Odors from
Pork Production Facilities. Bioresource Engineering Department, Oregon State University, Corvallis, OR.

Definitions, Assumptions, Limitations, Uncertainties:
1.      Innovative technology includes subsurface injection and  tillage of waste within three
       hours of application.
2.      Desirable technology includes low pressure sprinkler systems (15-20 psi), minimizing
       land application impacts near residents, low trajectory spray, avoiding extra-fine spray.
3.      Conventional technology includes medium pressure (30-70 psi) sprinkler systems, avoids
       weekends and holiday application, and uses vegetative screens.
4.      Poor technology includes high pressure sprinkler systems (>80 psi), high trajectory spray,
       does not avoid  application on weekends or holidays, and does not use vegetative screens.
5.      Subsurface injection and tillage technology is assumed to avoid high water tables and
       highly permeable soils.
6.      Injection of slurry can reduce the odor by 80%  and ammonia emissions by 95%.
7.      Above ground application of wastes should be  tilled into the soil as soon as possible to
       reduce the rate of odor emissions. Plowing immediately after application reduces the rate
       of odor emission during the first hour by 85%.
8.      None equals no technology used or reported.
EPA Contacts:
Joe Swick (U.S. EPA Region 6 Dallas,  TX, 75202), swick.joseph@epa.gov
Gerald Carney (U.S. EPA Region 6 Dallas,  TX, 75202), carney.gerald@epa.gov
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
                                           A-69

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                               Dj Criterion: Nitrogen Budget

          Crop Nitrogen Budget (CNBV    Score
          < 100%                             1
          99%-110%                          2
          111%-120%                         3
          121%-130%                         4
          > 130%                             5

           The Crop Nitrogen Budget percent is the ratio of the sum of the annual plant available
          nitrogen produced and the commercial nitrogen fertilizer to be used divided by the crop
          nitrogen that can be utilized each year times 100.
Databases:
Information from facility.

References:
Agri-Waste Technology, Inc., HUC Cumulative Risk Index Analysis Swine Producer Group, October 26, 1996,
Raleigh, NC. [unpublished]

Natural Resource Conservation Service. Estimate of land Area Needed for Waste Application and Value of
Nutrients Applied, [incomplete citation]

Natural Resources Conservation Service. 1992. Agricultural Waste Management Field Handbook. Natural
Resources Conservation Service, USD A, Washington, D.C.

U.S. EPA.  1993. 40 CFR 122. NPDES General Permit for Discharges from Concentrated Animal Feeding
Operations. U. S. Environmental Protection Agency, Washington, D.C.

Definitions, Assumptions, Limitations, Uncertainties:
1.      The Crop Nitrogen Budget percent is the ratio of the sum of the annual plant available nitrogen
       produced and the commercial nitrogen fertilizer to be used divided by the crop nitrogen that can
       be utilized each year times 100.
2.      Annual plant available nitrogen is the amount of nitrogen available to the plant from the applied
       waste effluent.
3.      Land application crops typically require commercial fertilizers in addition to nutrients from
       waste effluent.
4.      Application rates of waste effluent might be limited by other parameters  (salt loadings,
       phosphorus loadings, hydraulic loadings).
EPA Contacts:
Joe Swick (U.S. EPA Region 6 Dallas, TX, 75202), swick.joseph@epa.gov
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
                                             A-70

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                                Dj Criterion: Phosphorus Budget

                         Crop Phosphorus Budget (CPBV    Score
                         < 100%
                         99%- 110%
                         111%- 120%
                         > 130%
1
2
O

4
5
                           The Crop Phosphorus Budget percent is the ratio of the sum
                         of the annual plant available phosphorus produced and the
                         commercial phosphorus fertilizer to be used divided by the
                         crop phosphorus that can be utilized each year times 100.
Databases:
Information from
facility.

References:
Natural Resource Conservation Service. Estimate of Land Area Needed for Waste Application and Value of
Nutrients Applied, [incomplete citation]

Natural Resources Conservation Service. 1992. Agricultural Waste Management Field Handbook. Natural
Resources Conservation Service, USD A, Washington, D.C.

U.S. EPA. 1993. 40 CFR 122. NPDES General Permit for Discharges from Concentrated Animal Feeding
Operations. U. S. Environmental Protection Agency, Washington, D.C.

Definitions, Assumptions, Limitations, Uncertainties:
1.      Annual plant available phosphorus is the amount of phosphorus available to the plant
       from the applied waste effluent.
2.      Land application crops typically require commercial fertilizers in addition to nutrients
       from waste effluent.
3.      Application rates of waste effluent might be limited by other parameters (e.g., salt
       loadings, nitrogen loadings, hydraulic loadings).
4.      Buildup of phosphorus in the soil over time may have negative environmental impacts
       (e.g., runoff of accumulated phosphorus).

EPA Contacts:
Joe Swick (U.S. EPA Region 6 Dallas, TX, 75202), swick.joseph@epa.gov
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
                                           A-71

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                                 j Criterion: Lagoon Storage
                                        Capacity l
                             Retention time (days)
Score
                             >90
                             89-60
                             59-30
                             29-15
                             < 15
   1
   2
   O

   4
   5
                              Lagoon storage above the 25 year -24 hour
                             storm event capacity.
Databases:
Information from facility.

References:
Agri-Waste Technology, Inc., HUC Cumulative Risk Index Analysis Swine Producer Group, October 26, 1996,
Raleigh, NC. [unpublished]

Natural Resources Conservation Service.  1992. Agricultural Waste Management Field Handbook. Natural
Resources Conservation Service, USD A, Washington, D.C.

U.S. EPA. 1993. 40 CFR 122. NPDES General Permit for Discharges from Concentrated Animal Feeding
Operations. U. S. Environmental Protection Agency, Washington, D.C.

Definitions, Assumptions, Limitations, Uncertainties:
1.      Storage volume above the 25 year-24 hour storm can minimize  potential environmental
       impacts.

2.      Even though the expired CAFO permit required a lagoon capacity of at least 21 days, the
       15 day capacity or less (score of 5  above) reflects CAFOs that may fall below the permit
       threshold and do not meet permit conditions.

EPA Contacts:
Joe Swick (U.S. EPA Region 6 Dallas, TX, 75202), swick.joseph@epa.gov
Abu Senkayi (U.S. EPA Region 6 Dallas,  TX, 75202), senkayi.abu@epa.gov
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
                                          A-72

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                                 j Criterion: Well Head Protection
                              Well distance from source1
Score
                              > 500 feet
                              400- 499 feet
                              300-399 feet
                              200-299 feet
                              < 200 feet
  1
  2
  O

  4
  5
                               Source of potential ground water contamination
                              are water retention facilities, confinement
                              buildings, and application sites.
Databases:
Information from facility.

References:
Dendy, D. and M. Ladd 1996. Comments on Draft Cumulative Risk Analysis, ACCORD Agriculture, Inc.,
Farnsworth, TX. [unpublished]

Goan, C. 1992. Well Water Protection on Poultry Farms. University of Tennessee Agricultural Extension Service.

Definitions, Assumptions, Limitations, Uncertainties:
1.      Well location is a potential factor contributing to possible ground water contamination.
       The closer the well is to potential sources of contamination the greater the environmental
       concern.

2.      Well and shaft (outside of well pipe) are potential conduits for ground water
       contamination.

3.      Well head protection criteria does not consider construction and design parameters.

EPA Contacts:
Joe Swick (U.S. EPA Region 6 Dallas, TX, 75202), swick.joseph@epa.gov
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
                                           A-73

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Databases:
Information from facility.
                             Dj Criterion: Employment in CAFO
                                          Industry

                             Job Units1 at CAFO Site   Score
                             >4
                             O
                             2
                             1
                             0
1
2
3
4
5
                              A job unit is equal to the state average income.
References:
Canter, L. W. 1977. Environmental Impact Assessment. McGraw-Hill Book Co. New York, NY.

Dendy, D. and M. Ladd 1996. Comments on Draft Cumulative Risk Analysis, ACCORD Agriculture, Inc.,
Farnsworth, TX. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.      Job  opportunities have both positive and negative economic effects on the local
       community.

2.      Agricultural jobs lost may not equal the job (units) created.

3.      Construction jobs, are not included since they are primarily short term, may include
       mostly migrant workers, and contribute little to the local economy.

4.      Only a small percentage of construction materials (items that cannot be economically
       trucked in) and supplies are purchased locally and benefit the local economy.

EPA Contacts:
Joe Swick (U.S. EPA Region 6 Dallas, TX, 75202), swick.joseph@epa.gov
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
                                          A-74

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                               Dj Criterion: Odor (from CAFOs)

                            Total Number of Animals	Score
                           < 5 x threshold1
                           6- 10 x threshold
                           11-15 x threshold
                           16-20 x threshold
                           > 20 x threshold
1
2
3
4
5
                            Threshold for swine = 750 animal units
Databases:
Information from facility.
References:
Miner, J. R. and C. L. Earth. 1988. Controlling Odors from Swine Buildings. Purdue University Cooperative
Extension Service. West Lafayette, IN.

U.S. EPA.  1996.  Swine CAFO Odors: Guidance for Environmental Impact Assessment. U. S. Environmental
Protection Agency Region 6, Lee Wilson and Associates, Santa Fe, MM. EPA Contract No. 68-03-0142. Dallas,
TX.

Definitions, Assumptions, Limitations, Uncertainties:
1.      An individual's perception of odor is primarily a subjective response based on attitudes
       and previous experience.
2.      Odor may be associated with water pollution, flies, noise or other issues.
3.      Odor is an indicator of ineffective  air pollution control.
4.      Residents may be reasonably close to CAFO facilities.
5.      Animal units does not equal number of animals (e.g., 2500 swine over 55 Ibs each equals
       1000 animal units).
6.      Swine odor is generally considered to be more offensive than cattle or chicken odor.
7.      "Odor" includes chemicals such as ammonia, methane gas, and hydrogen sulfide that may
       affect the health of nearby residents.
8.      Animal type and management controls could also determine the intensity of odor.

EPA Contacts:
Joe Swick (U.S. EPA Region 6 Dallas, TX, 75202), swick.joseph@epa.gov
Abu Senkayi (U.S. EPA Region 6 Dallas,  TX, 75202), senkayi.abu@epa.gov
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
                                          A-75

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                               Dj Criterion:  Transportation near
                                            CAFOs

                               Number of Trucks/week   Score
                              <7                        1
                              7-14                       2
                              15-21                      3
                              22-28                      4
                              >28                       5
Databases:
Information from facility.

References:
Canter, L. W. 1977. Environmental Impact Assessment. McGraw-Hill Book Co. New York, NY.

Dendy, D. and M. Ladd 1996. Comments on Draft Cumulative Risk Analysis, ACCORD Agriculture, Inc.,
Farnsworth, TX. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.      The less truck traffic in the area the lower the potential for negative impacts.

2.      Trucks are defined as the vehicles used in feeding and transporting (live) animals.

3.      Potential negative impacts include traffic accidents, dust, noise and odor.

4.      Road surface conditions are considered to be unimproved, county roads.

EPA Contacts:
Joe Swick (U.S. EPA Region 6 Dallas, TX, 75202), swick.joseph@epa.gov
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
                                         A-76

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                                  j Criterion: Density of
                                        CAFOs1
                            Number within 4 mi
Score
                             <2
                             3
                             4
                             5
                             >5
   1
   2
   3
   4
   5
Databases:
Oklahoma DO A. 1996. CAFO Database. Oklahoma Department of Agriculture, Oklahoma
City, OK.

U.S. EPA. 1996. CAFO Location Data Set. U. S. Environmental Protection Agency Region 6
GIS Data Library. Dallas, TX.

Definitions, Assumptions, Limitations, Uncertainties:
1.      The more CAFOs in a watershed subunit, the greater the potential for negative impacts.
2.      Four mile radius is used to be comparable with other Region 6 risk index analyses (e.g.
       Human Health Risk Index, Environmental Justice Index).
3.      The majority of CAFOs are assumed to be in the same watershed or geographic area, but
       there is the possibility that CAFOs can be in different HUCs or geographic areas.
4.      The number of CAFOs in a four mile radius was chosen by considering the size of the
       facilities (0.25-1 mi. sq.),  desirable distance between the projects (2 miles), typical size
       of the  11 digit HUC,  and the impacts of the CAFOs (runoff and odor) on the watershed
       or other geographic area.
5.      EPA data used for states other than Oklahoma.
6.      The density criterion differs from the proximity criterion in that proximity measures how
       close CAFOs are to each other (must be a minimum buffer area), this is not necessarily
       measured in the density criterion.  For example, there may be 10 CAFOs in a certain area
       (density), but they may be more clumped (greater proximity) or dispersed (lesser
       proximity).

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Abu Senkayi (U.S. EPA Region 6 Dallas, TX, 75202), senkayi.abu@epa.gov
Tom Nelson (U.S. EPA Region 6 Dallas, TX, 75202), nelson.thomas@epa.gov
Angel Kosfiszer (U.S. EPA Region 6 Dallas, TX, 75202), kosfiszer.angel@epa.gov
                                        A-77

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                                 j Criterion: Proximity of
                                        CAFOsl
                            Number Within 2mi
                                              Score
                             0
                              Includes EPA and state CAFO data
Databases:
Oklahoma DO A. 1996. CAFO Database.  Oklahoma Department of Agriculture, Oklahoma
City, OK.

U.S. EPA.  1996. CAFO Location Data Set.  U. S. Environmental Protection Agency Region 6
GIS Data Library. Dallas, TX.

References:
U.S. EPA. 1996. Swine CAFO Odors: Guidance for Environmental Impact Assessment. U. S. Environmental
Protection Agency Region 6, Lee Wilson and Associates, Santa Fe, MM. EPA Contract No. 68-03-0142. Dallas,
TX.

Definitions, Assumptions, Limitations, Uncertainties:
1.      The closer the proximity of CAFOs, the greater the potential for negative environmental
       impact (e.g., odor, noise) to the watershed subunit or other geographic area.

2.      The majority of CAFOs are assumed to be in the same watershed or defined geographic
       area, but there is the possibility that CAFOs can be in different HUCs or geographic
       areas.
3.
EPA data used for states other than Oklahoma.
4.      The density criterion differs from the proximity criterion in that proximity measures how
       close CAFOs are to each other (must be a minimum buffer area), this is not necessarily
       measured in the density criterion. For example, there may be 10 CAFOs in a certain area
       (density), but they may be more clumped (greater proximity) or dispersed (lesser
       proximity).

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Abu Senkayi (U.S. EPA Region 6 Dallas, TX, 75202), senkayi.abu@epa.gov
Tom Nelson (U.S. EPA Region 6 Dallas, TX, 75202), nelson.thomas@epa.gov
Angel Kosfiszer (U.S. EPA Region 6 Dallas, TX, 75202), kosfiszer.angel@epa.gov
                                         A-78

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  APPENDIX B
Provisional Criteria

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            APPENDIX B: PROVISIONAL CRITERIA
Introduction




      Provisional criteria are those that have not been used, do not have a database to support their




use, or are in the process of being developed, peer reviewed, and finalized.  In some cases, provisional




criteria are developed anticipating a future need, but no appropriate data are available.  More




information on criteria development is found in Chapter 3.  Provisional criteria are not placed in a group




(Appendix A) until they are finalized.
                                        B-l

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Criteria
                        Dv Criterion: Severity of Ozone Pollution

                       Project Location	Score
                       moderate                          1
                       serious                            3
                       severe                             5
Databases:
www.epa.gov/oar/oaqps/greenbk

References:
CFRPartSl Clean Air Act. Http://www.epa.gov/airs/nonattn.html
References:

Definitions, Assumptions, Limitations, Uncertainties:
EPA Contacts:
Mark Sather
Peggy Wade
Dominique Lueckenhoff
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov

Contractor Support:
JeffDanielson (ACS GSG, EPA Region 6 support), danielson.jefF@epa.gov
                                          B-2

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                             Dv Criterion: Employment1 (% unemployed)

              % unemployed	Score
              < State average                            1
              State average-1.3 3 x State avg              2
              1.34 x State avg-1.66 x State avg            3
              1.67 x State avg- 2 x State avg              4
              > 2 x the State avg                         5

               Employment is measured by the percent of resident in an area which are unemployed
              compared to the state average.
Databases:
Census 2000 Summary File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau,
2002.

References:
U.S. EPA.  1995. Computer Assisted Environmental Justice Index Methodology. Office of Planning
and Analysis, Enforcement Division, Region 6 EPA, Dallas, TX. [unpublished]

U.S. EPA. 1994. Executive Order 12898: "Federal Actions to Address Environmental Justice in
Minority Populations and Low-Income Populations". 59 Federal Register Notice 7629 (1994).

U.S. EPA, Region 6, March 2000. Environmental Education Targeting Study: Border Report, Analysis
of Counties Within the US/Mexico 100 Km Border Buffer, Gerald Carney, Office of Planning and
Coordination, Dallas, TX 75202. [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.      Demographic data will be 2000 SF3 Census coverage. Data will change to year 2000 data the
       summer of 2001.
2.      The definition of "unemployed" is for persons 16 years old and older in a specific study area
       without jobs.
3.      The "Employment" analysis is a comparison to the state average and can be calculated for
       many different areas (block groups, tracts, counties, of radii around a point location). Region 6
       EPA enforcement, education and health risk targeting demographic evaluations (i.e., age,
       income, ethnicity, education) are often for 0.56 and 4 mile radii.
4.      Employment in a study area is related to economic status. It is assumed that a high rate of
       unemployment is an indication of a depressed economic area and therefore a risk for
       environmental  stress (environmental justice concerns).

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5.      It is assumed that there are different social-economic factors specific to each Region 6 state
       which justifies using state averages for comparisons. Factors include: availability of insurance
       and health care benefits for residents, education opportunities, public transportation systems,
       infrastructure stress related to language differences, state income tax, ethnic differences,
       employment rate, stability  of industrial - business base, housing and utility costs, use of land,
       presence of rural and urban areas, availability of natural resources.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX,75202), carney.gerald@epa.gov

Contractor Support:
Jeff Danielson (ACS GSG, EPA Region 6 support), danielson.jeff@epa.gov
                                              B-4

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                                  Dv Criterion: Age of Homes l
                             Year Home built
Score
                             1980-1999
                             1970-1979
                             1960-1969
                             1950-1959
    1
    2
    3
    4
Databases:
Census 2000 Summary File 3 - (AR, LA, NM, OK, TX) / prepared by the U.S. Census Bureau,
2002.

References:
U.S. EPA. 1997. Lead-Based Paint Geographical Information System (June 23,1997 Draft). Region 6
EPA, Dallas, TX, Anna Treines, Compliance and Enforcement Division, [unpublished]

Definitions, Assumptions, Limitations, Uncertainties:
1.      The housing age criteria is derived from a calculation of the Census block group percentage of
       older homes.
2.      The age of homes score is calculated by multiplying a weighted factor for each age range by the
       percentage of homes in that range, then summing the range scores to arrive at a single score of
       1 through 5. [{(% of homes built between 1980-1999) /  100} * 1] + [{(% 1970-79) / 100} *
       2] + [{(% 1960-69) / 100} * 3] + [{(%  1950-59) / 100}  * 4] +[{(% 1949 and earlier) / 100}
       *5].
3.      A cumulative ranking of all decades is given a weighted 1 to 5 ranking for all housing in a block
       group.
4.      It is assumed that older homes are more likely to have  a higher concentration of lead in paint
       and are more likely to have paint in deteriorated condition
5.      The criteria  is used in EPA Region 6 Lead-Based Paint Program for outreach to the home sales
       industry (The Real Estate Notification and Disclosure Rule, section 1018 of Title X). Data used
       in conjunction with income, age (children), and demographics.

EPA Contacts:
Gerald Carney (U.S. EPA Region  6 Dallas, TX,75202), carney.gerald@epa.gov

Contractor Support:
Jeff Danielson (ACS GSG, EPA Region 6 support), danielson.jeff@epa.gov
                                           B-5

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                        D! Criterion: RCRA Permitted Units 1
                                      at Facility

                       # permitted RCRA waste units    Score
                       0                                  1
                       1 or 2                              2
                       3                                  3
                       4                                  4
                       >4                                5

                         Waste disposal or storage process requiring an
                       EPA permit to operate.
Databases:
RCRIS, Resource Conservation and Recovery Act of 1976, as amended.

References:

Definitions, Assumptions, Limitations, Uncertainties:
1.      Information self reported by regulated facility.
2.      Only units directly affecting groundwater are included.
3.      Units include waste piles, landfills, land application and surface impoundments.
4.      All units are assumed to be operating.
5.      Waste stored for greater than 90 days.
6.      The greater the number of permitted units, the greater the potential for environmental impacts.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Larry Brnicky (U.S. EPA Region 6 Dallas, TX, 75202), brnicky.larry@epa.gov
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov

Contractor Support:
Jeff Danielson (ACS GSG, EPA Region 6 support), danielson.jeff@epa.gov
                                           B-6

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                                Dj Criterion: RCRA Hazardous
                                        Waste Disposal

                                Ib s of waste/day    Score
                                0- 100
                                101 - 1,000
                                >1,000
1
3
5
Databases:
RCRIS, Resource Conservation and Recovery Act of 1976, as amended.

References:

Definitions, Assumptions, Limitations, Uncertainties:
1.      Information supplied by facility.  All waste is considered equally hazardous whether waste is
       listed or meets constituent definition.
2.      The criterion numbers represent an average calculated annually by the facility.
3.      This criterion does not take into account toxicity.
4.      Disposal actions include the use of landfills, land application, surface impoundments, injection
       wells, and ocean dumping.
5.      Waste is assumed to be properly and adequately disposed of in an permitted location.
6.      The greater the amount of waste disposed, the greater the chance for potential environmental
       impact.
7.      Hazardous waste definitions, including disposal regulations are as defined in RCRA.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Larry Brnicky (U.S. EPA Region 6 Dallas, TX, 75202), brnicky.larry@epa.gov
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov

Contractor Support:
Jeff Danielson (ACS GSG, EPA Region 6 support), danielson.jeff@epa.gov
                                           B-7

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                              Dj Criterion: Water Design Flow Data

                         NPDES Design Flow data (gal/day^	Score
                         < 1,000,000                               1
                         1,000,001-2,500,000                       2
                         2,500,001- 5,000,000                      3
                         5,000,001-10,000,000                      4
                         >10,000,000                              5
References:
U.S. EPA, 1999. SNC Tracker, URL:http//intranet.epa.gov/oeca/oc/eptdd/teb/sncgloss.html.
Washington, D.C. 20460, from PCS (Permit Compliance System)

Definitions, Assumptions, Limitations, Uncertainties:
1.      NPDES (National Pollutant Discharge Elimination System) CWA permits
2.      Design flow from an NPDES facility is the permitted waste stream effluent for the site.
3.      It is assumed that if the design flow amount is relatively high, there is more potential for
       environmental harm.
4.      Receiving stream flow capacities are not directly considered in the criteria.  Stream flow is a
       separate watershed vulnerability criteria.  Design Flow and Stream Flow criteria may be used
       together to assess stream loading.
5.      Specifics concerning the waste being released are not considered.

EPA Contacts:
Gerald Carney (U.S. EPA Region 6 Dallas, TX, 75202), carney.gerald@epa.gov
Bob Goodfellow (U.S. EPA Region 6 Dallas, TX, 75202), goodfellow.bob@epa.gov
Cathy Bius (U.S. EPA Region 6, Dallas, TX, 75202), bius.catherine@epa.gov
Being Verified with Enforcement Targeting

Contractor Support:
Jeff Danielson (ACS GSG, EPA Region 6 support), danielson.jeff@epa.gov

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Databases:
National Register of Historical
Sites
                                  Criterion: Density of National
                                       Historical Places
                               Number within 4 mi
                               <2
                               O

                               4
                               5
                               >5
Score
    1
    2
    3
    4
    5
References:

Definitions, Assumptions, Limitations, Uncertainties:
1.      The more National Historic Places in a watershed subunit, the greater the potential for negative
       impacts.
2.      Four mile radius is used to be comparable with other Region 6 risk index analyses (e.g. Human
       Health Risk Index, Environmental Justice Index).
3.      The majority of National Historical Places are assumed to be in the same watershed, but there
       is the possibility that managed lands can be in different HUCs.
4.      The number of five managed lands in a five mile radius was chosen by considering the size of
       the facilities (0.25-1 mi. sq.), desirable distance between the projects (2 miles), typical size of
       the 11 digit HUC,  and the impacts of the managed lands on the watershed.

EPA Contacts:
Dominique Lueckenhoff (U.S. EPA Region 6, Austin, TX), lueckenhoff.Dominique@epa.gov
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
David Parrish (U.S. EPA Region 6, Dallas, TX, 75202), parrish.david@epa.gov

Contractor Support:
Jeff Danielson (ACS GSG, EPA Region 6  support), danielson.jeff@epa.gov
                                           B-9

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                                Criterion: Proximity of National
                                      Historical Places
                          Number within 2mi radius
Score
                          >2
                          1.5-2
                          1-1.5
                          0.5-1
                          <0.5
    1
    2
    3
    4
    5
Databases:
National Register of Historical Sites

References:

Definitions, Assumptions, Limitations, Uncertainties:
1.      The closer the proximity of historic sites, the greater the potential for negative environmental
       impact to the watershed subunit.
2.      The majority of historic sites are assumed to be in the same watershed, but there is the
       possibility that managed lands can be in different HUCs.

EPA Contacts:
Dominique Lueckenhoff (U.S. EPA Region 6, Austin, TX), lueckenhoff.Dominique@epa.gov
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
David Parrish (U.S. EPA Region 6, Dallas, TX, 75202), parrish.david@epa.gov

Contractor Support:
Jeff Danielson (ACS GSG, EPA Region 6 support), danielson.jeff@epa.gov
                                          B-10

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                           j Criterion: Environmental Assessment
                                                                Score
                        Independent assessment/s performed          1
                        Self assessment/s performed                 3
                        No Environmental assessment/s performed     5
Databases
FEDPLAN: (PGMT) Environmental Program Management Costs

References:

Definitions, Assumptions, Limitations, Uncertainties:
1.      An environmental assessment is a review or audit of the organizations environmental system
       which may include current compliance status with applicable environmental laws.
2.      An independent assessment is an environmental assessment conducted by personnel
       independent of the area/s being assessed.
3.      A self assessment is an environmental assessment conducted by personnel which are affiliated
       with the area/s being assessed.
4.      The result of any self or independent environmental assessments benefits the facility by
       establishing baseline conditions and/or results in corrective actions. Environmental assessments
       does not include those conducted under the National Environmental Policy Act (NEPA) for
       proposed Federal actions. Time  frame for consideration is limited to the last five years.
5.      The degree of benefit is not measured by this indicator.  The impacted media are unknown
       without further analyses.
6.      Corrective action follow-through is an uncertainty.

EPA Contacts:
Joyce Stubblefield (U.S. EPA Region 6 Dallas, TX, 75202), stubblefield.joyce@epa.gov
Tim Dawson (U.S. EPA Region 6 Dallas, TX, 75202), dawson.timothy@epa.gov
                                           B-ll

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                                 j Criterion: Pollution Prevention
                 Activity (within last 2 years')
Score
                 Pollution Prevention Plan and > 4 of the listed activities         1
                 Pollution Prevention Plan + 4 of the listed activities             2
                 Pollution Prevention Plan + 3 of the listed activities             3
                 Pollution Prevention Plan only                               4
                 No Pollution Prevention activities                            5
Databases:
Data from facility.

References:

Definitions, Assumptions, Limitations, Uncertainties:
1.      Acceptable Pollution Prevention activities include: establishing a Pollution Prevention Plan,
       source reduction, waste treatment, recycling, training of personnel or partnership with other
       entities for pollution prevention activities, and recognition or award for pollution prevention
       activities.
2.      Pollution Prevention is any practice that (1) reduces the amount of any hazardous substance,
       pollutant, or contaminant  entering any waste stream or otherwise released into the environment
       (including fugitive emissions) prior to recycling, treatment, or disposal; and (2) reduces the
       hazardous to public health and the environment associated with the release of such substances,
       pollutants, or contaminants.
3.      Recycling is defined as a series  of activities by which materials that are no longer useful to the
       generator are collected, sorted,  processed, and converted into raw materials and used in the
       production of new products.
4.      Treatment is defines as any  method, technique, or process designated to change the physical, or
       biological character or composition of any hazardous waste so as to neutralize such waste, or to
       render non-hazardous.

EPA Contacts:
Eli Martinez (U.S. EPA Region 6 Dallas, TX, 75202), martinez.eli@epa.gov
Joyce Stubblefield (U.S. EPA Region  6 Dallas, TX, 75202), stubblefield.joyce@epa.gov
                                            B-12

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                               Criterion: Model Energy Code (MEC)
                           % over MEC Guidelines
                           >25%
                            21-25%
                            11-20%
                            5-10%
Score
    1
    2
    3
    4
    5
Databases:
Information from facility.

References:
E.O. 12843 Procurement Requirements and Policies for Federal Agencies for Ozone-Depleting
Substances (April 23, 1993)

E.O. 12844 Federal Use of Alternative Fueled Vehicles (April 21,1993)

E.O. 12845 Requiring Agencies to Pursue Energy-Efficient Computer Equipment (April 21, 1993)

E.O. 12873 Federal Acquisition, Recycling and Waste Prevention (October 20,1993)

E.O. 12902 Energy Efficiency and Water Conversation at Federal Facilities (March 8, 1994)

Energy Policy Act of 1992

Climate Change Action Plan (CCAP)

Definitions, Assumptions, Limitations, Uncertainties:
1.      The use of energy efficient practices in the construction of buildings are to follow Model Energy
       Code for both Residential and Commercial buildings in all Federal facilities.  In following these
       guidelines software is used as an easy check for compliance. The Model Energy Code includes
       new construction as well as renovation.
2.      Many levels of Energy efficiency are possible.  For instance the use of 12 SEER FTVAC
       systems, tinted and spectrally select low emissivity glazing for glass, attic ventilation to reduce
       heat build-up, perimeter of slab foundation insulation, use of   high R sheathing, use of radiant
       barriers on sidewalls and in attic, placement of duct and mechanical equipment of conditioned
                                           B-13

-------
       space.  All of these will improve energy efficiency of a structure.
3.      The ultimate goal is a 70 percent reduction in energy consumption to receive the highest rating.
4.      Energy Policy Act of 1992 (EPAct) authorizes DOE,  Department of Commerce and US EPA
       and other Federal agencies to work in tandem to reduce the energy consumption of appliances,
       set standards of efficiency, promote new technologies and reduce pollution through increased
       efficiency.  The EPAct provides for mandatory standards as well as voluntary development and
       adoption of housing standards, commercial building code standards and labeling of a select
       group of consumer products.
5.      E.O 12843 directs federal agencies and facilities to change procurement policies to reduce the
       use of ozone depleting substances earlier than Montreal Protocol phase-out schedules. A
       reduction of ozone depleting includes less use of a substance such as R-22 and elimination of
       CFC-11 and 12.
6.      E.O. 12844 places  the federal government in a leadership role in the demand for and use of
       alternative fueled vehicles.
7.      E.O. 12845 encourage market transformation through increased purchase of energy-efficient
       computer products that save money and reduce pollution.
8.      E.O. 12873 directs executive agencies to increase the purchase of 1) products containing
       recovered materials and 2)environmentally preferable products.  The order also
       encourages agencies to intensify their recycling and waste prevention activities.
9.      E.O. 12902 encourages increased use of energy and water saving-saving products in federal
       facilities. Purchasing of products in the top of the market for energy and water   efficiency
       leads to large savings on annual utility bills.
10.    Climate Change Action Plan (CCAP) promotes voluntary partnerships to reduce and prevent
       pollution through cost effective practices that conserve energy and waste. Federal facilities are
       asked to implement energy efficiency practices and waste reduction practices by taking
       advantage of energy saving practices such as 1) the use of energy efficient       construction
       practices and technologies, 2) energy efficient office equipment, 3) energy efficient appliances,
       4) recycling of glass, aluminum, steel, office paper, and newspaper, 5) reuse of landscape
       (yard) wastes.
11.    Percent over MEC guidelines values will be  rounded to the nearest integer.

EPA Contacts:
Joyce Stubblefield (U.S. EPA Region 6 Dallas, TX, 75202), stubblefield.joyce@epa.gov
Patrick Kelly (U.S. EPA Region 6 Dallas, TX, 75202), kelly.patrick@epa.gov
                                            B-14

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Dj Criterion: Energy Efficient
Office Equipment
% Facility Upgrade
>40%
31-40%
21-30%
11-20%
< 10%
Score
1
2
3
4
5
Databases:
Information from facility.

References:
See D! Criterion Model Energy Code(MEC).

Definitions, Assumptions, Limitations, Uncertainties:
1.      This criterion measures the percentage of your facility that has upgraded to energy efficient
       office equipment, operating as it was intended.
2.      Energy Efficient Office Equipment includes Copy Machines, Facsimile Machines, Computers,
       Computer Monitors, Scanners, Printers.
3.      Percent facility upgrade values will be rounded to the nearest integer.

EPA Contacts:
Joyce Stubblefield (U.S. EPA Region 6 Dallas, TX, 75202), stubblefield.joyce@epa.gov
Patrick Kelly (U.S. EPA Region 6 Dallas, TX, 75202), kelly.patrick@epa.gov
                                           B-15

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                              D! Criterion: Energy Efficient (EE)
                                         Appliances

                          % improvement on the EE scale  Score
> 80%
61-80%
41-60%
21-40%
< 20%
                                                             1
                                                             2
                                                             3
                                                             4
                                                             5
Databases:
Information from facility.

References:
See Dj Criterion Model Energy Code(MEC).

Definitions, Assumptions, Limitations, Uncertainties:
1.      Residential and commercial appliances include Refrigerators, Dishwashers, Washing Machines,
       and Room Air-conditioners.
2.      In replacing these appliances life cycle issues should be taken into consideration. As an
       example, a cheaper price tag on a room air conditioner that may cost $400 may end up
       costing in excess of $2000 to operate over a ten year period. Similarly a $600 room air
       conditioner with similar cooling capacity will consume $1200 over a ten period.
3.      Different appliances have different efficiency ratings.
4.      The benchmark can be found on Federal Trade Commission Energy Guide placed on every
       appliance by federal law. They are a guidepost (not necessarily actual) measure of its energy
       efficiency.
5.      Percent improvement on EE scale values will be rounded to the nearest integer.

EPA Contacts:
Joyce Stubblefield (U.S. EPA Region 6 Dallas, TX, 75202), stubblefield.joyce@epa.gov
Patrick Kelly (U.S. EPA Region 6 Dallas, TX, 75202), kelly.patrick@epa.gov
                                           B-16

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Dj Criterion: Lighting System
% reduction in enersv usase
>25%
21-25%
16-20%
11-15%
< 10%
Upgrade
Score
1
2
3
4
5
Databases:
Information from facility.

References:
See D! Criterion Model Energy Code(MEC).

Definitions, Assumptions, Limitations, Uncertainties:
1.      Since the passage of EPAct, energy efficiency is being standardized on all appliances, consumer
       electronics, lighting products and mechanical systems.
2.      Energy Star Buildings and Green Lights Program participants have demonstrated a reduction of
       45 percent after renovating to Green Lights standards.
3.      Percent reduction in energy usage values will be rounded to the nearest integer.

EPA Contacts:
Joyce Stubblefield (U.S. EPA Region 6 Dallas, TX, 75202), stubblefield.joyce@epa.gov
Patrick Kelly (U.S. EPA Region 6 Dallas, TX, 75202), kelly.patrick@epa.gov
                                           B-17

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                               Dj Criterion: Million Solar Roofs
                                           Initiative

                              No. of solar products used  Score
                              >5                          1
                              4                            2
                              2-3                          3
                              1                            4
                              0                            5
Databases:
Information from facility.

References:
See D! Criterion Model Energy Code(MEC).

Definitions, Assumptions, Limitations, Uncertainties:
1.      Solar technologies include: solar hot water heaters, photovoltaic landscape lighting, photovoltaic
       street lighting, remote water pumping, and photovoltaic panels for power     generation.
2.      Solar Thermal Energy is a simple way to preheat air for boilers and furnace air-intakes and
       water for residential and commercial use. It is possible to achieve significant reductions in
       energy consumption for hot water.
3.      Solar energy is a free source that has shown that it can routinely provide 70 percent of
       domestic and commercial hot water.
4.      The design of solar thermal hot water systems are now built and tested in accordance with strict
       federal and industry standards. Recognizing this advance in ability to perform in both Canada
       as well as desert environments of the southwest.
5.      Solar Photovoltaic Energy has seen tremendous reduction in price over the last ten years.
       Commercial sales of units as low as $4.50 per watt are now available. This makes photovoltaic
       installations economical for remote water pumping,  street lighting, remote locations and new
       construction in areas where line extensions, excavation or  other costs are high.
6.      Solar photovoltaic energy can be store on batteries or distributed across the power grid to
       others. Solar photovoltaic technology is now capable of substantially offsetting the peak
       demand of energy thus providing greater cost saving in the commercial sector where peak
       demand charges during daylight hours are high.
7.      Number of solar products used values will be rounded to the  nearest integer.

EPA Contacts:
Joyce Stubblefield (U.S. EPA Region 6 Dallas, TX, 75202), stubblefield.joyce@epa.gov
Patrick Kelly (U.S. EPA Region 6 Dallas, TX, 75202), kelly.patrick@epa.gov
                                            B-18

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                                 Dj Criterion: Federal Energy
                                    Management Program

                               % reduction from baseline  Score
                               >12%                      1
                               10-12%                    2
                               7- 9%                      3
                               4- 6%                      4
                               < 3%                      5
Databases:
Information from facility.

References:
See D! Criterion Model Energy Code(MEC).

Definitions, Assumptions, Limitations, Uncertainties:
1.      The Federal Energy Management Program (FEMP) is an Executive order which outlines the
       reduction of energy consumption by federal facilities by 30 percent in 2005 from 1985 levels,
       and 20 percent for industrial federal facilities by 2005 using 1990 as the baseline year.
2.      Percent reduction from baseline values will be rounded to the nearest integer.

EPA Contacts:
Joyce Stubblefield (U.S. EPA Region 6 Dallas, TX, 75202), stubblefield.joyce@epa.gov
Patrick Kelly (U.S. EPA Region 6 Dallas, TX, 75202), kelly.patrick@epa.gov
                                          B-19

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Dj Criterion: Proximity of Managed
Lands
Number within 2mi radius
>2
1.5-2
1-1.5
0.5-1
<0.5

Score
1
2
3
4
5
Databases:

References:


Definitions, Assumptions, Limitations, Uncertainties:
1.      Managed lands include National Park Service Lands, National Forest Service Lands, U.S. Fish
       & Wildlife Service Lands, State Parks and Wildlife Areas, City Parks, County Parks, and
       other lands used for conservation/recreation. Managed lands also may include other large
       properties owned/managed by the Federal Government such as Military Bases, BLM Lands,
       and DOE Lands. Section4f....
2.      The closer the proximity of managed lands, the greater the potential for negative environmental
       impact to the watershed subunit.
3.      The majority of managed lands are assumed to be in the same watershed, but there is the
       possibility that managed lands can be in different HUCs.

EPA Contacts:
Sharon Osowski (U.S. EPA Region 6 Dallas, TX, 75202), osowski.sharon@epa.gov
David Parrish (U.S. EPA Region 6, Dallas, TX, 75202), parrish.david@epa.gov
Dominique Lueckenhoff (U.S. EPA Region 6, Austin, TX), lueckenhoff.dominique@epa.gov

Contractor Support:
Jeff Danielson (ACS GSG, EPA Region 6 support), danielson.jeff@epa.gov
                                          B-20

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                       D! Criteria: Unregulaterf CAFO2 Facilities

                      % Unregulated CAFOs in HUC3   Score
                      <20                               1
                      20-29                             2
                      30-39                             3
                      40-50                             4
                      >50                               5

                        facilities not operating under EPA NPDES General
                      Permit (40 CFR I22.23[b]).
                        Concentrated Animal Feeding Operations
                        Hydrologic Unit Catalog (watershed subunit)
Databases:
None available

References:
U.S. EPA.  1992. A Synoptic Approach to Cumulative Impact Assessment: A Proposed
Methodology.  Office of Research and Development, EPA/600/R-92/167, Washington, D.C.

U.S. EPA. Code of Federal Regulations. (40 CFR 122.23[b]

Definitions, Assumptions, Limitations, Uncertainties:
1.      Regulated concentrated Animal Feeding Operations (CAFOs) are lots or facilities where
       animals have been, are, or will be stabled or confined and fed or maintained for a total of at
       least 45 days in any 12-month period, and the animal confinement areas do not sustain crops,
       vegetation, forage growth,  or post-harvest residues in the normal growing season (40 CFR
       122.23 [b]).
2.      The greater the percentage of unregulated CAFOs in a HUC, the greater the potential for
       negative environmental impacts.

EPA Contacts:
Joe Swick ((U.S. EPA Region 6, Dallas, TX, 75202), swick.joseph@epa.gov
Gerald Carney (U.S. EPA Region  6, Dallas, TX, 75202),carney.gerald@epa.gov

Contractor Support:
Jeff Danielson (ACS GSG, EPA Region 6 support), danielson.jeff@epa.gov
                                           B-21

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                                  Dv criteria: Presence of Aquifer
                          Aquifer presence1
Score
                          No aquifer present at site              1
                          Confined aquifer present at site         3
                          Unconfmed aquifer present at site       5
                            Aquifer or recharge area by data set overlay in GIS.
Databases:
US Geological Survey Digital Data Series DDS-11. Geology of the coterminous United States at
1:2,500,000 scale- a digital representation of King, P. B., and H. M. Beikman map 1974.

US Geological Survey, 1994. Hydrologic unit maps of the coterminous United States.

References:
US EPA, 2000. U.S. Environmental Protection Agency Designation of Sole Source Aquifers, Fact Sheet,
http://www.epa.gov/earthlr6/ 6wq/swp/ssa/ssafacts.htm, Region 6 Ground Water / UIC Section.

Federal Registers: Edwards Underground Reservoir  (40 FR 58344,12/16/75), Chicot Aquifer System (53 FR 20893,
06/07/88), Austin-Area Edwards Aquifer (53 FR 20897, 06/07/88), Southern Hills Aquifer System (53 FR 25538,
07/07/88), Arbuckle-Simpson Aquifer (54 FR 39230, 09/25/89).

Definitions, Assumptions, Limitations, Uncertainties:
1.      Local aquifers might not be shown on generalized databases

2.      Assumes that contaminants will enter aquifer through leakage or seepage from the surface
       environment

EPA Contacts:
Clay Chesney (U.S. EPA Region 6, Dallas, TX, 75202),  chesney.claybourne@epa.gov

Contractor Support:
Jeff Danielson (ACS GSG, EPA Region 6 support), danielson.jeff@epa.gov
                                              B-22

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Dv Criterion:
ASM
<0.20
0.20-0.29
0.30-0.39
0.40-0.49
>0.50
Landscape Texture
Score
1
2
3
4
5
Databases:
U.S. Geological Survey. 2000 National Land Cover Database. Compiled from Landsat satellite TM
imagery (circa 1992) with a spatial resolution of 30 meters.

References:
Mladenoff,D. J.andB. DeZonia. 2001. APACK 2.22 Analytical Software. User's Guide.

Musick and Grover. 1991. Image textural measures as indices of landscape pattern. IN Turner and Gardner (eds)
Quantitative Methods in Landscape Ecology. Springer-Verlag. New York, New York.

Definitions, Assumptions, Limitations, Uncertainties:
1.      Landscape texture is measured by the metric Angular  second moment (ASM), calculated using
the APACK software.                 i,,. ,=*
2.      The formula for ASM is   -S-SX: ">'"Xfd..r\'
3.      Angular second moment is a measure of image texture and habitat fragmentation.
4.      Angular second moment has a range of 0 to 1.  A zero equals a landscape with many cover
       types and little clumping. Unity equals a landscape with a single cover type and maximum
       clumping of a cover type.
5.      Maximum clumping likely has more core  habitat for interior species.  A high degree of edge
       habitat may be indicative of more opportunistic, "weedy" species.
6.      Wildlife habitats include flood plains, wetlands, bottomland hardwoods, rangelands, upland
       forests and grasslands.
7.      Landscape texture (ASM)  should be used with the other descriptive aspects of APACK in
       order to adequately characterize the landscape.
8.      APACK is a program that  calculates statistics of interest to landscape ecologists from raster
       data. It calculates many metrics useful in  determining landscape pattern and structure and
       calculates these metrics faster and upon larger data sets than other packages (e.g.,
       FRAGSTATS).

EPA Contacts: Sharon Osowski (U.S.EPA Region 6, Dallas, TX, 75202), osowski.sharon@epa.gov

                                            B-23

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Dv Criterion:
AI
>0.50
0.49-0.40
0.39-0.30
0.29-0.20
<0.20
Landscape Aggregation
Score
1
2
3
4
5
Databases:
U.S. Geological Survey. 2000 National Land Cover Database. Compiled from Landsat satellite TM
imagery (circa 1992) with a spatial resolution of 30 meters.

References:
Mladenoff,D. land B. DeZonia. 2001. APACK 2.22 Analytical Software. User's Guide.

He, H. S., B. E. DeZonia, and D. J. Mladenoff. 2000. An aggregation index (AI) to quantify spatial patterns of
landscapes. Landscape Ecology 15:591-601

Definitions, Assumptions, Limitations, Uncertainties:
1.

2.

3.
4.
5.
Aggregation index reports the degree to which patches of certain land cover classes (selected by
the user for a particular project) are clumped or dispersed.
Aggregation index can                                            be reported for the landscape
as a whole or for each    A ,    <• - ..-M. ...• - -.-.-	•-1  --.' -*•••> ••• • ••     land cover class of interest.
                           •   \:..- i  ,,,...•-.. I . 1 -. i ' i :....'..•• I, ••-.. f '
The formula for AI is
The aggregation index has a range of 0 to 1. A zero equals when each patch is narrow in one
direction and long in aother.  Unity equals a land cover class that is completely aggregated into a
single square patch.
Landscape aggregation is measured by the metric Aggregation Index (AI) calculated using the
APACK software.
Wildlife habitats include flood plains, wetlands, bottomland hardwoods, rangelands, upland forests
and grasslands.
Aggregation Index (AI) should be used with the other descriptive aspects of APACK in order to
adequately characterize the landscape.
APACK is a program that calculates statistics of interest to landscape ecologists from raster
data. It calculates many metrics useful in determining landscape pattern and structure and
calculates these metrics faster and upon larger data sets than other packages (e.g.,
FRAGSTATS).
EPA Contacts:
Sharon Osowski (U.S.EPA Region 6, Dallas, TX, 75202), osowski.sharon@epa.gov
                                               B-24

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Dv Criterion:
Ratio
<0.20
0.21-0.30
0.31-0.40
0.41-0.50
>0.50
Patch Area (normalized, average)
Score
1
2
O
4
5
Databases:
U.S. Geological Survey. 2000 National Land Cover Database. Compiled from Landsat satellite TM
imagery (circa 1992) with a spatial resolution of 30 meters.

References:
Mladenoff,D. J. andB. DeZonia.  2001. APACK 2.22 Analytical Software. User's Guide.

Riitters, K. H., R. V. O'Neill, C. T. Hunsaker, J. D. Wickham, D. H. Yankee, S. P. Timmins, K. B. Jones, and B. L.
Jackson. 1995. A factor analysis of landscape pattern and structure metrics. Landscape Ecology 1:23-39

Definitions, Assumptions, Limitations, Uncertainties:
1.       Normalized average patch area is a measure of habitat fragmentation.
2.       Patch area is measured by the metric normalized average patch area (AAM) calculated using the
        APACK software.
3.       Normalized average area per patch reports the average of                            each patch
        area relative to the area of a square with the same perimeter.        j' ,,. .;;'..;•:..',: ,-.
4.       The formula for normalized average patch area (AAN) is

5.       Normalized average area can be reported for the landscape as a whole or for each land cover
        class of interest.
6.       Normalized average area has a range of 0 to 1. A zero equals a patch that is narrow in one
        direction and long in another.  Unity equals a square.
7.       Normalized average patch area (AAN) is calculated using the APACK software.
8.       Wildlife habitats include  flood plains, wetlands, bottomland hardwoods, rangelands, upland forests
        and grasslands.
9.       Normalized average patch area should be used with the other descriptive aspects of APACK in
        order to adequately characterize the landscape.
10.     APACK is a program that calculates statistics of interest to landscape ecologists from raster
        data.  It calculates many  metrics useful  in determining landscape pattern and structure and
        calculates these metrics faster and upon larger data sets than other packages (e.g.,
        FRAGSTATS).

EPA Contacts:  Sharon Osowski (U.S.EPA Region 6, Dallas, TX, 75202), osowski.sharon@epa.gov
                                               B-25

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   APPENDIX C
GIS PROGRAMMING

-------
Introduction
       The GISST program runs using ESRI's Arclnfo Workstation 7.2.1 or higher on a Windows

NT 4.0 or Windows 2000 system. The program should also run on a UNIX workstation but has not

been tested.

       The GISST application can be run using either coordinates (latitude/longitude in degrees-

minutes-seconds) or a polygon coverage. The program is started by typing the following at the ARC

prompt:  &r criamain. ami .  The coord argument is used when entering coordinates

and the cover argument is used when running the program on a polygon coverage. CRIAMAIN.AML

contains the Arclnfo command to create the 0.5, 2, or 4 mile buffers. THe user only needs to specify

the distance within CRIAMAIN. Results from a GISST run are saved by writing the data to an INFO

file called TRACKCRIA which is created in the current directory. If the INFO file does not exist then

the file will be created. If the INFO file already exists in the current directory then the record will be

appended to the bottom of the file.

       The following Arc Macro Language (AML) are used to calculate the various criteria in GISST:
       CRIAAQUI.AML
       CRIACENSUS.AML
       CRIACLIP.AML

       CRIADELETE.AML
       CRIADISTANCE.AML
       CRIAENVIRO.AML
       CRIAFLOOD.AML
       CRIALANDUSE.AML

       CRIALOADTRACKERAML
area over a sole source aquifer (1 or 5)
calculates socioeconomic data
extracts data from library for water, road, and flood
criteria
deletes temporary files created during run
calculates distance to surface water
calculates number of facilities in area
calculates percent of area in flood plain
calculates land use criteria (percent wildlife, land cover
quality, etc.)
loads results of GISST run into tracking file
                                           C-l

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CRIAMAIN.AML
CRIARDS.AML
CRIASTATSGO.AML

CRIATRI.AML

CRIAWATERSHED.AML
CRIAWTRCHAN.AML
CRIAWTRQUAN.AML
CRIAWTRSURFAAML
Main ami - starts all others
calculates road density
calculates the soil criteria (permeability, groundwater
probability)
calculates criteria associated with Toxic Release
Inventory
identifies watershed (8-digit HUC) study area is in and
assigns watershed criteria

calculates density of channels/canals
calculates density of streams/rivers
calculates percent of area that is surface water.
                                    C-2

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                                         AMLs

                                    CRIAAQULAML
                           «Remove title before running in GIS»
ap
coo keyboard
mape sitebuff4
res \\r6gisntl\sharel\common\aquifer\ssaquifers polys one %.x% %.y%
&s .ssaqu = [before [show select \\r6gisntl\sharel\common\aquifer\ssaquifers polys],]
&if %.ssaqu% = 0 &then
 &s .ssaqu = 1
&else
 &s .ssaqu = 5
q
&return
                                  CRIACENSUS.AML
                           «Remove title before running in GIS»
ap
mape sitebuff4
create sitemape mape
q
build sitemape polys

/**extract data from the .cengeo layer.
&s xnum = 1
&do &until %xnum% > %.statenum%
 &if %xnum% = 1 &then
  &s libval %.stl%
 &else
  &if %xnum% = 2 &then
    &s libval %.st2%
  &else
    &if %xnum% = 3 &then
     &s libval %.st3%
    &else
     &s libval %.st4%
                                          C-3

-------
 librarian %libval%
  setcover sitemape
  setlayers names cengeo
  setoutputname cengeo sitemepop_%xnum%
  extract topological # clip
 q
 external sitemepop_%xnum%
 &s xnum = %xnum% + 1
&end

&if %.statenum% = 1 &then
 rename sitemepop_l sitemepop
&else
 &if %.statenum% = 2 &then
  &do
   append sitemepop polys all
   sitemepop_l
   sitemepop_2
   end
   clean sitemepop polys #
  &end
 &else
  &if %.statenum% = 3 &then
   &do
     append sitemepop polys all
     sitemepop_l
     sitemepop_2
     sitemepop_3
     end
     clean sitemepop polys #
   &end
  &else
   &if %.statenum% = 4 &then
     &do
     append sitemepop polys all
     sitemepop_l
     sitemepop_2
     sitemepop_3
     sitemepop_4
     end
     clean sitemepop polys #
                                         C-4

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     &end

clip sitemepop sitebuff4 site4pop poly
build site4pop polys
&data arc info
 ARC
  SEL SITEMEPOP.PAT
  ALTER CTBNA,,,C,,,,,
  REDEFINE
  19
  CENSUSID
  13
  13
  C

 QSTOP
&end
dissolve sitemepop sitepopds censusid poly
build sitepopds polys

additem site4pop.patpop site4pop.patpop areasqft 4 12 f 3
additem site4pop.patpop site4pop.patpop areasqmi 4 12 f 6
additem site4pop.patpop site4pop.patpop arearatio 4 12 f 6
additem site4pop.patpop site4pop.patpop poprate 4 12 f 6
additem site4pop.patpop site4pop.patpop newpoptmp 4 12 f 2
additem site4pop.patpop site4pop.patpop newpop 9 9 i
additem site4pop.patpop site4pop.patpop whitenh 4 12 f 2
additem site4pop.patpop site4pop.patpop minority 9 9 i
additem site4pop.patpop site4pop.patpop mintmp 4 12 f 2
additem site4pop.patpop site4pop.patpop mave 4 12 f 6
additem site4pop.patpop site4pop.patpop pave 4 12 f 6
additem site4pop.patpop site4pop.patpop blckpop 4 12 f 6
additem site4pop.patpop site4pop.patpop housetop 4 12 f 6
additem site4pop.patpop site4pop.patpop households 9 9 i
additem site4pop.patpop site4pop.patpop poortmp 4  12 f 6
additem site4pop.patpop site4pop.patpop poor 9 9 i
additem site4pop.patpop site4pop.patpop babestop 4 12 f 6
additem site4pop.patpop site4pop.patpop babes 9 9 i
additem site4pop.patpop site4pop.patpop pop25 9 9 i
additem site4pop.patpop site4pop.patpop pop25tmp 4 12 f 6
additem site4pop.patpop site4pop.patpop pop 16 9 9 i
                                           C-5

-------
additem site4pop.patpop site4pop.patpop pop!6tmp 4 12 f 6
additem site4pop.patpop site4pop.patpop pop5 9 9 i
additem site4pop.patpop site4pop.patpop popStmp 4 12 f 6
additem site4pop.patpop site4pop.patpop nohsdeg 9 9 i
additem site4pop.patpop site4pop.patpop nohsdegtmp 4 12 f 6
additem site4pop.patpop site4pop.patpop edusc 4 12 f 6
additem site4pop.patpop site4pop.patpop edusctmp 4 12 f 6
additem site4pop.patpop site4pop.patpop kids 9 9 i
additem site4pop.patpop site4pop.patpop kidstmp 4 12 f 6
additem site4pop.patpop site4pop.patpop older 9 9 i
additem site4pop.patpop site4pop.patpop oldertmp 4 12 f 6
additem site4pop.patpop site4pop.patpop unemploy 9 9 i
additem site4pop.patpop site4pop.patpop unemploytmp 4 12 f 6
additem site4pop.patpop site4pop.patpop engabil 9 9 i
additem site4pop.patpop site4pop.patpop engabiltmp 4 12 f 6
additem site4pop.patpop site4pop.patpop lingiso 9 9 i
additem site4pop.patpop site4pop.patpop lingisotmp 4 12 f 6
additem site4pop.patpop site4pop.patpop forborn 9 9 i
additem site4pop.patpop site4pop.patpop forborntmp 4 12 f 6
additem site4pop.patpop site4pop.patpop hseage 4 12 f 6
additem site4pop.patpop site4pop.patpop hseagetmp 4 12 f 6

&data arc info
 ARC
 SEL SITE4POP.PATPOP
 REDEFINE
 17
 BLCKGR-ID
 10
 10
 I

 QSTOP
&end

relate add
blkpop
\V6gisntl\sharel\%.st%\census\pl9417%.st%
info
censusid
censusid
                                           C-6

-------
ordered
ro
bgpop
%.progpath%%. st%stfl
info
blckgr-id
blckgr-id
ordered
ro
ap
&do rad &list 4 2
 &if %rad% = 4 &then &s .pop2go go
 &if %.pop2go% = go &then
 &do
 &s study %rad%
 mape site%rad%pop
 res sitepopds polys mape
 res sitepopds polys overlap sitebuff%rad% polys # within
 &s tempsel [before [show select sitepopds polys],]
 &if %tempsel% gt 0 &then
  &do
   infofile sitepopds polys ids.file censusid init
   res site%rad%pop region.pop keyfile ids.file censusid
   calculate site%rad%pop region.pop areasqft = ( areal * 27878400 )
   nsel site%rad%pop region.pop
  &end

 calculate site%rad%pop region.pop areasqft = area
 clearsel
 res site%rad%pop region.pop pop 100 > 0
 calculate site%rad%pop region.pop newpoptmp = (0.00000003587 * areasqft) * popdens
 calculate site%rad%pop region.pop newpop = newpoptmp
 calculate site%rad%pop region.pop newpoptmp = newpoptmp - newpop
 res site%rad%pop region.pop newpoptmp > .5
 calculate site%rad%pop region.pop newpop = newpop + 1
 clearsel
 res site%rad%pop region.pop pop 100 < newpop
 calculate site%rad%pop region.pop newpop = pop 100
 clearsel
                                          C-7

-------
res site%rad%pop region.pop pop 100 > 0
calculate site%rad%pop region.pop poprate = newpop / pop 100
calculate site%rad%pop region.pop whitenh = blkpop//p004_0002
calculate site%rad%pop region.pop mintmp = pop 100 - whitenh
calculate site%rad%pop region.pop mave = mintmp / pop 100
calculate site%rad%pop region.pop mintmp = mave * newpop
calculate site%rad%pop region.pop minority = mintmp
calculate site%rad%pop region.pop mintmp = mintmp - minority
res site%rad%pop region.pop mintmp > .5
calculate site%rad%pop region.pop minority = minority + 1

clearsel
res site%rad%pop region.pop newpop > 0
calculate site%rad%pop region.pop blckpop = bgpop//totpop
calculate site%rad%pop region.pop housetmp = bgpop//households
calculate site%rad%pop region.pop babestmp = bgpop//tot-kids16
calculate site%rad%pop region.pop popStmp = bgpop//pop>5
calculate site%rad%pop region.pop nohsdegtmp = bgpop//nohsdeg
calculate site%rad%pop region.pop kidstmp = bgpop//kidsO-6
calculate site%rad%pop region.pop oldertmp = bgpop//per55&over
calculate site%rad%pop region.pop unemploytmp = bgpop//unemploy
calculate site%rad%pop region.pop engabiltmp = bgpop//engabil
calculate site%rad%pop region.pop lingisotmp = bgpop//lingiso
calculate site%rad%pop region.pop forborntmp = bgpop//forborn
clearsel
calculate site%rad%pop region.pop edusctmp = bgpop//eduscore
calculate site%rad%pop region.pop hseagetmp = bgpop//hseagesc

res site%rad%pop region.pop blckpop > 0
res site%rad%pop region.pop households = 0
calculate site%rad%pop region.pop households = 1
clearsel
res site%rad%pop region.pop newpop = 0
calculate site%rad%pop region.pop households = 0
clearsel
res site%rad%pop region.pop newpop > 0
res site%rad%pop region.pop blckpop > 0
calculate site%rad%pop region.pop pave = newpop / blckpop

-------
calculate site%rad%pop region.pop housetop = housetop * pave
calculate site%rad%pop region.pop households = housetop
calculate site%rad%pop region.pop housetop = housetop - households
res site%rad%pop region.pop housetop > .5
calculate site%rad%pop region.pop households = households + 1
clearsel

&if%rad% = 4&then
 &s areabuff = %.area4mi%
&else
 &s areabuff = %.area2mi%

calculate site%rad%pop region.pop arearatio = (areasqft * 0.00000003587006) / %areabuff%

res site%rad%pop region.pop wid ne 99
calculate site%rad%pop region.pop edusc = edusctop * arearatio
calculate site%rad%pop region.pop hseage = hseagetop * arearatio
clearsel

&do type &list pop5 pop 16 pop25 babes poor nohsdeg kids older unemploy engabil lingiso forborn
 res site%rad%pop region.pop newpop = 0
 calculate site%rad%pop region.pop %type% = 0
 clearsel
 res site%rad%pop region.pop newpop > 0
 res site%rad%pop region.pop blckpop > 0
 calculate site%rad%pop region.pop %type%top = %type%tmp * pave
 calculate site%rad%pop region.pop %type% = %type%top
 calculate site%rad%pop region.pop %type%top = %type%tmp - %type%
 res site%rad%pop region.pop %type%tmp > .5
 calculate site%rad%pop region.pop %type% = %type% + 1
 clearsel
&end

statistics site%rad%pop region.pop # blckgrid
sum newpop
sum minority
sum households
sum pop5
sum pop 16
sum pop25
sum babes

                                         C-9

-------
sum poor
sum nohsdeg
sum kids
sum older
sum unemploy
sum engabil
sum lingiso
sum forborn
sum edusc
sum hseage

n
n
&s newpop [show select blckgrid info 1 item sum-newpop]
&s sminority [show select blckgrid info 1 item sum-minority]
&s households [show select blckgrid info 1 item sum-households]
&s pop5 [show select blckgrid info 1 item sum-pop5]
&s pop 16 [show select blckgrid info 1  item sum-pop 16]
&s pop25 [show select blckgrid info 1  item sum-pop25]
&s sbabes [show select blckgrid info 1 item sum-babes]
&s spoor [show select blckgrid info 1 item sum-poor]
&s snohsdeg [show select blckgrid info 1 item sum-nohsdeg]
&s skids [show select blckgrid info 1 item sum-kids]
&s solder [show select blckgrid info  1 item sum-older]
&s sunemploy [show select blckgrid info 1 item sum-unemploy]
&s sengabil [show select blckgrid info 1 item sum-engabil]
&s slingiso [show select blckgrid info 1  item sum-lingiso]
&s sforborn [show select blckgrid  info 1 item sum-forborn]
&s edusc [show select blckgrid info 1 item sum-edusc]
&s hseage [show select blckgrid info 1  item sum-hseage]

&if %newpop% > 0 &then
 &do
 &s minority [calc %sminority% / %newpop%]
 &s babes [calc %sbabes% / %newpop%]
 &s kids [calc %skids% / %newpop%]
 &s older [calc %solder% / %newpop%]
 &s forborn [calc %sforborn% / %newpop%]
 &if %households% > 0 &then

                                         C-10

-------
  &do
   &s poor [calc %spoor% / %households%]
   &s lingiso [calc %slingiso% / %households%]
  &end
 &else
  &do
   &s poor 0
   &s lingiso 0
  &end
 &if %pop25% > 0 &then
  &s nohsdeg [calc %snohsdeg% / %pop25%]
 &else
  &s nohsdeg 0
 &if %pop!6% > 0 &then
  &s unemploy [calc %sunemploy% / %pop!6%]
 &else
  &s unemploy 0
 &if %pop5% > 0 &then
  &s engabil [calc %sengabil% / %pop5%]
 &else
  &s engabil 0
&end
&else
&do
 &s minority 0
 &s babes 0
 &s poor 0
 &s nohsdeg 0
 &s kids 0
 &s older 0
 &s unemploy 0
 &s engabil 0
 &s lingiso 0
 &s forborn 0
&end

&if %study% = 4 &then
 &s cliparea = %.area4mi%
&else
 &s cliparea = %.area2mi%
                                       C-ll

-------
&s .popdens%rad% [calc %newpop% / %cliparea%]
&s popd [calc %newpop% / %cliparea%]
&if %popd% <= 200 &then
 &s .popdens%rad%sc 1
&else
 &if %popd% > 200 and %popd% <= 1000 &then
  &s .popdens%rad%sc 2
 &else
  &if %popd% > 1000 and %popd% <= 5000 &then
   &s .popdens%rad%sc 3
  &else
   &s .popdens%rad%sc 4

&select %.st%
&when AR
 &do
  &s nohsdegav .337
  &s minorityav .178
  &s poorav .360
  &s kidsav .099
  &s olderav .239
  &sbabesav.012
  &s unemployav .040
  &s engabilav .004
  &s lingisoav .005
  &s forbornav .011
 &end
&when LA
 &do
  &s nohsdegav .317
  &s minorityav .342
  &s poorav .363
  &s kidsav. 114
  &s olderav .191
  &sbabesav .014
  &s unemployav .056
  &s engabilav .010
  &s lingisoav .022
  &s forbornav .021
 &end
&when NM

                                      C-12

-------
 &do
  &s nohsdegav .249
  &s minorityav .496
  &s poorav .310
  &skidsav .117
  &solderav .187
  &sbabesav .014
  &s unemployav .049
  &s engabilav .034
  &s lingisoav .065
  &s forbornav .053
 &end
&when OK
 &do
  &s nohsdegav .254
  &s minorityav .190
  &s poorav .320
  &skidsav.!01
  &s olderav .223
  &sbabesav.012
  &s unemployav .042
  &s engabilav .007
  &s lingisoav .009
  &s forbornav .021
 &end
&when TX
 &do
  &s nohsdegav .279
  &s minorityav .394
  &s poorav .276
  &skidsav.H4
  &s olderav .176
  &sbabesav .014
  &s unemployav .046
  &s engabilav .052
  &s lingisoav .058
  &s forbornav .090
 &end
&otherwise
 &do
  &s nohsdegav 0
                                       C-13

-------
  &s minorityav 0
  &s poorav 0
  &s kidsav 0
  &s olderav 0
  &s babesav 0
  &s unemployav 0
  &s engabilav 0
  &s lingisoav 0
  &s forbornav 0
 &end
&end

&do type &list nohsdeg minority poor kids older babes unemploy engabil lingiso forborn
 &if %type% = nohsdeg &then
  &do
   &s one = %nohsdegav%
   &s per = %nohsdeg%
  &end
 &if %type% = minority &then
  &do
   &s one = %minorityav%
   &s per = %minority%
  &end
 &if %type% = poor &then
  &do
   &s one = %poorav%
   &s per = %poor%
  &end
 &if %type% = kids &then
  &do
   &s one = %kidsav%
   &s per = %kids%
  &end
 &if %type% = older &then
  &do
   &s one = %olderav%
   &s per = %kids%
  &end
 &if %type% = babes &then
  &do
   &s one = %babesav%
                                       C-14

-------
   &s per = %babes%
  &end
 &if %type% = unemploy &then
  &do
   &s one = %unemployav%
   &s per = %unemploy%
  &end
 &if %type% = engabil &then
  &do
   &s one = %engabilav%
   &s per = %engabil%
  &end
 &if %type% = lingiso &then
  &do
   &s one = %lingisoav%
   &s per = %lingiso%
  &end
 &if %type% = forborn &then
  &do
   &s one = %forbornav%
   &s per = %forborn%
  &end

 &stwo= 1.33 *%one%
 &s three = 1.66 * %one%
 &s four = 2 * %one%
 &if %per% <= %one% &then
  &s .%type%%rad%sc = 1
 &else
  &if %per% > %one% and %per% <= %two% &then
   &s .%type%%rad%sc = 2
  &else
   &if %per% > %two% and %per% <= %three% &then
    &s .%type%%rad%sc = 3
   &else
    &if %per% > %three% and %per% <= %four% &then
     &s .%type%%rad%sc = 4
    &else
     &s .%type%%rad%sc = 5
&end
                                     C-15

-------
&if %newpop% > 0 &then
 &do
 &s .newpop%rad% %newpop%
 &s .minority%rad% [calc %sminority% / %newpop%]
 &s .babes%rad% [calc %sbabes% / %newpop%]
 &s .kids%rad% [calc %skids% / %newpop%]
 &s .older%rad% [calc %solder% / %newpop%]
 &s .forborn%rad% [calc %sforborn% / %newpop%]
 &s .edusc%rad% %edusc%
 &s .hseage%rad% %hseage%
 &if %households% > 0 &then
   &do
    &s .pooi%rad% [calc %spoor% / %households%]
    &s .lingiso%rad% [calc %slingiso% / %households%]
   &end
 &else
   &do
    &s .poor%rad% 0
    &s .lingiso%rad% 0
   &end
 &if %pop25% > 0 &then
   &s  .nohsdeg%rad% [calc %snohsdeg% / %pop25%]
 &else
   &s  .nohsdeg%rad% 0
 &if %pop!6% > 0 &then
   &s  .unemploy%rad% [calc %sunemploy% / %pop!6%]
 &else
   &s  .unemploy%rad% 0
 &if%pop5%>0&then
   &s  .engabil%rad% [calc %sengabil% / %pop5%]
 &else
   &s  .engabil%rad% 0
&end
&else
&do
 &s .newpop%rad% 0
 &s .minority%rad% 0
 &s .babes%rad% 0
 &s .poor%rad% 0
 &s .nohsdeg%rad% 0
 &s .kids%rad% 0

                                     C-16

-------
 &s .older%rad% 0
 &s .unemploy%rad% 0
 &s .engabil%rad% 0
 &s .lingiso%rad% 0
 &s .forborn%rad% 0
 &s .edusc%rad% 0
 &s .hseage%rad% 0
 &end

&if %study% = 4 &then
 &do
  &if %newpop% > 0 &then
   &do
    arc clip site4pop  sitebuffi site2pop poly
    &s .pop2go go
   &end
  &else
   &do
    &s .pop2go no
    &s .newpop2 0
    &s .minority2 0
    &s .babes2 0
    &s .poor2 0
    &s .nohsdeg2 0
    &s .kids2 0
    &s .older2 0
    &s .unemploy2 0
    &s .engabi!2 0
    &s .Iingiso2 0
    &s .forborn2 0
    &s .edusc2 0
    &s .hseage2 0
    &s .minority2sc 1
    &s .babes2sc 1
    &s .poor2sc 1
    &s .nohsdeg2sc 1
    &s .kids2sc 1
    &s .older2sc 1
    &s .unemploy2sc 1
    &s .engabi!2sc 1
    &s .Iingiso2sc 1
                                       C-17

-------
     &s .forborn2sc 1
     &s .popdens2 0
     &s .popdens2sc 0
   &end
  &end

 &if [exists blckgrid -info] &then
  &s erase [delete blckgrid -info]
&end
&end

q
&return
                                    CRIACLIP.AML
                           «Remove title before running in GIS»
&sys copy %.progpath%\files\prj.adf sitebuff4\prj.adf

/"extract data from the .cengeo layer.
&s xnum = 1
&do &until %xnum% > %.statenum%
 &if %xnum% = 1 &then
  &s libval %.stl%
 &else
  &if %xnum% = 2 &then
    &s libval %.st2%
  &else
    &if %xnum% = 3 &then
    &s libval %.st3%
    &else
    &s libval %.st4%

 librarian %libval%
 setcover sitebuff4
 setlayers names cengeo
 setoutputname cengeo sitewtpy4_%xnum%
 extract topological # clip

 setlayers names a

                                          C-18

-------
 setoutputname a siterd4_%xnum%
 extract topological # clip

 setlayers names h
 setoutputname h sitewtar4_%xnum%
 extract topological # clip

 &if %.flooddata% = .TRUE. &then
  &do
   setlayers names flood
   setoutputname flood siteflood4_%xnum%
   extract topological # clip
  &end

 q

 external sitewtpy4_%xnum%
 external siterd4_%xnum%
 external sitewtar4_%xnum%
 &if [exists siteflood4_%xnum% -cover] &then
  external siteflood4_%xnum%

/* build sitewtpy4_%xnum% polys
 clean sitewtpy4_%xnum% # .0000000000000001 .0000000000000001
 build siterd4_%xnum% arcs
 build sitewtar4_%xnum% arcs
 &if [exists siteflood4_%xnum% -cover] &then
  clean siteflood4_%xnum% # .00000000000000001 .00000000000001
/*   build siteflood4_%xnum% polys

 &s xnum = %xnum% + 1

&end

&if %.statenum% = 1 &then
 &do
  rename sitewtpy4_l sitewtpy4
  rename siterd4_l siterd4
  rename sitewtar4_l sitewtar4
  &if [exists siteflood4_l  -cover] &then
    rename siteflood4 1 siteflood4
                                         C-19

-------
 &end
&if %.statenum% = 2 &then
 &do
  append sitewtpy4 polys all
  sitewtpy4_l
  sitewtpy4_2
  end
  clean sitewtpy4 # .0000000001 .00000000001
  append siterd4 arcs all
  siterd4_l
  siterd4_2
  end
  build siterd4 arcs
  append sitewtar4 arcs all
  sitewtar4_l
  sitewtar4_2
  end
  build sitewtar4 arcs

  &if [exists siteflood4_%l -cover] &then
   &do
     append siteflood4 polys all
     siteflood4_l
     siteflood2_l
     end
     clean siteflood4 # .00000000001 .00000000001
   &end
 &end
&if %.statenum% = 3 &then
 &do
  append sitewtpy4 polys all
  sitewtpy4_l
  sitewtpy4_2
  sitewtpy4_3
  end
  clean sitewtpy4 # .0000000001 .00000000001
  append siterd4 arcs all
  siterd4_l
  siterd4_2
  siterd4_3
  end

                                           C-20

-------
  build siterd4 arcs
  append sitewtar4 arcs all
  sitewtar4_l
  sitewtar4_2
  sitewtar4_3
  end
  build sitewtar4 arcs

  &if [exists siteflood4_l -cover] &then
    &do
     append siteflood4 polys all
     siteflood4_l
     siteflood4_2
     siteflood4_3
     end
     clean siteflood4 # .00000000001 .00000000001
    &end
 &end
&if %.statenum% = 4 &then
 &do
  append sitewtpy4 polys all
  sitewtpy4_l
  sitewtpy4_2
  sitewtpy4_3
  sitewtpy4_4
  end
  clean sitewtpy4 # .0000000001 .00000000001
  append siterd4 arcs all
  siterd4_l
  siterd4_2
  siterd4_3
  siterd4_4
  end
  build siterd4 arcs
  append sitewtar4 arcs all
  sitewtar4_l
  sitewtar4_2
  sitewtar4_3
  sitewtar4_4
  end
  build sitewtar4 arcs

                                           C-21

-------
  &if [exists siteflood4_l -cover] &then
   &do
     append siteflood4 polys all
     siteflood4_l
     siteflood4_2
     siteflood4_3
     siteflood4_4
     end
     clean siteflood4 # .00000000001 .00000000001
   &end
 &end

clip sitewtpy4 sitebuffi sitewtpy2 poly
clip siterd4 sitebufEZ siterd2 line
clip sitewtar4 sitebufEZ sitewtar2 line
&if [exists siteflood4 -cover] &then
 clip siteflood4 sitebufEZ siteflood2 poly

/*build sitewtpy2 polys
clean sitewtpy2 # .000000000000001 .0000000000001
build siterd2 arcs
build sitewtar2 arcs
&if [exists siteflood2 -cover] &then
 clean siteflood2 # .000000000000001 .000000000000001
/*  build siteflood2 polys

&s xnum = 1
&do &until %xnum% > %.statenum%
 &if %xnum% = 1 &then &s state = %.stl%
 &if %xnum% = 2 &then &s state = %.st2%
 &if %xnum% = 3 &then &s state = %.st3%
 &if %xnum% = 4 &then &s state = %.st4%
 clip \V6gisntl\sharel\%state%\soils\statsgo sitebuff4 stats4_%xnum% poly
 &s xnum = %xnum% + 1
&end

&if %.statenum% = 1 &then
 rename stats4_l stats4
&else
 &if %.statenum% = 2 &then
  &do
                                           C-22

-------
    append stats4 polys all
    stats4_l
    stats4_2
    end
    clean stats4 # .00000000001 .00000000001
  &end
 &else
  &if %.statenum% = 3 &then
    &do
     append stats4 polys all
     stats4_l
     stats4_2
     stats4_3
     end
     clean stats4 # .00000000001  .00000000001
    &end
  &else
    &if %.statenum% = 4 &then
     &do
      append stats4 polys all
      stats4_l
      stats4_2
      stats4_3
      stats4_4
      end
      clean stats4 # .00000000001 .00000000001
     &end
clip stats4 sitebuff2 stats2 poly
/*build stats2 polys
clean stats2 # .0000000000001 .00000000000001

&return
                                   CRIADELETE.AML
                            «Remove title before running in GIS»
&if [exists sitebuff2 -cover] &then kill sitebufEZ all
&if [exists sitebuff4 -cover] &then kill sitebuff4 all
                                           C-23

-------
&if [exists stats2 -cover] &then kill stats2 all
&if [exists stats4 -cover] &then kill stats4 all
&if [exists stats4_l -cover] &then kill stats4_l all
&if [exists stats4_2 -cover] &then kill stats4_2 all
&if [exists stats4_3 -cover] &then kill stats4_3 all
&if [exists stats4_4 -cover] &then kill stats4_4 all
&if [exists sitept -cover] &then kill sitept all
&if [exists siterd2 -cover] &then kill siterd2 all
&if [exists siterd4 -cover] &then kill siterd4 all
&if [exists siterd4_l -cover] &then kill siterd4_l all
&if [exists siterd4_2 -cover] &then kill siterd4_2 all
&if [exists siterd4_3 -cover] &then kill siterd4_3 all
&if [exists siterd4_4 -cover] &then kill siterd4_4 all
&if [exists site2bna -cover] &then kill site2bna all
&if [exists site4bna -cover] &then kill site4bna all
&if [exists sitewtpy2 -cover] &then kill sitewtpy2 all
&if [exists sitewtpy4 -cover] &then kill sitewtpy4 all
&if [exists sitewtpy4_l -cover] &then kill sitewtpy4_l all
&if [exists sitewtpy4_2 -cover] &then kill sitewtpy4_2 all
&if [exists sitewtpy4_3 -cover] &then kill sitewtpy4_3 all
&if [exists sitewtpy4_4 -cover] &then kill sitewtpy4_4 all
&if [exists sitewtar2 -cover] &then kill sitewtar2 all
&if [exists sitewtar4 -cover] &then kill sitewtar4 all
&if [exists sitewtar4_l -cover] &then kill sitewtar4_l all
&if [exists sitewtar4_2 -cover] &then kill sitewtar4_2 all
&if [exists sitewtar4_3 -cover] &then kill sitewtar4_3 all
&if [exists sitewtar4_4 -cover] &then kill sitewtar4_4 all
&if [exists siteflood2 -cover] &then kill siteflood2 all
&if [exists siteflood4 -cover] &then kill siteflood4 all
&if [exists siteflood4_l -cover] &then kill siteflood4_l all
&if [exists siteflood4_2 -cover] &then kill siteflood4_2 all
&if [exists siteflood4_3 -cover] &then kill siteflood4_3 all
&if [exists siteflood4_4 -cover] &then kill siteflood4_4 all
&if [exists sitelumape  -cover] &then kill sitelumape all
&if [exists sitelumed -cover] &then kill sitelumed all
&if [exists site41ud -cover] &then kill site41ud all
&if [exists site21ud -cover] &then kill site21ud all
&if [exists site41u -cover] &then kill site41u all
&if [exists site21u -cover] &then kill site21u all
&if [exists sitemape -cover] &then kill sitemape all
&if [exists site4pop -cover] &then kill site4pop all

                                               C-24

-------
&if [exists site2pop -cover] &then kill site2pop all
&if [exists sitemepop -cover] &then kill sitemepop all
&if [exists sitepopds -cover] &then kill sitepopds all
&if [exists site4clgr -grid] &then kill site4clgr all
&if [exists soiWstatsarea -info] &then &s erase [delete soiWstatsarea -info]
&if [exists soi!2statsarea -info] &then &s erase [delete soi!2statsarea -info]
&if [exists soi!4stats -info] &then &s erase [delete soiWstats -info]
&if [exists soi!2stats -info] &then &s erase [delete soi!2stats -info]
&if [exists wtpy2stat -info] &then &s erase [delete wtpy2stat -info]
&if [exists wtpy4stat -info] &then &s erase [delete wtpy4stat -info]
&if [exists wtarch4stat -info] &then &s erase [delete wtarch4stat -info]
&if [exists wtarch2stat -info] &then &s erase [delete wtarch2stat -info]
&if [exists wtar2stat -info] &then &s erase [delete wtar2stat -info]
&if [exists wtar4stat -info] &then &s erase [delete wtar4stat -info]
&if [exists rd4stat -info] &then &s erase [delete rd4stat -info]
&if [exists rd2stat -info] &then &s erase [delete rd2stat -info]
&if [exists site41ustat -info] &then &s erase [delete site41ustat -info]
&if [exists site21ustat -info] &then &s erase [delete site21ustat -info]
&if [exists site41udstat -info] &then &s erase [delete site41udstat -info]
&if [exists site21udstat -info] &then &s erase [delete site21udstat -info]
&if [exists site4agstat -info] &then &s erase [delete site4agstat -info]
&if [exists site2agstat -info] &then &s erase [delete site2agstat -info]
&if [exists site4wetstat -info] &then &s erase [delete site4wetstat -info]
&if [exists site2wetstat -info] &then &s erase [delete site2wetstat -info]
&if [exists fldl004stat -info] &then &s erase [delete fld!004stat -info]
&if [exists fldl002stat -info] &then &s erase [delete fld!002stat -info]
&if [exists fld5004stat -info] &then &s erase [delete fld5004stat -info]
&if [exists fld5002stat -info] &then &s erase [delete fld5002stat -info]
&if [exists ids.file -info] &then &s erase [delete ids.file -info]
&if [exists blckgrid -info] &then &s erase  [delete blckgrid -info]
&if [exists tristat -info] &then &s erase [delete tristat -info]
                                     CRIADISTANCE.AML
                              «Remove title before running in GIS»

ap
mape sitebuff4
res sitewtar4 arcs circle %.x% %.y% 300
&s .dswft = [before [show select sitewtar4 arcs],]
&if %.dswft% = 0 &then
                                               C-25

-------
 &do
  clearsel
  res sitewtar4 arcs circle %.x% %.y% 900
  &s .dswft = [before [show select sitewtar4 arcs],]
  &if %.dswft% = 0 &then
   &do
     clearsel
     res sitewtar4 arcs circle %.x% %.y% 2700
     &s .dswft = [before [show select sitewtar4 arcs],]
     &if %.dswft% = 0 &then
     &do
       clearsel
       res sitewtar4  arcs circle %.x% %.y% 8100
       &s .dswft = [before [show select sitewtar4 arcs],]
       &if %.dswft% = 0 &then
        &s .dswsc 1
       &else
        &s .dswsc 2
     &end
     &else
     &s .dswsc 3
   &end
  &else
   &s .dswsc 4
 &end
&else
 &s  .dswsc 5
q
&return
                                  CRIAENVIRO.AML
                           «Remove title before running in GIS»

ap
mape sitebuff4
res %.progpath%r6ef99 points mape
res %.progpath%r6ef99 points overlap sitebuff4 polys
&s .othersite4 = [before [show select %.progpath%r6ef99 points],]
&if %.othersite4% = 0 &then
 &s  .othfac4 1
                                          C-26

-------
&else
 &if %.othersite4% = 1 &then
  &s .othfac4 2
 &else
  &if %.othersite4% = 2 &then
   &s .othfac4 3
  &else
   &if %.othersite4% = 3 &then
    &s .othfac4 4
   &else
    &s .othfac4 5
clearsel
res %.progpath%r6ef99 points mape
res %.progpath%r6ef99 points overlap sitebuffZ polys
&s .othersite2 = [before [show select %.progpath%r6ef99 points],]
&if %.othersite2% = 0 &then
 &s .othfac2 1
&else
 &if %.othersite2% = 1 &then
  &s .othfac2 2
 &else
  &if %.othersite2% = 2 &then
   &s .othfac2 3
  &else
   &if %.othersite2% = 3 &then
    &s .othfac2 4
   &else
    &s .othfac2 5
q
&return
                                   CRIAFLOOD.AML
                           «Remove title before running in GIS»

&if %.flooddata% = .TRUE. &then
 &do
  ap
  /******** 100 year flood
  res siteflood4 polys zone = 'A'
  statistics siteflood4 polys # fldl004stat

-------
sum area
end
&s fldl004area [show select fldl004stat info 1 item sum-area]
&s .fld!004 [calc [calc [calc %fldl004area% * 0.00000003587006] /%.area4mi%] * 100]
&if %.fldl004% It 20 &then
  &s .fld!004sc  1
&else
  &if %.fldl004% ge 20 and %.fld!004% It 30 &then
   &s.fld!004sc2
  &else
   &if %.fldl004% ge 30 and %.fld!004% It 40 &then
     &s.fld!004sc3
   &else
     &if %.fldl004% ge 40 and %.fld!004% It 50 &then
     &s.fld!004sc4
     &else
     &s .fld!004sc 5

res siteflood2 polys zone = 'A'
statistics siteflood2 polys # fld!002stat
sum area
end
&s fld!002area [show select fld!002stat info 1 item sum-area]
&s .fld!002 [calc [calc [calc %fldl002area% * 0.00000003587006] /%.area2mi%] * 100]
&if %.fldl002% It 20 &then
  &s .fldl002sc  1
&else
  &if %.fld!002% ge 20 and %.fldl002% It 30 &then
   &s.fldl002sc2
  &else
   &if %.fld!002% ge 30 and %.fldl002% It 40 &then
     &s.fldl002sc3
   &else
     &if %.fld!002% ge 40 and %.fldl002% It 50 &then
     &s.fldl002sc4
     &else
     &s .fldl002sc 5

/********500 year flood
clearsel
res siteflood4 polys zone = 'A' or zone = 'X500'

                                     C-28

-------
statistics siteflood4 polys # fld5004stat
sum area
end
&s fld5004area [show select fld5004stat info 1 item sum-area]
&s .fld5004 [calc [calc [calc %fld5004area% * 0.00000003587006] /%.area4mi%] * 100]
&if %.fld5004% It 20 &then
  &s .fld5004sc 1
&else
  &if %.fld5004% ge 20 and %.fld5004% It 30 &then
   &s .fld5004sc 2
  &else
   &if %.fld5004% ge 30 and %.fld5004% It 40 &then
     &s .fld5004sc 3
   &else
     &if %.fld5004% ge 40 and %.fld5004% It 50 &then
     &s .fld5004sc 4
     &else
     &s .fld5004sc 5

clearsel
res siteflood2 polys zone = 'A' or zone = 'X500'
statistics siteflood2 polys # fld5002stat
sum area
end
&s fld5002area [show select fld5002stat info 1 item sum-area]
&s .fld5002 [calc [calc [calc %fld5002area% * 0.00000003587006] /%.area2mi%] * 100]
&if %.fld5002% It 20 &then
  &s .fld5002sc 1
&else
  &if %.fld5002% ge 20 and %.fld5002% It 30 &then
   &s .fld5002sc 2
  &else
   &if %.fld5002% ge 30 and %.fld5002% It 40 &then
     &s .fld5002sc 3
   &else
     &if %.fld5002% ge 40 and %.fld5002% It 50 &then
     &s .fld5002sc 4
     &else
     &s .fld5002sc 5
                                      C-29

-------
 &end
&else
 &do
  &s .fld!004 -99
  &s.fldl004scO
  &s .fld!002 -99
  &s.fldl002scO
  &s .fld5004 -99
  &s .fld5004sc 0
  &s .fld5002 -99
  &s .fld5002sc 0
 &end

&return

                                 CRIALANDUSE.AML
                           «Remove title before running in GIS»

project cover sitebuff4 site4bna %.progpath%files\r6alb2natalb
/*build site4bna polys
clean site4bna # .00000000001 .00000000001
project cover sitebuff2 site2bna %.progpath%files\r6alb2natalb
/*build site2bna polys
clean site2bna# .000000000001 .00000000000001
grid
mape site4bna
setwindow site4bna
site4clgr = f:\nlcd\region6
q
gridpoly site4clgr sitelumape
/*build sitelumape polys
clean sitelumape # .0000000000001 .0000000000001

relate add
lu
%.progpath%lucodes
info
grid-code
grid-code
ordered
ro
                                          C-30

-------
dissolve sitelumape sitelumed lu//wlh poly
clip sitelumed site4bna site41ud poly
/*build site41ud polys
clean site41ud # .0000000000001  .00000000000001
clip sitelumed site2bna site21ud poly
/*build site21ud polys
clean site21ud # .0000000000001  .00000000000001

additem sitelumape.pat sitelumape.pat arearat 8 8 n 6
additem sitelumape.pat sitelumape.pat lucode 8 8 n 6
additem sitelumape.pat sitelumape.pat agland 8 8 n 6
additem sitelumape.pat sitelumape.pat wetlands 8 8 n 6

clip sitelumape site4bna site41u poly
/*build site41u polys
clean site41u # .0000000000000001 .000000000000001
clip site41u site2bna site21u poly
/*build site21u polys
clean site21u # .000000000000000001 .0000000000000001

ap
calculate site41u polys arearat = (area * 0.0000003861003 ) / %.area4mi%
calculate site41u polys lucode = arearat * lu//lurank
statistics site41u polys # site41ustat
sum lucode
end
&s .wlhlur4 [show select site41ustat info 1 item sum-lucode]
&if %.wlMur4% le 1 &then
 &s .wlhlur4sc =  1
&else
 &if %.wlMur4% gt 1  and %.wlhlur4% le 2 &then
   &s .wlhlur4sc = 2
 &else
   &if %.wlhlur4% gt 2 and %.wlhlur4% le 3 &then
    &s .wlhlur4sc = 3
   &else
    &if %.wlMur4% gt 3 and %.wlhlur4% le 4 &then
     &s .wlhlur4sc = 4
    &else
                                            C-31

-------
     &s .wlhlur4sc = 5

calculate site21u polys arearat = (area * 0.0000003861003 ) / %.area2mi%
calculate site21u polys lucode = arearat * lu//lurank
statistics site21u polys # site21ustat
sum lucode
end
&s .wlhlur2 [show select site21ustat info 1 item sum-lucode]
&if %.wlMur2% le  1 &then
 &s .wlhlur2sc = 1
&else
 &if %.wlMur2% gt 1 and %.wlhlur2% le 2 &then
   &s .wlhlur2sc = 2
 &else
   &if %.wlMur2%  gt 2 and %.wlhlur2% le 3 &then
    &s .wlhlur2sc = 3
   &else
    &if %.wlMur2% gt 3 and %.wlhlur2% le 4 &then
     &s .wlhlur2sc = 4
    &else
     &s .wlhlur2sc = 5

res site41ud polys wlh en 'Y'
statistics site41ud polys # site41udstat
sum area
sum perimeter
end
&s wlharea [show select site41udstat info 1 item sum-area]
&s wlhperim [show select site41udstat info 1 item sum-perimeter]
&s .wlhapr4 [calc %wlharea% / %wlhperim%]
&if %.wlhapr4% It  1 &then
  &s .wlhapr4sc =  1
&else
  &if %.wlhapr4%  ge 1  and %.wlhapr4% It 2 &then
   &s .wlhapr4sc = 2
  &else
   &if %.wlhapr4% ge 2 and %.wlhapr4% It 3 &then
    &s .wlhapr4sc = 3
   &else
    &if %.wlhapr4% ge 3 and %.wlhapr4% It 4 &then
      &s .wlhapr4sc = 4
                                           C-32

-------
    &else
     &s .wlhapr4sc = 5

&s .wlhper4 [calc [calc [calc %wlharea% * 0.0000003861003] / %.area4mi%] * 100]
&if %.wlhper4% It 20 &then
  &s .wlhper4sc = 1
&else
  &if %.wlhper4% ge 20 and %.wlhper4% It 30 &then
   &s .wlhper4sc = 2
  &else
   &if %.wlhper4% ge 30 and %.wlhper4% It 40 &then
    &s .wlhper4sc = 3
   &else
    &if %.wlhper4% ge 40 and %.wlhper4% It 50 &then
     &s .wlhper4sc = 4
    &else
     &s .wlhper4sc = 5

res site21ud polys wlh en 'Y'
statistics site21ud polys # site21udstat
sum area
sum perimeter
end
&s wlharea  [show select site21udstat info 1 item sum-area]
&s wlhperim [show select site21udstat info 1 item sum-perimeter]
&s .wlhapr2 [calc %wlharea% / %wlhperim%]
&if %.wlhapr2% It 1 &then
  &s .wlhapr2sc = 1
&else
  &if %.wlhapr2% ge 1 and %.wlhapr2% It 2 &then
   &s .wlhapr2sc = 2
  &else
   &if %.wlhapr2% ge 2 and %.wlhapr2% It 3 &then
    &s .wlhapr2sc = 3
   &else
    &if %.wlhapr2% ge 3 and %.wlhapr2% It 4 &then
     &s .wlhapr2sc = 4
    &else
     &s .wlhapr2sc = 5

&s .wlhper2 [calc [calc [calc %wlharea% * 0.0000003861003] / %.area2mi%] * 100]

                                         C-33

-------
&if %.wlhper2% It 20 &then
  &s .wlhper2sc = 1
&else
  &if %.wlhper2% ge 20 and %.wlhper2% It 30 &then
   &s .wlhper2sc  = 2
  &else
   &if %.wlhper2% ge 30 and %.wlhper2% It 40 &then
    &s .wlhper2sc = 3
   &else
    &if %.wlhper2% ge 40 and %.wlhper2% It 50 &then
     &s  .wlhper2sc = 4
    &else
     &s  .wlhper2sc = 5

/********Agriculture lands
clearsel
res site41u polys lu//grid-code ge 81 and lu//grid-code le 85
asel site41u polys lu//grid-code = 61
statistics site41u polys # site4agstat
sum area
end
&s agarea [show select site4agstat info 1 item sum-area]
&s .agper4 [calc [calc [calc %agarea% * 0.0000003861003] / %.area4mi%] * 100]
&if %.agper4% It 20 &then
  &s .agper4sc = 1
&else
  &if %.agper4% ge 20 and %.agper4% It 30 &then
   &s .agper4sc = 2
  &else
   &if %.agper4% ge 30 and %.agper4% It 40 &then
    &s .agper4sc  = 3
   &else
    &if %.agper4% ge 40 and %.agper4% It 50 &then
     &s  .agper4sc = 4
    &else
     &s  .agper4sc = 5

clearsel
res site21u polys lu//grid-code ge 81 and lu//grid-code le 85
asel site21u polys lu//grid-code = 61
statistics site21u polys # site2agstat

                                           C-34

-------
sum area
end
&s agarea [show select site2agstat info 1 item sum-area]
&s .agper2 [calc [calc [calc %agarea% * 0.0000003861003] / %.area2mi%] * 100]
&if %.agper2% It 20 &then
  &s .agper2sc = 1
&else
  &if %.agper2% ge 20 and %.agper2% It 30 &then
   &s .agper2sc = 2
  &else
   &if %.agper2% ge 30 and %.agper2% It 40 &then
    &s .agper2sc = 3
   &else
    &if %.agper2% ge 40 and %.agper2% It 50 &then
     &s .agper2sc = 4
    &else
     &s .agper2sc = 5


clearsel
res site41u polys lu//grid-code ge 91 and lu//grid-code le 92
statistics site41u polys # site4wetstat
sum area
end
&s wetarea [show select site4wetstat info 1 item sum-area]
&s .wetper4 [calc [calc [calc %wetarea% * 0.0000003861003] /%.area4mi%] * 100]
&if %.wetper4% It 20 &then
  &s .wetper4sc = 1
&else
  &if %.wetper4% ge 20 and %.wetper4% It 30 &then
   &s .wetper4sc = 2
  &else
   &if %.wetper4% ge 30 and %.wetper4% It 40 &then
    &s .wetper4sc = 3
   &else
    &if %.wetper4% ge 40 and %.wetper4% It 50 &then
     &s .wetper4sc = 4
    &else
     &s .wetper4sc = 5

clearsel

                                          C-35

-------
res site21u polys lu//grid-code ge 91 and lu//grid-code le 92
statistics site21u polys # site2wetstat
sum area
end
&s wetarea [show select site2wetstat info 1 item sum-area]
&s .wetper2 [calc [calc [calc %wetarea% * 0.0000003861003] /%.area2mi%] * 100]
&if %.wetper2% It 20 &then
  &s .wetper2sc = 1
&else
  &if %.wetper2% ge 20 and %.wetper2% It 30 &then
   &s .wetper2sc = 2
  &else
   &if %.wetper2% ge 30 and %.wetper2% It 40 &then
    &s .wetper2sc = 3
   &else
    &if %.wetper2% ge 40 and %.wetper2% It 50 &then
     &s .wetper2sc = 4
    &else
     &s .wetper2sc = 5

q
&return
                             CRIALOADTRACKER.AML
                          «Remove titie before running in GIS»

&data arc info
ARC
SEL TRACKCRIA
ADD
%.datename%
%.st%
%.coname%
%.cocode%
%.reqname%
%.mcode%
%.siteid%
%.namesite%
                                        C-36

-------
%.logdg%
%.logmn%
%.logsc%
%.latdg%
%.latmn%
%.latsc%
%.radiusl%
%.radius2%
%.swuse%
%.swuserr%
%.sto%
%.stor%
%.rain%
%.raini%
%.uwa%
%.aveflow%
%.aveflowr%
%.aqgeo%
%.dswsc%
%.nonatt%
%.ssaqu%
%.wtaq4%
%.wtaq4sc%
%.wtpy4%
%.wtpy4sc%
%.perm4%
%.gwp4%
%.wlhper4%
%.wlhper4sc%
%.wlMur4%
%.wlhlur4sc%
%.wlhapr4%
%.wlhapr4sc%
%.agper4%
%.agper4sc%
%.wetper4%
%.wetper4sc%
%.fld!004%
%.fld!004sc%
%.fld5004%
%.fld5004sc%
                                       C-37

-------
%.rdsq4%
%.rdsq4sc%
%.wtch4%
%.wtch4sc%
%.othersite4%
%.othfac4%
%.nohsdeg4%
%.nohsdeg4sc%
%.edusc4%
%.poor4%
%.poor4sc%
%.minority4%
%.minority4sc%
%.kids4%
%.kids4sc%
%.older4%
%.older4sc%
%.babes4%
%.babes4sc%
%.unemploy4%
%.unemploy4sc%
%.popdens4%
%.popdens4sc%
%.newpop4%
%.engabi!4%
%.engabi!4sc%
%.lingiso4%
%.lingiso4sc%
%.forborn4%
%.forborn4sc%
%.hseage4%
%.airlbs4%
%.airlbssc4%
%.wtrlbs4%
%.wtrlbssc4%
%.landlbs4%
%.landlbssc4%
%.airdi4%
%.airdisc4%
%.wtrdi4%
%.wtrdisc4%
                                        C-38

-------
%.wtaq2%
%.wtaq2sc%
%.wtpy2%
%.wtpy2sc%
%.perm2%
%.gwp2%
%.wlhper2%
%.wlhper2sc%
%.wlMur2%
%.wlMur2sc%
%.wlhapr2%
%.wlhapr2sc%
%.agper2%
%.agper2sc%
%.wetper2%
%.wetper2sc%
%.fld!002%
%.fld!002sc%
%.fld5002%
%.fld5002sc%
%.rdsq2%
%.rdsq2sc%
%.wtch2%
%.wtch2sc%
%.othersite2%
%.othfac2%
%.nohsdeg2%
%.nohsdeg2sc%
%.edusc2%
%.poor2%
%.poor2sc%
%.minority2%
%.minority2sc%
%.kids2%
%.kids2sc%
%.older2%
%.older2sc%
%.babes2%
%.babes2sc%
%.unemploy2%
%.unemploy2sc%
                                       C-39

-------
%.popdens2%
%.popdens2sc%
%.newpop2%
%.engabi!2%
%.engabi!2sc%
%.lingiso2%
%.lingiso2sc%
%.forborn2%
%.forborn2sc%
%.hseage2%
%.airlbs2%
%.airlbssc2%
%.wtrlbs2%
%.wtrlbssc2%
%.landlbs2%
%.landlbssc2%
%.airdi2%
%.airdisc2%
%.wtrdi2%
%.wtrdisc2%

QSTOP
&end
                                   CRIAMAIN.AML
                           «Remove title before running in GIS»

&arg loctype
&if [null %loctype%] &then
 &return & warning Usage: CRIAMAIN < coord cover >

&terminal 9999
&menupath \\r6gisntl\sharel\acs\j daniels\criaprogtest
&amlpath \\r6gisntl\sharel\acs\jdaniels\criaprogtest
&s .progpath \\r6gisntl\sharel\acs\jdaniels\criaprogtest\

&if %loctype% = coord &then
 &menu coord.menu &position &cc &stripe coord.menu
&if %loctype% = cover &then
 &menu cover.menu &position &cc &stripe cover.menu
                                          C-40

-------
&s gridlic = [before [after [show product grid],],]
&if %gridlic% le 1 &then
 product grid reserve
&else
 &do
  &popup %.progpath%files\noli censeavailable.txt
  &s .letsgo = Z
 &end

&if %.letsgo% = Y &then
 &do
  &if %loctype% = coord &then
   &call PROJECTFILE
  &if %loctype% = cover &then
   &call GETCOVERXY
  &call ERRORCHECK
  &if %.letsgo% = Y &then
   &call MAINWORK
 &end

&return

&routine MAINWORK
&if %loctype% = coord &then
 &call GENERATEPTCOVER
&if %loctype% = cover &then
 &call POLYBUFF
&call OVERLAPSTATE
&r criawatershed
&r criaaqui
&r criaenviro
&r criaclip
&r criadistance
&r criards
&r criawtrquan
&r criawtrchan
&r criawtrsurfa
&r criaflood
&r criastatsgo
&r crialanduse
product grid available
                                        C-41

-------
&r criacensus
&r criatri
&r crialoadtracker
&r criadelete
&dv*
&dv.*

&return

&routine PROJECTFILE
&s .logdg = 0 - [abs %.logdg%]
&s .longjat [quote %.logdg% %.logmn% %.logsc%  %.latdg% %.latmn% %.latsc%]
&s .daytime [date -vfull]
&s .datename [date -flag]
&s .llunit = [open lldms.txt .openll -write]
&if [write %.llunit% %.long_lat%] = 0 &then
 &do
  &type
  &type LAT/LONG IS BEING PROJECTED	
 &end
&else
 &type TfflS IS FAILING %.llunit%  %.openll%
&s closefile = [close %.llunit%]
/**projects location from degrees-minutes-seconds to albers feet.
project file lldms.txt llalb.txt %.progpath%dms2alb
&s .llunit = [open llalb.txt .openll -read]
&s readline = [read %.llunit% readst]
&s .x = [unquote [substr %readline% 721]]
&s .y = [unquote [substr %readline% 28 20]]
&s closefile = [close %.llunit%]
&if [exists lldms.txt -file] &then
 &s erase [delete lldms.txt -file]
&if [exists llalb.prj -file] &then
 &s erase [delete llalb.prj -file]
&if [exists llalb.txt -file] &then
 &s erase [delete llalb.txt -file]
&return

&routine GETCOVERXY
 createlabels %.covername%
 centroidlabels %.covername% inside
                                          C-42

-------
 addxy %.covername%
 ap
 res %.covername% polys %.covername%-id = 1
 &s .x [show select %.covername% poly 1 item x-coord]
 &s .y [show select %.covername% poly 1 item y-coord]
 q
&return

&routine ERRORCHECK
&if %.radiusl% > %.radius2% &then
 &s .letsgo = Y
&else
 &do
  &type	
  &type .
  &type .  The outer buffer is less than or equal to the
  &type .  inner buffer. It should be larger than the inner .
  &type .  buffer. Press "Enter" to restart.
  &type	
  &s .letsgo = Z
  &pause 'Press  to continue'
 &end

&if %.letsgo% = Y &then
 &do
  ap
  coo keyboard
  searchtolerance .00001
  mape \\r6gisntl\sharel\common\states
  res \\r6gisntl\sharel\common\states polys one %.x% %.y%
  &s .st [show select \\r6gisntl\sharel\common\states poly 1 item st]
  &s error = [before [show select \\r6gisntl\sharel\common\states polys],]
  res \\r6gisntl\sharel\common\r6cos polys one %.x% %.y%
  &s .cocode [show select \\r6gisntl\sharel\common\r6cos poly 1 item tile-name]
  &s .flooddata [exists \\r6gisntl\sharel\%.st%\tgr92\%.cocode%\flood -cover]
  coo cursor
  q
  &if %error% = 0 &then
   &do
    &type	
    &type .
                                          C-43

-------
     &type . The longitude and latitude entered does not fall  .
     &type . within the boundaries of Region 6.
     &type . Verify that the longitude/latitude were entered  .
     &type . correctly. If the data is correct as entered, then .
     &type . the site location will have to be verified. Press .
     &type. "Enter" to restart.
     &type	
     &s .letsgo = Z
     &pause 'Press  to continue'
   &end
  &else
   &do
     &s .errorcheck = 0
     &sys els
     &type	
     &type .
     &type . Site processing will now start	
     &type	
     &type
     &pause & seconds 5
     ap
     coo keyboard
     searchtolerance .00001
     mape \V6gi snt 1 \share I\common\r6cos
     res \\r6gisntl\sharel\common\r6cos polys one %.x% %.y%
     &s .cocode [show select \\r6gisntl\sharel\common\r6cos poly 1 item tile-name]
     &s .flooddata [exists \\r6gisntl\sharel\%.st%\tgr92\%.cocode%\flood -cover]
     coo cursor
     q
   &end
 &end
&return

&routine GENERATEPTCOVER
 &s .rad4 = [calc %.radiusl% * 5280]
 &s .rad2 = [calc %.radius2% * 5280]
 generate sitept
 point
 l,%.x%,%.y%
 end
 quit

                                          C-44

-------
 build sitept points
 generate sitebuff4
 circles
 end
 quit
 build sitebuff4 polys
 generate sitebufEZ
 circles
 end
 quit
 build sitebuff2 polys
 ap
 res sitebuff4 polys sitebufF4-id = 1
 &s .area4mi [calc [show select sitebuff4 poly 1 item area] * 0.00000003587006]
 res sitebuffZ polys sitebuff2-id = 1
 &s .area2mi [calc [show select sitebuffiZ poly 1 item area] * 0.00000003587006]
 q
&return

&routine POLYBUFF
 &s .logdg 0
 &s .logmn 0
 &s .logsc 0
 &s .latdg 0
 &s .latmn 0
 &s .latsc 0
 &s .daytime [date -vfull]
 &s .datename [date -flag]

 &s .rad4 = [calc %.radiusl% * 5280]
 &s .rad2 = [calc %.radius2% * 5280]

 buffer %.covername% sitebufF4 # # %.rad4% # poly /**** outer buffer
 &if %.radius2% =  .001 &then
  copy %.covername% sitebuff2
 &else
  buffer %.covername% sitebuffZ # # %.rad2% # poly /**** inner buffer

 ap

                                           C-45

-------
 res sitebuff4 polys sitebuff4-id = 1
 &s .area4mi [calc [show select sitebuff4 poly 1 item area] * 0.00000003587006]
 res sitebuffZ polys sitebuff2-id = 1
 &s .area2mi [calc [show select sitebuffi poly 1 item area] * 0.00000003587006]
 q
&return

&routine OVERLAPSTATE
 ap
 res \V6gisntl\sharel\common\states polys overlap sitebuff4 polys
 &s .statenum [before [show select \\r6gisntl\sharel\common\states polys],]
 &s xnum = 1
 &do &until %xnum% > %.statenum%
  &s .st%xnum% [show select \\r6gisntl\sharel\common\states poly %xnum% item st]
  &s xnum = %xnum% + 1
 &end
 q
&return

                                    CRIARDS.AML
                           «Remove title before running in GIS»
ap
statistics siterd4 arcs # rd4stat
sum length
end
&s rd41ength [show select rd4stat info 1 item sum-length]
&s .rdsq4 [calc [calc %rd41ength% / 5280] / %.area4mi%]
&if %.rdsq4% It 1.20 &then
  &s .rdsq4sc 1
&else
  &if %.rdsq4% ge 1.20 and %.rdsq4% It 1.80 &then
   &s .rdsq4sc 2
  &else
   &if %.rdsq4% ge 1.80 and %.rdsq4% It 2.20 &then
     &s .rdsq4sc 3
   &else
     &if %.rdsq4% ge 2.20 and %.rdsq4% It 2.60 &then
      &s .rdsq4sc 4
     &else

                                          C-46

-------
      &s .rdsq4sc 5

statistics siterd2 arcs # rd2stat
sum length
end
&s rd21ength [show select rd2stat info 1 item sum-length]
&s .rdsq2 [calc [calc %rd21ength% / 5280] / %.area2mi%]
&if %.rdsq2% It 1.20 &then
  &s .rdsq2sc 1
&else
  &if %.rdsq2% ge 1.20 and %.rdsq2% It 1.80 &then
   &s .rdsq2sc 2
  &else
   &if %.rdsq2% ge 1.80 and %.rdsq2% It 2.20 &then
     &s .rdsq2sc 3
   &else
     &if %.rdsq2% ge 2.20 and %.rdsq2% It 2.60 &then
      &s .rdsq2sc 4
     &else
      &s .rdsq2sc 5

q
&return
                                 CRIASTATSGO.AML
                           «Remove title before running in GIS»

&do rad &list 2 4
 additem stats%rad%.pat stats%rad%.pat arearat 8 8 n 6
 additem stats%rad%.pat stats%rad%.pat gwpmod 8 8 n 6
 additem stats%rad%.pat stats%rad%.pat spmmod 8 8 n 6
&end
ap
relate add
soils
%.progpath%soils
info
muid
muid
ordered

                                         C-47

-------
ro

&do rad &list 2 4
 res stats%rad% polys muid nc W
 statistics stats%rad% polys # soil%rad%statsarea
 sum area
 end
 &s studyarea [show select soil%rad%statsarea info 1 item sum-area]
 &s studyarea [calc %studyarea% * 0.00000003587006]
 calculate stats%rad% polys arearat = (area * 0.00000003587006 ) / %studyarea%
 res stats%rad% polys soils//gwprob > 0
 calculate stats%rad% polys gwpmod = soils//gwprob * arearat
 nsel stats%rad% polys
 calculate stats%rad% polys gwpmod = 1 * arearat
 clearsel
 res stats%rad% polys soils//perm > 0
 calculate stats%rad% polys spmmod = soils//perm *  arearat
 nsel stats%rad% polys
 calculate stats%rad% polys spmmod = 1 * arearat
 clearsel
 res stats%rad% polys muid nc W
 statistics stats%rad% polys # soil%rad%stats
 sum gwpmod
 sum spmmod
 end
 &s .gwp%rad% [show select soil%rad%stats info 1 item sum-gwpmod]
 &s .perm%rad% [show select soil%rad%stats info 1 item sum-spmmod]
&end

q
&return
                                    CRIATRI.AML
                           «Remove title before running in GIS»

ap
mape sitebuff4

relate add
tri
                                          C-48

-------
%.progpath%r6tri98rel
info
tri_id
tri_id
ordered
ro
&do rad &list 4 2
res %.progpath%r6tri98 points mape
res %.progpath%r6tri98 points overlap sitebuff%rad% polys
statistics %.progpath%r6tri98 points # tristat
sum tri//air
sum tri//water
sum tri//diair
sum tri//diwtr
sum tri//othland
end

&s airl [show select tristat info 1 item sum-air]
&s wtrl [show select tristat info 1 item sum-water]
&s aird [show select tristat info 1 item sum-diair]
&s wtrd [show select tristat info 1 item sum-diwtr]
&s landl [show select tristat info 1 item sum-othland]

&do type &list airlbs wtrlbs airdi wtrdi landlbs
 &if %type% = airlbs &then
   &do
   &samt = %airl%
   &s .airlbs%rad% = %airl%
   &end
 &if %type% = wtrlbs &then
   &do
   &samt = %wtrl%
   &s .wtrlbs%rad% = %wtrl%
   &end
 &if %type% = airdi &then
   &do
   &s amt = %aird%
   &s .airdi%rad% = %aird%
   &end

                                           C-49

-------
 &if %type% = wtrdi &then
  &do
   &s amt = %wtrd%
   &s .wtrdi%rad% = %wtrd%
  &end
 &if %type% = landlbs &then
  &do
   &s amt = %landl%
   &s .landlbs%rad% = %landl%
  &end

 &if %amt% le 300000 &then
  &s .%type%sc%rad% 1
 &else
  &if %amt% gt 300000 and %amt% le 1000000 &then
   &s .%type%sc%rad% 2
  &else
   &if %amt% gt 1000000 and %amt% le 2000000 &then
    &s .%type%sc%rad% 3
   &else
    &if %amt% gt 2000000 and %amt% le 5000000 &then
      &s .%type%sc%rad% 4
    &else
      &s .%type%sc%rad% 5

 clearsel
 &if [exist tristat -info] &then
  &s erase [delete tristat -info]
&end
&end
q
&return
                             CRIAWATERSHED.AML
                         «Remove title before running in GIS»

ap
coo keyboard
mape sitebuff4
res \\r6gisntl\sharel\common\r6cos polys one %.x% %.y%

                                       C-50

-------
&s .coname [quote [show select \\r6gisntl\sharel\common\r6cos poly 1 item county]]
&s .cocode [show select \V6gisntl\sharel\common\r6cos poly 1 item stcofips]
res %.progpath%nonattain info stcofips en [quote %.cocode%]
&s .nonatt [show select %.progpath%nonattain info 1 item nonattain]
res \V6gisntl\sharel\common\hydro\r6clip polys one %.x% %.y%
&s .hucode [show select \V6gisntl\sharel\common\hydro\r6clip poly 1  item huc8]
res %.progpath%hucscores info huc8 en [quote %.hucode%]
&s .swuse [show select %.progpath%hucscores info 1 item swuse]
&s .swuserr [show select %.progpath%hucscores info 1 item swuserr]
&s .sto [show select %.progpath%hucscores info 1 item sto]
&s .stor [show select %.progpath%hucscores info 1 item stor]
&s .rain [show select %.progpath%hucscores info 1 item rainfall]
&s .rainr [show select %.progpath%hucscores info  1 item rainr]
&s .uwa [show select %.progpath%hucscores info 1 item uwa]
&s .aveflow [show select %.progpath%hucscores info 1 item aveflow]
&s .aveflowr [show select %.progpath%hucscores info 1 item aveflow_r]
&s .aqgeo [show select %.progpath%hucscores info 1 item aqgeo]
&s .hucname [show select %.progpath%hucscores info 1 item hucname]
q
&return

                                 CRIAWTRCHAN.AML
                           «Remove title before running in GIS»
ap
res sitewtar4 arcs cfcc en 'H20' or cfcc en 'H21' or cfcc en 'H22'
statistics sitewtar4 arcs # wtarch4stat
sum length
end
&s wt41ength [show select wtarch4stat info 1 item sum-length]
&s .wtch4 [calc [calc %wt41ength% / 5280] / %.area4mi%]
&if %.wtch4% It 0.917 &then
  &s .wtch4sc 1
&else
  &if %.wtch4% ge 0.917 and %.wtch4% It 1.15 &then
   &s .wtch4sc 2
  &else
   &if %.wtch4% ge 1.15 and %.wtch4% It 1.43 &then
     &s .wtch4sc 3
   &else

                                          C-51

-------
     &if %.wtch4% ge 1.43 and %.wtch4% It 1.70 &then
      &s .wtch4sc 4
     &else
      &s .wtch4sc 5

res sitewtar2 arcs cfcc en 'H20' or cfcc en 'H21' or cfcc en 'H22'
statistics sitewtar2 arcs # wtarch2stat
sum length
end
&s wt21ength [show select wtarch2stat info 1 item sum-length]
&s .wtch2 [calc [calc %wt21ength% / 5280] / %.area2mi%]
&if %.wtch2% It 0.917 &then
  &s .wtch2sc 1
&else
  &if %.wtch2% ge 0.917 and %.wtch2% It 1.15 &then
   &s .wtch2sc 2
  &else
   &if %.wtch2% ge 1.15 and %.wtch2% It 1.43 &then
     &s .wtch2sc 3
   &else
     &if %.wtch2% ge 1.43 and %.wtch2% It 1.70 &then
      &s .wtch2sc 4
     &else
      &s .wtch2sc 5

q
&return
                                 CRIAWTRQUAN.AML
                           «Remove title before running in GIS»

ap
statistics sitewtar4 arcs # wtar4stat
sum length
end
&s wt41ength [show select wtar4stat info 1 item sum-length]
&s .wtaq4 [calc [calc %wt41ength% / 5280] / %.area4mi%]
&if %.wtaq4% It 0.917 &then
  &s .wtaq4sc 1
&else

                                          C-52

-------
  &if %.wtaq4% ge 0.917 and %.wtaq4% It 1.15 &then
   &s .wtaq4sc 2
  &else
   &if %.wtaq4% ge 1.15 and %.wtaq4% It 1.43 &then
    &s .wtaq4sc 3
   &else
    &if %.wtaq4% ge 1.43 and %.wtaq4% It 1.70 &then
     &s .wtaq4sc 4
    &else
     &s .wtaq4sc 5

statistics sitewtar2 arcs # wtar2stat
sum length
end
&s wt21ength [show select wtar2stat info 1 item sum-length]
&s .wtaq2 [calc [calc %wt21ength% / 5280] / %.area2mi%]
&if %.wtaq2% It 0.917 &then
  &s .wtaq2sc 1
&else
  &if %.wtaq2% ge 0.917 and %.wtaq2% It 1.15 &then
   &s .wtaq2sc 2
  &else
   &if %.wtaq2% ge 1.15 and %.wtaq2% It 1.43 &then
    &s .wtaq2sc 3
   &else
    &if %.wtaq2% ge 1.43 and %.wtaq2% It 1.70 &then
     &s .wtaq2sc 4
    &else
     &s .wtaq2sc 5

q
&return
                                CRIAWTRSURFA.AML
                          «Remove title before running in GIS»
ap
res sitewtpy4 polys wid = 99
statistics sitewtpy4 polys # wtpy4stat

                                         C-53

-------
sum area
end
&s wt4area [show select wtpy4stat info 1 item sum-area]
&s .wtpy4 [calc [calc [calc %wt4area% * 0.00000003587006] /%.area4mi%] * 100]
&if %.wtpy4% It 10 &then
  &s .wtpy4sc 1
&else
  &if %.wtpy4% ge 10 and %.wtpy4% It 20 &then
   &s .wtpy4sc 2
  &else
   &if %.wtpy4% ge 20 and %.wtpy4% It 30 &then
     &s .wtpy4sc 3
   &else
     &if %.wtpy4% ge 30 and %.wtpy4% It 40 &then
     &s .wtpy4sc 4
     &else
     &s .wtpy4sc 5

res sitewtpy2 polys wid = 99
statistics sitewtpy2 polys # wtpy2stat
sum area
end
&s wt2area [show select wtpy2stat info 1 item sum-area]
&s .wtpy2 [calc [calc [calc %wt2area% * 0.00000003587006] /%.area2mi%] * 100]
&if %.wtpy2% It 10 &then
  &s .wtpy2sc 1
&else
  &if %.wtpy2% ge 10 and %.wtpy2% It 20 &then
   &s .wtpy2sc 2
  &else
   &if %.wtpy2% ge 20 and %.wtpy2% It 30 &then
     &s .wtpy2sc 3
   &else
     &if %.wtpy2% ge 30 and %.wtpy2% It 40 &then
     &s .wtpy2sc 4
     &else
     &s .wtpy2sc 5

q
&return
                                        C-54

-------
 APPENDIX D
Peer Review Log

-------
                                 GISST Peer Review Log
The following is a rough timeline of GISST activities since its inception in 1996.  In addition, Figure D-l
shows the locations of projects that used GISST.
1996


1997


1997


3/14/97


4/25/97

4/30/97


5/22/97

5/2S/-6/5/97


7/10/97

7/14/97

9/3/97


12/5/97

1998
1/6/98
Regional Administrator (Saginaw) requests some way of assessing the impacts
of multiple CAFOs

CRIA developed with in-house expertise
Roll out to get industry and citizen group comments

Initial development and review of criteria by internal experts
Presented at Conference on Environmental Decision-making (San Antonio)

Presentation to Sherri Goodman (Undersec Env. Security) on GIS
Screening models

Procurement Request for GIS services for Federal Facilities Project

Formulation of objectives and letter asking for R6 workgroup members  for
Federal Facilities Project

Memo requesting members from each Division for Federal Facilities Project

Notification of Division reps to workgroup for Federal Facilities Project
(FRIA)

R6 Workgroup: Criteria development meeting

Email asking for review of FRIA methodology draft

Presentation on FRIA methodology to Federal Facilities Community, informal
request for comments

Second Draft FRIA methodology for review

CRIA applied to several NPDES New Source permits for CAFOS
Environmental Assessments. FNSI's are not  signed until company agrees to
further water monitoring (based on red flags identified by CRIA process)

Presentation to Federal Facilities Community  at EJ/TRI meeting, 2nd request for
                                           D-l

-------
                     comments
7/14/98

8/18/98

9/18/98

10/31/98

11/10/98

12/4/98
1/6/99


2/23/99

2/25/99

5/13/99


6/14/99

8/31/99

11/2/99

11/24/99

12/10/99

10/1999

9/2000
Letter toSherri Goodman (DOD) requesting comments on FRIA methodology

Start calculation of FRIA scores using GIS

FRIA Degree of Vulnerability results

Draft Report of FRIA results for DOD/DOE facilities

Request for comments on FRIA results report

Meeting with DOD to present FRIA methodology and calculations in FRIA
report. Request for comments on FRIA methodology.  Based on comments in
this meeting, several new criteria were suggested

Development of additional criteria. Production of Impact "blanks" for DOD
verification

Revision of FRIA criteria to include P2 & energy criteria

R6 staff review revised criteria

Presentation to Federal Facilities community of FRIA report results, request for
comments

OIG Report No 1999-P-209

FRIA methods/results presentation to DOD

Presentation to Fort Polk

Meeting to discuss further action by DOD on FRIA

Presented FRIA as an additional tool for Pollution Prevention Partnership

CAFO CRIA Conceptual Model

Project w/ Ft. Polk effectively ends due to attenuation.
EPA notes violations of nitrates in CAFO  monitoring wells in OK and initiated
enforcement actions
                                           D-2

-------
2000

1/21/00


6/21/00

8/2000

2001
1/2001

1/2001

3/2001


4/2001
5/2001

8/2001

9/2001


11/2001

2002
OSU student project using CRIA (including habitat fragmentation portion)

Videoconference with HQ EPA & Pentagon to discuss DOD further
participation

Conference call w/Ft Bliss, Ft Polk to discuss pilot/ refinement

SMU Peer Review of EJ index

Lantana peer review  panel provides several recommendations based on review
of CRIA and HRI products
Interest by staff working with COE (Jana Harvill) and transportation
(Dominique Lueckenhoff)
EN-XP committee established to determine a process for updating and refining
CRIA
Based on EN-XP feedback, the decision is made to go with a more general
acronym (GISST)
CRIA/GISST marketed to COE, but because of their organization, interest
wanes.

Innovations Award Submission

Environmental Monitoring and Assessment Journal Article

GISST marketed heavily to transportation community. FHWA and TxDOT
contract with EPA to provide GISST data for IH69

OFA NEPA-GIS workshop-GISST presented along with other regional tools
(Regions 2 and 4) EPA Region 6 is considered on the cutting edge of the
development of geospatial tools (along with Regions 4 and 5)

Baylor University does study of GIS data for N. Bosque

Meeting with FWS to introduce GISST and determine interest

Lantana Peer review  panel evaluates Mustang-Tuttle and enforcement
Targeting projects

OFA GIS technical conference

Work on updating the "CRIA data criteria library," to  address the Lantana peer
                                           D-3

-------
2002

5/2002


6/2002


9/2002

12/2002

2/2003

3/2003

4/2003


5/2003

6/2003

6/2003

7/2003

11/2003


12/2003

5/2004

5/2004
review panel comments, begins ultimately resulting in the GISST User's
Manual: involves standardizing criterion descriptions and math, verifying
references and databases, requesting review by listed contacts for each criteria,
separating out "final" from "provisional" criteria, developing case studies,
factsheets, and other marketing products (miniposters, tutorial, and CD).

Internal EPA expert review of criteria to determine updates

IH69 General Engineering Contractors Meeting to present GISST method and
results

Presentation of GISST and ORD/Regional Critical Ecosystem Workshop,
Keystone CO

Presentation to OPEI

Presentation to Pecora Conference, Denver CO

GISST Users Manual Revision/review of criteria

GISST Users Manual Final

Presentation of GISST and results to LBJ National Historic Site, Austin, TX,
San Antonio Missions, San Antonio, TX, and Padre Island National Seashore.

Presentation of GISST to DOI Conference on the Environment, Pheonix, AZ

EPA-State Environmental Innovation Symposium, DC

GISST Users Manual Update

GISST Users Manual Animated ppt demo

Presentation of GISST method and results to IH69 Technical Advisory
Committee/Steering Committee meeting

Presentation toOEI National Meeting, Phoenix, AZ

GISST Users Manual Update

Submission of GISST for SAB review
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6/2004

6/2004


7/2004

8/2004


2/2005

3/2005

9/2005

10/2005

10/2005

11/2005
Presentation to FHWA 2004 Environmental Conference, Alexandria, VA

Presentation of TEAP and GISST to North Texas Association of
Enivronmental Professionals, Dallas

Collaborations visit by EPA Chief of Staff McKeown, Dallas

Presentation of TEAP/GISST results to IH69 Technical Advisory
Committee/Steering Committee meeting

Briefing to new R6 Divison Director

Texas Ecological Assessment Protocol results incorporated into GISST

Technology Transfer Agreement with TXDOT to transfer GISST technology

Revision of GISST User's Manual, update of case studies

Seminar on TEAP  and GISST at Baylor University

Presentation to Green Highways Initiative College Park, MD
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Figure D-1
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   APPENDIX E
 Additional Maps and
Individual SIU 3 Maps

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Figure E-1.
            E-l

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Figure E-2.
            E-2

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Figure E-3.
            E-3

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Figure E-4.
            E-4

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Figure E-5.
            E-5

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Figure E-6.
            E-6

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Figure E-7.
            E-7

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Figure E-8.
            E-S

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Figure E-9.
            E-9

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Figure E-10.
           E-10

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Figure E-11.
           E-ll

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Figure E-12.
           E-12

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Figure E-13.
           E-13

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Figure E-14.
           E-14

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Figure E-15.
           E-15

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Figure E-16.
           E-16

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Figure E-17.
           E-17

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Figure E-18.
           E-18

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Figure E-19.
           E-19

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Figure E-20.
           E-20

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Figure E-21
           E-21

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Figure E-22.
           E-22

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Figure E-23.
           E-23

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Figure E-24.
           E-24

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Figure E-25.
           E-25

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