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
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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
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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
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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
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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
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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.
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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.
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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.
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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).
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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.
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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..
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Figure
30
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Figure 2
31
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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
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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
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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
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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
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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
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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.
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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.
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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
-------
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.
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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.
Burris, R. K. and L. W. Canter. 1997. Cumulative impacts are not properly addressed in
environmental assessments. Environmental Impact Assessment Review 17:5-18.
Canter, L. W. and R. Clark. 1997. NEPA effectiveness-a survey of academics. Environmental
Impact Assessment Review 17:313-327.
Canter, L. W. and J. Kamath. 1995. Questionnaire checklist for cumulative impacts.
Environmental Impact Assessment Review 15:311-339.
Caruso, B. S. and R. C. Ward. 1998. Assessment of nonpoint source pollution from inactive mines
using a watershed-based approach. Environmental Management 22:225-243.
78
-------
Cederstrand, J. and A. Rea. 1995. Watershed boundaries for Oklahoma. OFR 95-727. U.S.
Geological Survey, Oklahoma City, OK.
Clevenger, A. P., J. Wierzchowski, B. Chruszcz, and K. Gunson. 2002. GIS-generated, expert-
based models for identifying wildlife habitat linkages and planning mitigation passages.
Conservation Biology 16:503-514
Costanza, R. and M. Ruth. 1998. Using dynamic modeling to scope environmental problems and build
consensus. Environmental Management 22:183-195
Cox, L. H. and W. W. Piegorsch. 1996. Combining environmental information. L: Environmental
monitoring, measurement, and assessment. Environmetrics 7:299-208.
Dale, H., A. W. King, L. K. Mann, R. A. Washington-Allen, and R. A. McCord. 1998. Assessing
land-use impacts on natural resources. Environmental Management 22:203-211.
Dale, V. H., R. V. O'Neill, F. Southworth, and P. Pedlowski, 1994. Modeling effects of land
management in the Brazilian Amazonian settlement of Rondonia. Conservation Biology
8:196-206.
79
-------
Dickert, T. G. and A. E. Tuttle. 1985. Cumulative impact assessment in environmental planning: a
coastal wetland watershed example. Environmental Impact Assessment Review 5:37-64.
EPA 1999. Guidance for performing aggregate exposure and risk assessments.
Espejel, I, D. W. Fischer, A. Hinojosa, C. Garcia, and C. Levya. 1999. Land use planning for the
Guadalupe Valley, Baja California, Mexico. Landscape and Urban Planning 45:219-232.
Howard, D. C. and R. G. H. Bunce. 1996. The countryside information system: a strategic-level
decision support system. Environmental Monitoring and Assessment 39:373-384.
Iverson, L. R., D. L. Szafoni, S. E. Baum, E. A. Cook. 2001. A riparian wildlife habitat evaluation
scheme developed using GIS. Environmental Management 28:639-654.
Ji, W. and P. Leeberg. 2002. A GIS-based approach for assessing the regional conservation status of
genetic diversity: an example from the southern Appalachians. Environmental Management
29:531-544
Jones, K. B., A. C. Neale, M. S. Nash, R. D. Van Remortel, J. D. Wickham, K. H. Riitters, and R. V.
O'Neill. 2001. Predicting nutrient and sediment loadings to streams from landscape metrics: a
80
-------
multiple watershed study from the United States Mid-Atlantic Region. Landscape Ecology
16:301-312
Kahn, A. E. 1966. The tyranny of small decisions: market failures, imperfections, and the limits of
economics. KYKLOS 19:23-45.
Karydis, M. 1996. Quantitative assessment of eutrophication: a scoring system for characterizing
water quality in coastal marine ecosystems. Environmental Monitoring and Assessment
41:233-246
Laskowski, S. L. and F. W. Kutz. 1998. Environmental data in decision making in EPA regional
offices. Environmental Monitoring and Assessment 51:15-21.
Leibowitz, S. G., C. Loehle, B-L. Li, and E. M. Preston. 2000. Modeling landscape functions and
effects: a network approach. Ecological Modelling 132:77-94
McCold, M. and J. W. Saulsbury. 1996. Including past and present impacts in cumulative impact
assessments. Environmental Management 20:767-776.
Miller, W., M. Collins, F. Steiner, and E. Cook. 1998. An approach for greenway suitability analysis.
Landscape and Urban Planning 42:91-105.
81
-------
Montgomery, D. R., G. E. Grant, and K. Sullivan. 1995. Watershed analysis as a framework for
implementing ecosystem management. Water Resources Bulletin 31:369-385.
Moschandreas, D. J. and S. Karuchit. 2002. Scenario-model-parameter: a new method of cumulative
risk uncertainty analysis. Environment International 28:247-261
Mysz, A. T., C. G. Maurice, R. F. Beltran, K. A. Cipollini, J. P. Perrecone, K. M. Rodriguez, and M.
L. White. 2000. A targeting approach for ecosystem protection. Environmental Science
and Policy 3:347'-35
Odum, W. E., 1982. Environmental degradation and the tyranny of small decisions. BioScience
32:728-729.
O'Neill, R. V., K. H. Riitters, J. D. Wickham, and K. B. Jones. 1999. Landscape pattern metrics and
regional assessment. Ecosystem Health 5:225-233
Osowski, S. L., J. D. Swick, Jr., G. R. Carney, H. B. Pena, J. E. Danielson, and D. A. Fairish. 2001.
A watershed-based cumulative risk impact analysis: environmental vulnerability and impact
criteria. Environmental Monitoring and Assessment 66:159-185
82
-------
Partidario, M. R. 1996. Strategic environmental assessment: key issues emerging from recent practice.
Environmental Impact Assessment Review 16:31-55.
Peccol, E., C. A. Bird, and T. R. Brewer. 1996. GIS as a tool for assessing the influence of
countryside designations and planning policies on landscape change. Journal of
Environmental Management 47:355-367.
Piegorsch, W. W. and L. H. Cox. 1996. Combining environmental information. II: Environmental
epidemiology and toxicology Environmetrics 7:309-324.
Rees, W. E. 1995. Cumulative environmental assessment and global change. Environmental Impact
Assessment Review 15:295-309.
Serveiss, V. B. 2002. Applying ecological risk principles to watershed assessment and management.
Environmental Management 29:145-154.
Smits, B. and H. Spaling. 1995. Methods for cumulative effects assessment. Environmental Impact
Assessment Review 15:81-106.
Stakhiv, E. Z. 1988. An evaluation paradigm for cumulative impact analysis. Environmental
Management 12:725-748
83
-------
Steiner, F., J. Blair, L. McSherry, S. Guhathakurta, J. Marruffo, and M. Holm. 2000a. A watershed
at a watershed: the potential for environmentally sensitive area protection in the upper San
Pedro Drainage Basin (Mexico and USA). Landscape and Urban Planning 49:129-148
Steiner, F., L. McSherry, and J. Cohen. 2000b. Land suitability analysis for the upper Gila River
watershed. Landscape and Urban Planning 50:199-214
Store, R. and J. Kangas. 2001. Integrating spatial multi-criteria evaluation and expert knowledge for
GIS-based habitat suitability modeling. Landscape and Urban Planning 55:79-93.
Sullivan, W. C., F. E. Kuo, and M. Prabhu. 1996. Assessing the impact of environmental impact
statements on citizens. Environmental Impact Assessment Review 16:171-182.
Suter, G. W. 1993. A critique of ecosystem health concepts and indices. Environmental
Toxicology and Chemistry 12:1533-1539.
Theobald, D. M., J. R. Miller, and N. T. Hobbs. 1997. Estimating the cumulative effects of
development on wildlife habitat. Landscape and Urban Planning 39:25-36
Theobald, D. M., N. T. Hobbs, T. Bearly, J. A. Zack, T. Shenk, and W. E. Riebsame. 2000.
84
-------
Incorporating biological information in local land-use decision making: designing a system for
conservation planning. Landscape Ecology 15:3 5-45.
Tinker, D. B., C. A. C. Resor, G. P. Beauvais, K. F. Kipfmueller, C. I. Fernandes, and W. L. Baker.
1998. Watershed analysis of forest fragmentation by clearcuts and roads in a Wyoming forest.
Landscape Ecology 13:149-165.
TNRCC. 1996. The statewide water shed management approach for Texas-a guidance manual
for TNRCCs Office of Water Resource Management. August 29 Draft.
Iran, L. T., C. G. Knight, R. V. O'Neill, E. R. Smith, K. H. Riitters, and J. Wickham. 2002. Fuzzy
decision analysis for integrated environmental vulnerability assessment of the Mid-Atlantic
Region. Environmental Management 29:845-859.
Treweek, J. and N. Veitch. 1996. The potential application of GIS and remotely sensed data to the
ecological assessment of proposed new road schemes. Global Ecology and Biogeography
Letters 5:249-257.
Wang, X. and Z-Y. Yin. 1997. Using GIS to assess the relationship between land use and water
quality at a watershed level. Environment International 23:103-114.
85
-------
Wickham, J. D., K. B. Jones, K. H. Riitters, R. V. O'Neill, R. D. Tankersley, E. R. Smith, A. C.
Neale, and D. J. Chaloud. 1999. An integrated environmental assessment of the Mid-Atlantic
Region. Environmental Management 24:553-560.
Xiang, W-N. 2001. Weighting-by-choosing: a weight elicitation method for map overlay.
Landscape and Urban Planning 56:61-73
Zhang, M., S. Geng, and S. L. Ustin. 1998. Quantifying the agricultural landscape and assessing
spatio-temporal patterns of precipitation and groundwater use. Landscape Ecology 13:3 7-5 3.
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
A-10
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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
A-ll
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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
A-12
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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
A-25
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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
A-26
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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
A-27
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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
A-28
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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
A-29
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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,
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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
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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
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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
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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
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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
A-51
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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
A-52
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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
A-53
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
&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
D-4
-------
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
D-5
-------
Figure D-1
D-6
-------
APPENDIX E
Additional Maps and
Individual SIU 3 Maps
-------
Figure E-1.
E-l
-------
Figure E-2.
E-2
-------
Figure E-3.
E-3
-------
Figure E-4.
E-4
-------
Figure E-5.
E-5
-------
Figure E-6.
E-6
-------
Figure E-7.
E-7
-------
Figure E-8.
E-S
-------
Figure E-9.
E-9
-------
Figure E-10.
E-10
-------
Figure E-11.
E-ll
-------
Figure E-12.
E-12
-------
Figure E-13.
E-13
-------
Figure E-14.
E-14
-------
Figure E-15.
E-15
-------
Figure E-16.
E-16
-------
Figure E-17.
E-17
-------
Figure E-18.
E-18
-------
Figure E-19.
E-19
-------
Figure E-20.
E-20
-------
Figure E-21
E-21
-------
Figure E-22.
E-22
-------
Figure E-23.
E-23
-------
Figure E-24.
E-24
-------
Figure E-25.
E-25
------- |