James Kuiper
Producing a Programmatic Environmental Impact Statement for a Large Federal Facility: A GIS Technical Leader's Perspective
To be presented at the Sixteenth Annual Esri User
Conference,
Palm Springs, California, USA
May 20-24, 1996
Sponsored by Environmental Systems Research Institute
(Esri)
ABSTRACT
Producing a programmatic Environmental Impact Statement (EIS) for a large federal facility requires consideration of a wide range of activities, collection of an extensive amount of data, and analysis and modeling to determine the nature and extent of potential environmental impacts. EIS documents provide the most detailed analyses of federal facilities required by the National Environmental Policy Act of 1969. An extensive, environmentally focused Geographic Information System (GIS) was developed and used in the analyses, modeling, and mapping for an EIS of a federal facility with an area of more than 100 square miles. The final products of the EIS process will include a printed document with more than 250 GIS-produced maps, CD-ROM versions of both the document and the GIS metadata dictionary, and an environmentally focused GIS that will form a baseline of information for the facility. The environmental GIS will augment the installation's existing infrastructure-related GIS.
INTRODUCTION
Since the 1970s, one of the central activities of the Environmental Assessment Division (EAD) of Argonne National Laboratory (ANL) has been to analyze federal facilities for compliance with environmental regulations. The National Environmental Policy Act of 1969 (NEPA 1969) is the primary legislation mandating this work. NEPA requires an Environmental Impact Statement (EIS) to be produced for major proposed federal actions that have the potential to significantly affect the environment. EISs are intended to inform the public and decision makers about the potential environmental consequences of the proposed actions and their alternatives. The EIS process requires analysis of a broad range of issues such as land use, waste management, soils, air and water quality, ecology, noise, radiation, cultural resources, recreation, visual resources, transportation and traffic, socioeconomics, and environmental justice.
The ANL-EAD scientific staff form a multidisciplinary team with experts in the areas necessary for NEPA analysis. The Geographic Information System (GIS) and related technologies have been effective in supporting the diverse data automation, analysis, modeling, and reporting tasks for these projects. The Spatial Analysis Laboratory was formed within EAD to provide these services.
At large federal facilities, NEPA compliance presents a complex challenge to maintain mission goals, protect the environment, promote a positive public image, and avoid litigation in an efficient and cost-effective manner. Frequently, EISs and other NEPA analyses are conducted on a project-by-project basis, often by separate organizations at the facility. This can be inefficient, with repetitive analysis of a wide variety of issues for many separate actions. Such an incremental approach can also overlook the aggregate and cumulative effects of the combined actions.
Example EIS Project: Aberdeen Proving Ground
This paper focuses on the programmatic EIS that is being produced for Aberdeen Proving Ground (APG). APG is a large U.S. Army facility on Chesapeake Bay in Maryland, with an area exceeding 100 square miles. Primary mission activities are the testing of ordnance, military vehicles, and other military equipment. APG is the site of the Army's primary facility to train mechanical maintenance specialists, and of the Maryland National Guard. More than 70 tenant organizations and 300 mission activities are located at APG. The facility has existed for about 80 years. Throughout that time, APG has been protected from the effects of urbanization and similar human disturbance that have affected adjacent lands. However, some testing activities at APG have had detrimental effects on the environment. Overall, the installation has maintained appropriate stewardship of natural and cultural resources.
Many hundreds of environmental documents have been prepared by APG since 1969 for regulatory compliance. Most focused on an individual action or activity, and did not comprehensively consider interactions with the other activities or actions at the installation. In the programmatic EIS that is under way, ongoing and future activities at APG are analyzed as a whole and cumulative effects are considered. This approach will result in a baseline of environmental data and analyses that can be referenced in future documents at a substantial cost savings. A significant component of creating this baseline is establishing an environmentally focused GIS for APG.
GIS DEVELOPMENT PROCESS
Diverse Data Sources / Environmental Focus
The GIS took about a year to develop. For efficiency, development focused on using existing sources, rather than collection of new data. We found that a tremendous amount of useful information existed at the facility, and that the main challenge was to locate and integrate it into a single system. In many cases the information was not yet in digital form. Scientists representing the major subject areas were responsible for identifying and obtaining source data, and the GIS specialists focused on facilitating that process and automating the information. Up-front planning was hampered by the challenge of locating data sources for a large and complex facility. In some cases, useful information was obtained later in the project; this information sometimes superceded data sets that had already been developed. Table 1 lists some of the major data layers produced for the project and their primary sources.
Table 1. Major data layers produced for the project with their primary sources. Categories follow the Tri-Service GIS/Spatial Data Standard (v1.4 draft), except for Imagery, which is not addressed in the standard.
Category and Data Layer Data Source
------------------------------------------- --------------------------------------------------------
Auditory
Blast Noise Zones Center for Health Promotion and Preventative
Medicine (APG-CHPPM)
Aircraft Noise Modeling Argonne National Laboratory (ANL)
Boundary/Cadastre
Site Boundaries Directorate of Public Works (APG-DPW)
County Boundaries U.S. Bureau of the Census (BOC)
Buildings
Buildings APG-DPW
Climate
Surface Atmospheric Measurement Stations Directorate of Safety, Health and Environment (APG-DSHE)
Meteorological Towers APG-DSHE
Cultural
Archeological Sites and Surveys Maryland Historic Trust (MHT)
Historical Structures Inventories U.S. Army Corps of Engineers (COE)
Archeologic High Potential Zones COE
Potential Cultural Sites COE
Demographics
Census Tracts and Block Groups BOC
Environmental Hazards
Air Emission Point Source Inventories APG-DSHE
Ambient Air Quality Monitoring Stations U.S. Environmental Protection Agency (EPA)
Sampling and Supply Wells COE and DPW
Surface Water and Bottom Sediment Samples U.S. Geological Survey (USGS)
Installation Restoration Program Sites ANL (from COE data)
Hazardous Substance Storage Areas APG-DSHE
Air Quality Modeling Isopleths ANL
Radiation Related Facilities APG-DSHE and other APG tenants
Stormwater Pollution Concern Areas APG-DSHE
Solid Waste Management Units APG-DSHE
Fauna
Bald Eagle Nests and Roosts APG-DSHE
Aquatic Species Sampling Sites U.S. Fish and Wildlife Service (FWS)
Waterfowl Survey Data USGS
Flora
Vegetation Cover APG-DPW
Submerged Aquatic Vegetation EPA
Planted Submerged Aquatic Vegetation APG-DSHE
Geodetic
Reference Grid ANL
Geology
Surface Geology and Fault Lines Maryland Geological Survey (MGS)
Hydrography
Shorelines APG-DPW and National Oceanographic and Atmospheric
Administration (NOAA)
Chesapeake Bay Critical Areas Modeled by ANL based on Harford County
Department of Planning publication
Historical APG Shorelines MGS
100 and 500-Year Floodlines Modeled by ANL based on Federal Emergency Management
Agency (FEMA) flood heights.
Watershed Boundaries Modeled by ANL
Surface Hydrology APG-DPW
Wetlands FWS
Bathymetric Depth Contours and Points NOAA
Bathymetric Depth Model ANL
Imagery
Scanned Aerial Photography National Aerial Photography Program
Landsat Thematic Mapper (TM) Imagery USGS EROS Data Center
Calibrated Aerial Multispectral Scanner
Aerial Imagery National Aeronautics and Space Administration (NASA)
Land Status
Land Cover Classification - Landsat TM ANL
Land Cover Classification - Aerial Photos ANL
Land Use DPW
Land Cover Map COE
Regional Land Use/Land Cover Maryland Office of Planning
Permitted Water Discharge Points Maryland Department of the Environment
Timber Stands and Compartments COE
Recreation Areas DPW and COE
Landform
Elevation Contours and Points APG-DSHE
Digital Elevation Model ANL
Military Operations
Range Activities APG Combat Systems Test Authority
Test Track Roads APG Test and Evaluation Command
Testing/Operation Fields DPW
Proposed
EIS Proposed and Continuing Actions ANL
Soil
Soil Survey Polygons USDA - Soil Conservation Service
Soil Erosion/Deposition Modeling Modeled by ANL
Transportation
Roads and Airfields DPW
Airfield Clear Zones ANL (based on U.S. Air Force publication)
Collaboration with Existing APG GIS Efforts
Detailed GIS facilities management data provided by the APG Directorate of Public Works (DPW) to the EIS project were of significant importance. The DPW had recently conducted a large-scale overflight of the facility and used it to produce digital orthophotography and vector data layers for infrastructure mapping. A highly accurate system of ground control points was established with GPS and a modern projection, rather than the previous local coordinate system, was used. For the EIS project, these data provided many basic map layers and were a source of accurate reference points for georeferencing.
Collaboration with DPW, especially in light of DPW's previous implementation of an Intergraph MGE GIS system at APG, led to some significant project turning points. The APG EIS proponent and ANL had been using ArcInfo and ERDAS Imagine software for most GIS work on the project and had considerable experience and investment with these systems. A command-level decision at APG mandated that all GISs for the facility be implemented in Intergraph MGE. Concerns were raised about data conversion issues (between ArcInfo and MGE) and possible problems with maintaining multiple GIS systems at APG. The eventual decision was to provide the final GIS database to APG in Intergraph MGE format. It was not practical for ANL to switch GIS systems at this stage of the project; however, an efficient methodology to translate data from ArcInfo to MGE was developed and demonstrated to APG.
Implementation of the Tri-Service Spatial Data Standard
The GIS database was coded according to the Tri-Service GIS/Spatial Data Standards (TS-SDS) under development by the Tri-Service CADD/GIS Technology Center (Tri-Service 1993, 1995). The Center is a cooperative effort of the U.S. Army, Navy, and Air Force to standardize the content and organization of spatial data. It is designed to complement the work of the Federal Geographic Data Committee and focuses mainly on facilities management and large-scale data. Implementation of the standard, especially of draft 1.2 (Tri-Service 1993), was difficult for the ArcInfo system. Despite the claim in the introduction that the TS-SDS adopts "terminology that is commonly understood and not singular to any one system," very little of the standard was applicable to ArcInfo. TS-SDS 1.2 provided guidelines useful for organizing coverages into specific workspaces according to subject, but little else. The material in the standard was modeled closely after the Intergraph MGE system and included a highly detailed structure that specified even the sets of acceptable values within particular fields within tables for thematic layers. This did not mesh well with the use of pre-existing data sources, none of which were developed according to the standard.
The next draft of the TS-SDS (Tri-Service 1995) had several major improvements including more content applicable to ArcInfo, definitions of themes that had been missing in the previous draft, and the fact that it was released in digital form. Structure definitions from the standard were extracted into files that could be parsed by Unix scripts. These scripts streamlined the process of defining Intergraph MGE GIS elements. This draft of the standard was followed at ANL to the fullest practical extent. Filenames, feature classes, feature types, entity names, entity codes, table names, table structures, levels, colors, styles and weights followed the standard.
In general, the TS-SDS was a useful guide, especially for Intergraph MGE, which has highly structured hierarchy in its data model. It saved much of the effort needed to plan data organization. The main difficulty of applying the TS-SDS was its philosophy of predefining the full universe of data content without offering guidelines on how to adapt it to the real world. Attribute tables frequently contained 30 or more fields, of which most had to be left blank for lack of data. When fields corresponded more closely, their sizes or data types frequently had to be adjusted to make them applicable. Similar situations occurred at higher levels of the hierarchy, such as data layers and thematic categories. Despite this, the database structure was improved by the standard and should be easily understood by other users of TS-SDS.
ANALYSIS AND MODELING EFFORTS
The GIS needed to support each of the disciplinary areas to be analyzed for the EIS, and use of the GIS became a means of collaboration for scientists in different disciplinary areas. For example, once watershed boundaries and 100-year flood contours were produced for the hydrologist, they could be used by waste management specialists for risk assessments. Similarly, noise modeling results were used by ecologists and archaeologists to consider effects on wildlife species and structural integrity of historic buildings.
Much of the GIS analysis involved mapping, data retrieval, statistical summary, layer intersection, and other functions provided by standard GIS commands; however, there were also many unique or challenging analytical and modeling tasks. This section will highlight selected projects.
Watershed Delineation
One of the primary landscape units used to characterize the ecology of areas is the watershed. A watershed is an area of land that is drained at a single outlet or has a sink with no outlet. Watersheds are significant for analyzing sediment transport, groundwater recharge, dispersion of waterborne pollutants, and many other ecological processes. Modeling in ArcInfo Grid was performed to determine the boundaries between the watersheds of the major streams of APG.
The main steps in watershed delineation were to identify the drainage channels and determine flow directions from topographic aspects. Channels were identified from existing hydrology data for APG. A digital elevation model was developed using coverages of elevation contours and points. The ArcInfo Grid procedures to delineate watersheds were followed using this information (Esri 1995). Once a raster watershed map was completed, it was converted to polygon form and the boundaries were smoothed to remove pixel edge patterns.
The APG site has complex drainage that could not fully be represented with the methodology and data used. In some areas, natural drainage predominates, but many of these areas are low and marshy with very little slope to determine flow direction. In built-up areas, man-made features such as sewers influence flow in directions other than the surface flow direction. To improve the modeling results, editing was done using ancillary data. In Figure 1, one of the watersheds is shown with hydrologic features, contour lines, and a shaded digital elevation model.
Figure 1. A watershed delineated for Aberdeen Proving Ground with 2-foot contour lines, hydrology, and the digital elevation model.
Once the watersheds were defined, they were attributed with the main channel name and the name of the larger basin they contributed to. In addition, a number of data sets were intersected with the watersheds and their statistics were collected on a per-watershed basis. The result was extensive information for each watershed useful for management decisions or future change detection. Figure 2 shows three charts produced for the watershed in Figure 1 with relative proportions of land cover, wetland types, and modeled sediment transport characteristics.
Figure 2. Charts depicting relative proportions of (a) land cover, (b) wetland types from the U.S. Fish and Wildlife Service National Wetlands Inventory, and (c) sediment transport modeling results for the watershed in Figure 1.
Sediment Transport
To study the watersheds further, the Areal Nonpoint Source Watershed Environment Response Simulation (ANSWERS) model was run for the whole site. The model was designed at Purdue University (Beasley and Huggins 1981) and the user interface was enhanced by ANL. The sediment transport process was simulated for a specified rainfall event to produce a map of erosion and deposition patterns and hydrographs for specified watershed outlet points. Input parameters included topography, land cover, and soil characteristics.
One of the significant ecological problems in Chesapeake Bay is siltation covering oyster beds and other productive bay habitats. Runoff processes transport not only sediment, but other substances in the soil such as fertilizers, pesticides, and other contaminants. These substances have the potential to degrade water quality and impact aquatic ecosystems. This analysis provided APG with a sitewide map useful for assessing erosion problems and for planning further management strategies. The modeling results were also used for the EIS analysis to evaluate potential impacts to aquatic systems. Figure 3 depicts the ANSWERS model results for a portion of the site.
Figure 3. Results from simulation of sediment transport with the ANSWERS model. Shades from tan to brown indicate increasing levels of erosion. Shades from light green to dark green indicate increasing levels of deposition. Gray indicates areas with no significant sediment transport.
Prediction of Archeological Sites
Because of its many resources and proximity to early settlement of North America, the Chesapeake Bay area has a rich concentration of prehistoric and historic archaeological sites. These sites are a valuable cultural resource and are legally protected, but they can be difficult to find on large facilities such as APG without intensive field work. The use of predictive modeling to more efficiently locate potential sites helps focus surveys and is an important tool for archaeologists. A useful overview of these techniques can be found in Kohler and Parker (1986).
This GIS modeling work focused on prediction of prehistoric archaeologic sites. To provide data for the predictive models, information about known sites within the region was collected from the Maryland Historic Trust, including site locations and a variety of environmental variables. The variables included topographic setting, distance to water, water type, slope, aspect, elevation, soil type, and drainage. This resulted in a database of 572 sites occurring in the region, with 39 known sites occurring on APG property. The site database was analyzed for patterns in the environmental variables and results were used to design models for predictive mapping. GIS data layers corresponding to the environmental variables were also produced. Model results were used with the GIS data layers to produce the map shown in Figure 4.
Figure 4. This map shows the results of predictive mapping for prehistoric archaeological sites. Shades from yellow to red indicate increasing potential for sites. The model was based on environmental characteristics of known sites in the region and corresponded well with known sites on APG.
Air Pollution Dispersion
Modeling for dispersion of airborne pollutants related to APG activities was conducted for 12 combinations of pollutants, areas, and time intervals to examine air quality characteristics at APG. The analysis helped assess the nature of air quality impacts at APG and place them into a regional context. One of the significant data sets contributing to this analysis was the APG Air Emissions Inventory, which identified detailed information about point sources of air pollutants. For the GIS modeling, the inventory data were linked to building locations by building number.
The EPA Industrial Source Complex (ISC2) Short Term Dispersion model was used to simulate the process of pollutant dispersion (EPA 1992). Model parameters included receptor locations, topography, locations of sources with statistics about their pollutants, and meteorological data such as wind speed and direction. An example of model output is shown in Figure 5.
Figure 5. Results of modeling for 24-hour average sulfur dioxide concentrations with the EPA ISC2 model. Higher concentrations are shown in red and lower concentrations are yellow. The highest concentration of 19.5 ug/m3 was well below the National Ambient Air Quality Standard of 365 ug/m3. Buildings with point source emissions are highlighted in magenta.
Aircraft Noise Dispersion
A potentially significant environmental problem that facilities such as APG struggle with is the propagation of noise from mission activities. Many airfields face the no-win situation that their existence attracts development for access to their services, yet noise and other environmental effects result in complaints and mission restrictions that limit activities.
For the EIS, noise from aircraft activity was modeled for impact. The modeling software, NoiseMap v6.4, was developed by the U.S. Air Force and is their official method for determining the dispersion of aircraft noise. An example of the modeling output is shown in Figure 6. Output isopleths of decibel levels from the model represent annual average day/night noise levels. Key parameters include runway lengths and orientations, schedules of operations, flight tracks, and flight operations data such as aircraft power settings, airspeed, and altitude as a function of position along the flight track.
Figure 6. NoiseMap model results for an airfield at APG 1995 activities. Annual average day/night noise levels range from 80 dB (red) to 65 dB (yellow).
Bald Eagle Habitat Suitability
APG has the largest concentration of bald eagles in the Chesapeake Bay area. This is due to the relative lack of human disturbance and the density of forest cover on APG compared with surrounding areas, which have been subjected to greater anthropogenic development. Bald eagles are protected by the Endangered Species Act. The locations of nests and roosting areas were considered in the EIS analysis.
The habitat suitability modeling presented here was based on the Bald Eagle Habitat Suitability Index Model developed by the U.S. Fish and Wildlife Service (Peterson 1986). The model takes into account human disturbance, habitat preferences, food availability, and other factors. It yields an index from 0 to 1 for suitability. In Figure 7 some of the model results are shown with known nesting and roosting sites superimposed.
Most of the information necessary for the model, such as identification of mature forest stands, was obtained from analysis of available remote sensing imagery, such as National Aerial Photography Program aerial photographs and recent Landsat Thematic Mapper satellite scenes. GIS layers of buildings and roads were used as one of the measures of human disturbance. The model output correlated well with known eagle nests and roosts on APG.
Figure 7. This map shows a portion of the bald eagle habitat suitability model. Yellow to green shades represent increasing suitability levels, and gray represents unsuitable areas. Known bald eagle nest sites and roosting areas are shown in red.
FINAL SYSTEM COMPONENTS
The traditional product of a NEPA analysis project such as this one is the EIS document, which is a lengthy and detailed report of proposed actions, alternatives, impact analyses, conclusions, and recommendations. The APG EIS is currently at the internal draft stage and is about 1,500 pages. A significant contribution of the GIS was the production of about 250 map figures for the document. To add value to the document and make future NEPA analyses of APG more efficient, the GIS database itself is being delivered to the sponsor.
All GIS data layers were quality checked and documented to increase their value for future use by others. Not all the data were of ideal accuracy, so the documentation provides truth-in-labeling that will help APG use the data appropriately. A data dictionary was produced in printed form with plots and documentation reports of each data set. This meta-data was compiled in digital form as a hypertext markup language (HTML) document delivered with the GIS data and on CD-ROM. The CD-ROM product affords personnel interested in the GIS an efficient means to learn about the available data.
The final EIS will be a public document and APG is responsible for making it available upon request. Current APG plans are to produce an electronic version of the final EIS on CD-ROM. This document will be in hypertexted format and will include all GIS-based figures used in the EIS. The electronic version of the EIS and associated GIS figures will facilitate future use and archival of the document.
DISCUSSION AND CONCLUSIONS
The EIS project for APG was significantly enhanced by the GIS. This approach provided a spatial context for analysis and visualization of environmental processes for the large land area represented. It also provided APG with an extensive and integrated baseline of information for future analysis projects.
The project presented some significant logistical and technical challenges that were not fully anticipated in early planning. Because of the size and complexity of the facility, locating and obtaining data was sometimes challenging, especially for digital data. Automation of data required careful maintenance of accuracy, especially in cases of georeferencing and reprojection to the final coordinate system. A major technical challenge was the translation of GIS data from ArcInfo to Intergraph MGE. For a database of the size produced, it was critical to automate the transfer process, include careful quality checking, and support the full range of data types. We found that for environmental GIS, MGE had a more complex organization, more limitations on spatial data structure and topology, and a less efficient user interface. Despite this, it was more important to deliver a system compatible with the established hardware and software at APG, than to debate the merits of various software systems.
Essential to the GIS, both internally and for future users, was the documentation of our work. This provided truth-in-labeling that allowed appropriate use of data, which were received with a wide variety of qualities, source scales, source dates, projections, and formats. We frequently made an extra effort to determine these details for data that were received in undocumented form. Implementation of the TS-SDS helped to streamline some of the data organization issues and will make the data more easily understood by other U.S. Department of Defense organizations.
As the project proceeded the GIS became an integrating technology that helped scientists work together more productively. They were able to analyze impacts relating to other disciplinary areas by examining GIS layers produced by others, and collaborating with them. The on-line data dictionary also gave scientists a means of keeping up to date on new information being added to the database.
In some areas, such as data automation and figure production, the GIS team worked as a support group for the EIS authors, but as the database became established we were able to move to more innovative and complex projects. This led to some specific contributions, including the modeling examples presented earlier. The successful integration of NEPA analysis and GIS technology on the APG programmatic EIS shows that much can be gained from following this approach, both in the Environmental Assessment Division at Argonne and in other organizations.
ACKNOWLEDGMENTS
Many others contributed to the project. I would like to thank Rich Olsen and John Krummel for delegating to me a challenging and interesting project. Thanks to the following for their valuable work on the GIS efforts: Andy Ayers, Dana Bobzin, Young-Soo Chang, Rich Derr, Mary Elliott, Frank Freda, Lisa Godzik, Ken Gotsch, Margaret Greaney, Allison Huber, Doug Hudson, Dan Maloney, Joan Meyer, Konnie Moeller, Mary Snider, Haiping Su, and Nicole Tynski.
The submitted paper has been authored by a contractor of the U.S. Government under contract No. W-31-109-ENG-38. Accordingly, the U.S. Government retains a nonexclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for U.S. Government purposes. Work supported under a military interdepartmental purchase request from the U.S. Department of Defense, through U.S. Department of Energy contract W-31-109-Eng-38.
Beasley, D.B., and L.F. Huggins (1981) ANSWERS (Areal Nonpoint Source Watershed Environment Response Simulation) Users Manual, EPA-905/9-82-001, U.S. Environmental Protection Agency, Chicago, Illinois.
Esri, Inc. (1995) Cell Based Modeling With Grid-Surface Hydrologic Analysis, Environmental Systems Research Institute, Inc., Redlands, California. On-line ArcDoc Version 7.0 Help Document.
Kohler, T.A., and S.C. Parker (1986) Predictive Models for Archaeological Resource Location, Advances in Archaeological Method and Theory, Volume 9. Academic Press, Inc., New York.
National Environmental Policy Act (NEPA) (1969), The National Environmental Policy Act of 1969, Public Law 91-190, 42 U.S. Code 4321-4347, as amended by Public Law 94-52, July 3, 1975, and Public Law 94-83, August 9, 1975.
Peterson, A. (1986) Habitat Suitability Index Models: Bald Eagle (Breeding Season), Biological Report 82(10.126), Fish and Wildlife Service, U.S. Department of the Interior, Washington DC.
Tri-Service CADD / GIS Technology Center (1993) Tri-Service GIS / Spatial Data Standards, Release 1.2, U.S. Department of Defense, Washington DC.
Tri-Service CADD / GIS Technology Center (1995) Tri-Service GIS / Spatial Data Standards, Release 1.4, U.S. Department of Defense, Washington DC.
U.S. Environmental Protection Agency (1992) User's Guide for the Industrial Source Complex (ISC2) Dispersion Models, Volumes I-III, Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina.
AUTHOR INFORMATION
James A. Kuiper: Biogeographer / GIS AnalystDISTRIBUTION STATEMENT AND DISCLAIMERS
Distribution Restriction Statement:
Approved for public release:
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DISCLAIMER OF LIABILITY: This document was prepared as an account of work sponsored by an agency of the U.S. Government. Neither the U.S. Government nor any agency thereof, nor the University of Chicago, nor any of their employees or officers, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights.
DISCLAIMER OF ENDORSEMENT: Reference to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the U.S. Government or any agency thereof. The views and opinions of document authors do not necessarily state or reflect those of the U.S. Government or any agency thereof, Argonne National Laboratory, or the University of Chicago.
April 5, 1996