An environmental decision support system for spatial assessment and selective remediation
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Accepted 23 December 2010Available online 3 February 2011
Spatial Analysis and Decision Assistance (SADA) is a Windows freeware program that incorporates spatial
Visualization is a key component of modern site assessments;however, visualization combined with spatial data analysis andassessment algorithms requiresprogramming skills andbudgetsnot
resources and add functionality in response to market demand(ESRI), or even a sequence of individual models to handle eachcomponent separately. This situation creates a gap in softwareavailability, decision support freeware for environmental assess-ment practitioners that does not require a signicant investment intraining and/or nances to apply at contaminated sites.
Spatial Analysis and Decision Assistance (SADA) is freeware thatlls this gap. It has been developed at the University of TennesseesInstitute for Environmental Modeling in collaboration with other
* Corresponding author.
Contents lists availab
Environmental Modelling & Software 26 (2011) 751e760E-mail address: firstname.lastname@example.org (S.T. Purucker).SADA is freeware: binaries for the full-featured installation les,documentation, databases, and examples are available forfree download at: http://www.tiem.utk.edu/wsada/index.shtml. Minimum resolution is 1024768 with 16million colors (for 3-D viewing); processor should bea minimum Pentium 3 processor with 800 GHz, 512 MBRAM, and 800 MB hard disk space available for installa-tion. The interface is constructed in VB.Net and leveragesdynamic link libraries for algorithms constructed in otherlanguages, including C and Fortran.
Geographic Information Systems (GIS), sample design, statistics,data management, two- and three-dimensional visualization,spatial modeling, uncertainty analysis, risk assessment, remedialdesign, and cost/benet analysis. These diverse capabilities mustthen be used together in a coherent, defensible, repeatable decisionprocess (Matott et al., 2009). Generally, this algorithmic integrationis achieved through highly exible implementations that requiresignicant programming capability (e.g., command-line interface ofRwith contributedpackages (RDevelopmentCoreTeam,2010); plugand play model capabilities of FRAMES (Babendreier and Castleton,2005)), proprietary software that may require signicant nancialKeywords:Environmental decision support softwareGeostatisticsSample designRisk assessmentSelective remediation
Software availability1364-8152/$ e see front matter Published by Elsevierdoi:10.1016/j.envsoft.2010.12.010assessment with spatial modeling tools. It has since evolved into a freeware product primarily targetedfor spatial site investigation and soil remediation design, though its applications have extended intomany diverse environmental disciplines that emphasize the spatial distribution of data. Because of thevariety of algorithms incorporated, the user interface is engineered in a consistent and scalable mannerto expose additional functionality without a burdensome increase in complexity. The scalable environ-ment permits it to be used for both application and research goals, especially investigating spatial aspectsimportant for estimating environmental exposures and designing efcient remedial designs. The result isa mature infrastructure with considerable environmental decision support capabilities. We provide anoverview of SADAs central functions and discuss how the problem of integrating diverse models ina tractable manner was addressed.
Published by Elsevier Ltd.
always available for site assessments. These assessments require theintegrated use of tools from multiple disciplines; includingReceived in revised form20 December 2010visualization, geospatial analysis, statistical analysis, human health and ecological risk assessment, cost/benet analysis, sampling design, and decision support. SADA began as a simple tool for integrating riskReceived 21 April 2010 assessment tools for effective environmental remediation. The software integrates modules for GIS,An environmental decision support systremediation
Robert N. Stewart a,b, S. Thomas Purucker c,*aDepartment of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TbComputational Sciences and Engineering Division, Oak Ridge National Laboratory, P.OcOfce of Research and Development, U.S. Environmental Protection Agency, 960 Colleg
a r t i c l e i n f o a b s t r a c tjournal homepage: www.Ltd.for spatial assessment and selective
996, USA2008, Oak Ridge, TN 37831, USA
ation Road, Athens, GA 30605, USA
le at ScienceDirect
elling & Software
(Rizzoli and Davis, 1999). For any given modeling task, the interfacepresents only the required steps and functional elements for thattask; these methods range from simple statistical output to risk-based area of concern (AOC) maps based on ecological exposuremodels and geostatistical algorithms.
This manuscript describes the primary environmental assess-ment methods that are incorporated into the software, then pres-ents the results of the integration with a focus on how the decisionelements were accommodated into the interface, and a discussionconcerning the elements of the scalable decision interface that bothallows for added functionality in a scalable manner and guides theuser through complicated, multi-step decision processes.
SADAs basic tool set encompasses many areas of environmental characteriza-tion and assessment. Fig. 1 shows how these tools are typically organized to facilitatedecision support from the earliest phases of an investigation. In the following text,we briey summarize available methods for: determining the number of samplesneeded to characterize a site; creating sample designs; data management; explor-atory data analysis; and spatial modeling tools.
2.1. Sample design
Sampling is imperfect and subject to error (Christakos and Olea, 1992); uncer-tainty sources include the complex and dynamic processes under study, economicrealities, and the technical limitations of available sampling and analytical methods(Thompson,1999). Short of exhaustive sampling, investigatorsmust use a limited set
al Modelling & Software 26 (2011) 751e760Universities, federal agencies, national laboratories, private sectorcompanies, and individual consultants. The software has been fun-ded by the U.S. Nuclear Regulatory Commission (USNRC), U.S.Environmental Protection Agency (USEPA), and theU.S. Departmentof Energy (USDOE). SADAhas been in development for over a decadewith itsmost recent release (Version 5.0) in 2009. Past versions havegenerated world-wide interest, over 20,000 downloads from 100countries, with the majority of users in North America and Europe.The software provides a tool that makes a direct, practical connec-tion between data analysis, modeling, and decision-making withina spatial context (Stewart and Purucker, 2006). The target user is anenvironmental investigator interested in characterizing contami-nation for a particular site and designing selective remedial actions.
SADA has been used in a number of environmental modelingapplications e including site assessments, academic investigations,and regulatory frameworks designed to streamline characterizationprocesses. Direct application of the software, or discussion of its rolein environmental investigations have appeared in a number of peer-reviewed articles. These include the assessment of contaminatedsites such as underground storage tanks; landll disposal sites (Buttet al., 2008a,b); contaminated land (Bardos et al., 2001; Carlon et al.,2007; Chung et al. 2007); surface water (Boillot et al., 2008);groundwater (Hornbruch et al., 2009); oodplains (Sauer et al.,2007); and applied spatial analyses for all these media (Goovaerts,2010). Broader applications include investigations of microbialcommunity structure (Franklin andMills, 2003) and enzyme activity(Smart and Jackson, 2009); multi-criteria decision analyses (Linkovet al., 2004); boundary delineation of soil polygons for terrain anal-ysis (Sunila et al., 2004); disease incidence (Ifatimehin and Ogbe,2008); examination of interactions between habitat and contami-nation on ecological dose (Purucker et al., 2007); soil remediationframeworks (Norrman et al., 2008; Rgner et al., 2006); hot spotdelineation (Sinha et al., 2007); landscape ecology (Purucker et al.,2009b; Steiniger and Hay, 2009); human health risk (Andam et al.,2007); and determining laboratory analytical support necessary tosupport eld-level data collection (Puckett and Shaw, 2005).
In addition, SADA capabilities as an environmental managementcomputational toolkit (Holland et al., 2003) have led its adoptionwithin regulatory frameworks. Principal among these are theUSEPAs Triad approach (Crumbling, 2001) and the USNRCs Multi-Agency Radiation Survey and Site InvestigationManual (MARSSIM).The USEPA has long recommended SADA as a tool for strategic siteinvestigation and characterization (U.S. Environmental ProtectionAgency (USEPA), 2001a, 2005a, 2005b); however, recent develop-ments in the Triad approach, a consensus-based decision-makingapproach for hazardous waste sites that maximizes use of innova-tive characterization tools and strategies (Crumbling et al., 2003),have increased its utility for larger-scale applications.
Construction of this type of software provides numerous oppor-tunities for application and research. As a vehicle for deployingexistingmethods and developing newassessment approaches, SADAmust meet several, sometimes conicting, objectives successfully. Acritical factor is the user interface. It is important to engineer anassessment environment that encompasses needed functionality,yet is also suited to specic users with specic goals. These objec-tives are met by a scalable interface that is visually consistent whileproviding access to the depth of available algorithms. The interfacewas createdwith four criteria inmind: (1) users should easily nd anachievable assessment goal; (2) the interface should present onlyrelevant tool sets and not clutter the computational environment;(3) the interface should provide open, exible guidance through anyinterim steps required to achieve the assessment goal; and (4) theinterface should be consistent across all goals. The primary modulesand functions that help meet these criteria are encapsulated as
R.N. Stewart, S.T. Purucker / Environment752objects that can be accessed for various decision-informing outputsof data and accept uncertainty to characterize conditions at a site. An early decisionwhen characterizing contaminated sites is determining how many samples tocollect. For smaller sites this is often a function of the available sampling budget;however, statistical power curve methods recommended by the U.S. EnvironmentalProtection Agency (USEPA) (2000, 2006) can be used to estimate sample sizesrequired to support decisions with specied error rates. SADA uses power curvesbased on the non-parametric Sign and Wilcoxon rank sum tests from MARSSIMguidance (Gogolak et al., 1998) and algorithms implemented in Gogolak (2001) tocalculate required sample sizes and display power curves. Implementation requiresspecifying alpha and beta decision errors, a target concentration for the alphadecision error, a lower bound for the beta decision error, and an estimate of thestandard deviation. After the data has been collected, a retrospective power curvecan be constructed using the number of samples actually collected and the observedstandard deviation to ensure that decision objectives were satised.
Fig. 1. Primary components of the graphical user interface that contain SADA softwarefunctions and a typical example ow path. Different chart elements are supported bydecision support interview processes that guide the user through the relevant steps.The scalable and object-oriented nature of the components allows for new functions tobe added to the software in a consistent manner and for existing modules to berecombined in useful ways, with new interview processes, to provide support fora range of decision support processes. Although environmental assessment decisionprocesses are usually considered to be acyclic, decision support software must facili-
tate the ability to revisit earlier steps with update information. The presentedcomponents correspond to subheadings in the methods and results text.
The two broad categories of spatial sampling strategies are design-based andmodel-based (Brus and de Gruijter, 1997). Design-based strategies are used forcollecting data where prior knowledge may not exist, or, if present, are ignored forstatistical design purposes. Model-based sampling strategies utilize availableinformation about the site, such as previously sampled data, historical information,or a spatial model, that allows for developing biased sampling strategies to achievespecic objectives. Because SADA permits users to import supporting data sets,construct spatial models, or create user-dened prior knowledge maps that supportdesign-based strategies, these two categories are not mutually exclusive. A subset ofsample designs in SADA is summarized in Table 1.
U.S. Environmental Protection Agency (USEPA) (2000, 2002) provides guidanceon the selection of sampling designs. Grid approaches can be implemented ina triangular or square partitioning of the site (Yfantis et al., 1987) in which a samplepoint is located in the center of each partition. In addition, unaligned imple-mentations of simple and standard grids are available that randomly place sampleswithin each partition, not at a central point. This hybrid approach shares propertiesof both simple random and gridded designs: its samples are located with reasonablygood spatial coverage, but still possess an element of randomness (Gilbert, 1987).Any sample design can be made into a stratied design using the standard drawingtools that allow users to aggregate sections of the site for purposes such as deningexposure areas or conducting stratied sampling.
Model-based sampling strategies can be based on hard data, soft data, or a data-driven model. These designs can incorporate the presence of spatial correlation,spatially delineated decision rules, and cost/benet considerations. Geographical
R.N. Stewart, S.T. Purucker / Environmental Mdesigns target particular areas of interest to rene local knowledge about spatialprocesses, rather than to estimate a population statistic. Among these are designsthat place samples in high risk or boundary zones at a contaminated site, based onspatial modeling, subjective prior information maps, or Local Index of SpatialAssociation (LISA) statistics. Methods are also available to place samples in areas ofinsufcient data density, including adaptive ll, which sequentially places samplesat a site location that maximizes the distance to the nearest sampled location, andRipleys K, which sequentially places samples in neighborhoods with the lowestsample d...