management is rarely considered in this process because the EH&S organization, as a cost center, is not perceived to add value to the firm, and therefore rarely attracts such an investment. The EH&S organization is then left to manage its data on its own, even though much of the information on which it depends is in fact owned by line organizations within the company. 2.1 The Need for Integration The many processes of the typical EH&S organization are usually supported by as many diverse environmental management information systems, many of them manual (i.e., with little or no computer support). These information systems have evolved in response to individual needs, generally without regard to inter- dependencies between processes and their information management needs. Apart from the obvious inefficiencies which result from such cir- cumstances, this ad-hoc structure has resulted in redundant and inconsistent databases—multiple databases store the same piece of information, and they sometimes disagree on its value. For example, several EH&S information systems may use facilities data from different databases which conflict with one another. This sort of inconsistency ultimately threatens compliance. 2.2 An Integrated Solution There is an approach which improves the situation by developing the framework for an integrated environmental information system (IEIS), an important special case of EMIS. It is important to note that the term “information system,” as operationally defined here, is much broader than the computer hardware and software which might support it. It includes a data model incorporating the structure, definition, and relationships between data elements, as well as the processes and procedures by which these data are created, modified, used, and destroyed. While much of this can and should be supported by computer systems, this fact has little relevance to the conceptual definition of the information system. Once the IEIS is defined, a systems engineering activity can readily determine the design and structure of the hardware and software systems which will support it, about which more will be said later. 2.3 Conceptual Framework The IEIS approach is predicated on the notion that one can usefully separate data from the management processes that use them. That is, most or all data of use to EH&S are descriptive of objects, while the various management processes undertaken by EH&S professionals are focused on these objects. An object- oriented approach to EH&S information might start with the definition of such high-level objects as employees, customers, buildings, vehicles, services, and Copyright 2002 by Marcel Dekker, Inc. All Rights Reserved. products. Each of these can then be decomposed in a similar fashion, as appro- priate, with the terminal objects described by a data structure. The various EH&S management processes can generally be viewed as operating on the data objects suggested above. For instance, SARA Title III Section 312 reporting is focused (by regulation) on buildings, while OHSA training requirements are focused on employees. Furthermore, each process may be supported by one or more software applications. In general, the software applications serving EH&S processes are the agents which interact with the data required for these processes (Figure 1). Thus, there is envisioned a clear separation between data, processes, and applications: 1. A datum may be used by multiple processes; e.g., Building Address is used for SARA Title III reporting and for OSHA accident reporting. 2. A process may be served by multiple applications; e.g., one software application might support the SARA inventory maintenance activity by site personnel, while another application is used to generate the SARA reports. 3. In some instances, applications may be used by multiple processes; e.g. the software used by site personnel to maintain chemical invento- ries may serve the purposes of both SARA and OSHA compliance processes. Data Object 1 Software Application 1 Process 1 Process 2 Software Application M Software Application 2 Data Object 2 Data Object L Process N … … … FIGURE 1 An exemplary relationship between data, processes, and software applications. Copyright 2002 by Marcel Dekker, Inc. All Rights Reserved. In essence, this approach addresses our need to understand this relation- ship between our information and our processes so that we may ensure the availability of the correct data and the correct software applications to interact with those data. 2.4 The Path to Integration There are four essential steps to achieving an integrated environmental informa- tion system: 1. Develop an integrated data model. 2. Map the integrated data model onto corporate databases of record. 3. Define high-level requirements for the IEIS. 4. Implement the foundation of the IEIS. While some of these can be executed concurrently, it is imperative that we recognize the precedence implicit in their ordering. As with any systems engi- neering activity, in this activity the what has to lead the how, rather than the other way around. It will be advantageous to look ahead to current and future system implementations to help us to achieve an understanding of requirements, but particular discipline must be applied to prevent us from erroneously finding a requirement in what is merely a habit. This discipline will be encouraged by a phased approach, in which we first define an IEIS for the set of processes as they currently exist, admitting that the model will be revisited as a result (and indeed in support of) efforts to reengineer those processes. 2.5 Model Development The first step in the project is the development of an integrated data model which correctly describes the firm from an EH&S point of view. The initial (baseline) data model must include all data items required by the current set of EH&S processes, but must be orthogonal to these processes so that data objects and fields which are common to multiple processes occur only once in the data model, to be shared by the processes requiring them. This is critical to the identification of shared information and the elimination of redundant databases. Once such a baseline data model has been developed, it can and should be refined and revised as appropriate to reflect the ongoing reengineering of the EH&S organization’s structure and processes. 2.6 Mapping the Model onto Databases The integrated data model so developed will then be analyzed to determine the appropriate owner for each of the data categories and elements. In many cases, this will be the so-called database of record for the company, and will not be under the control of the EH&S organization. For example, much informa- Copyright 2002 by Marcel Dekker, Inc. All Rights Reserved. tion about corporate facilities might be maintained by a real estate organization within the firm but outside of EH&S. Identifying our stake in such external databases is essential since, as customers of these databases, we will need to be recognized and have a voice in the implementation and management of the data. There may also be data items of importance to EH&S which should and could readily be maintained in these external databases; we will want to be in a position to lobby the appropriate organizations for such extensions. Furthermore, interfaces to these data sources must be engineered so that the data truly will be shared, rather than simply copied into yet another system, further contributing to data redundancy. 2.7 Defining IEIS Requirements The third step is the definition of high-level requirements for the integrated information system. The integrated data model and analysis described above form the foundation for this. What must be added are the functional requirements for the integrated system. For example, if EH&S information must be globally accessible by EH&S leadership, this requirement should be articulated clearly. 2.8 Implementing the IEIS Foundation The fourth step addresses the implementation of the IEIS. Implementation includes the interaction and negotiation with other organizations whose informa- tion assets have been identified as a subset of the EH&S data model in step 2. It also includes the planning and acquisition and/or development of software required to realize the IEIS from the starting position of our existing information management systems. The result of this step is not necessarily a single software system; in fact, this outcome is highly unlikely, given that the software to be used by individuals and groups engaged in the various processes will have to satisfy functional requirements which may be peculiar to those processes. As long as the ensemble of computer systems finally in use by the EH&S organization (a) im- plements the integrated data model developed in steps 1 and 2, and (b) satisfies the high-level requirements defined in step 3, then we will have achieved an integrated environmental information system and will reap the benefits thereof. This, perhaps, is the point of departure of this approach from conventional thinking about integration—we seek to achieve the benefits of integrated infor- mation while valuing diversity of software applications and vendors. Once these four steps have been executed, the design and implementation of the integrated system using an appropriate combination of existing and new platforms can proceed through conventional information project management and systems engineering activities. In fact, it might be hoped that through effective communication, any ongoing procurement and development activities underway during the execution of these steps can be appropriately guided so as to minimize Copyright 2002 by Marcel Dekker, Inc. All Rights Reserved. changes or disruption once they are complete. For example, an early intermediate result will be the identification of data common to the first key processes to be evaluated. This knowledge can surely be used during the procurement of support- ing systems to anticipate the results of the integration effort. 2.9 EMIS Summary This approach to integrating environmental management information systems into an integrated environmental information systems serves to illustrate the issues attending these systems in general. Whether this approach or some other is used, however, the critical element for proactive environmental management is that integration be achieved in the interests of eliminating compliance-threatening redundancy and removing substantial inefficiencies. 3 ENVIRONMENTAL DECISION SUPPORT SYSTEMS (EDSS) As the complexity of our environmental management problems has increased, so has the need to apply the information management potential of computing technology to help environmental decision makers with the difficult choices facing them. Environmental information systems have already taken many forms, with most based on a relational database foundation (as described in the previous section). Such systems have helped greatly with the day-to-day operations of environmental management, such as chemical and hazardous waste tracking and reporting, but they have two critical shortcomings which have prevented them from significantly improving the lot of environmental scientists and planners tackling more strategic decisions. Traditional environmental management information systems generally ig- nore the crucial spatial context of virtually all environmental management problems, and they offer little or no support for the dynamics of environmental systems, both manufacturing and otherwise. Fortunately, a relatively new cate- gory of system, called an environmental decision support system (EDSS), shows real promise in both of these areas. 3.1 What are Environmental Decision Support Systems? Environmental decision support systems are computer systems which help humans make environmental management decisions. They facilitate “natural intelligence” by making information available to the human in a form which maximizes the effectiveness of their cognitive decision processes, and they can take a number of forms (1). As defined here, EDSSs are focused on specific problems and decision makers. This sharp contrast with the general-purpose character of such software Copyright 2002 by Marcel Dekker, Inc. All Rights Reserved. systems as geographic information systems (GIS) is essential if we are to put and keep EDSSs in the hands of real decision makers who have neither the time nor inclination to master the operational complexities of general-purpose systems. Indeed, it can be argued that most environmental specialists are in need of computer support which provides everything that they need, but only what they need. This point becomes more critical when it is understood that many important “environmental” decisions in design and manufacturing, for example, are not made by environmental specialists at all, but are instead made by professionals in other disciplines. 3.2 The Need for Environmental Decision Support Systems The development of environmental policies and generation of environmental management decisions is currently, to a large extent, an “over-the-counter” operation. Technical specialists are consulted by decision makers (who may or may not have a technical background), to assist in gathering information and exploring scenarios. Because of the inaccessibility of data and modeling tools, decision makers must consult their technical support personnel with each new question, a time-consuming and inefficient process. If the data and analytical tools could be placed within reach of decision makers, they would be able to consult them more readily, and would therefore be more likely to base their decisions on a technical foundation. In some instances, the availability of environmental decision support determines whether or not a product design or manufacturing process will indeed be “environmentally con- scious.” This is the premier reason why environmental decision support systems, of a sort described in part herein, are necessary if we are to achieve higher quality in our environmental management decisions and obtain more protection with our finite resources. 3.3 Foundations Environmental decision support systems address a problem domain of remarkable breadth, ranging from selection of an appropriate light switch for an automobile to the determination of community risk associated with stored chemicals. The character of environmental decisions and their surrounding issues is central to the design of a successful EDSS. 3.4 The Nature of Environmental Management Decisions To understand environmental management decisions, we must first identify the decision makers. The stereotypical image of an environmental manager is an environmentally trained business manager given the responsibility for avoiding Copyright 2002 by Marcel Dekker, Inc. All Rights Reserved. fines and other sanctions, and perhaps pursuing “beyond compliance” goals, all within the constraints of finite (and generally tight) budgets. Indeed, many environmental decision makers fit this description. However, these individuals also have their counterparts in the regulatory arena (such as agency compliance officers). Furthermore, critical environmental decisions are often made by market researchers, product designers, and manufac- turing process developers. Naturally, the level of environmental expertise these individuals possess is highly variable. Nonetheless, all of them can and do make critical environmental decisions. It is therefore incumbent upon the toolbuilders— including EDSS architects—to craft systems and processes that will help to bridge the gap between technical expertise and the decision maker, so that the benefits of this expertise may be realized. 3.5 Characteristics of the Problem Environmental decision makers are clearly a diverse group of people faced with a diverse group of problems. The breadth of their problem domain, in fact, defines the need for eclectic individuals with tools to match. In general, environmental decision problems are Spatial, in that most human activities and their environmental impacts are associated with a place having its own characteristics which influence the decision Multidisciplinary, requiring consideration of issues crossing such seem- ingly disparate fields of expertise as atmospheric physics, aquatic chemistry, civil engineering, ecology, economics, geology, hydrology, toxicology, manufacturing, materials science, microbiology, oceanogra- phy, radiation physics, and risk analysis Quantitative, because the constituent disciplines themselves are highly quantitative, and because the costs and ramifications are generally so significant, that objective metrics are desired to help mitigate controversy Uncertain, in that while the elements are quantitative, the sparsity of data and nascent state of the constituent disciplines leaves many unknowns Quasi-procedural, since many environmental decisions are tied to a regu- latory or corporate policy framework which specifies the steps by which a decision is to be reached, and because the threat of liability dictates a defensible audit trail for the decision process Political, reflecting the fact that environmental management is driven by public policy, influenced by such considerations as economics, social impacts, and public opinion The diversity of these characteristics of the problem domain make effective environmental decision support extremely challenging. Copyright 2002 by Marcel Dekker, Inc. All Rights Reserved. 3.6 Implications for Environmental Decision Support Because of these factors, it is not practical to contemplate a generic decision framework for environmental management. Even if it were possible to capture all of the elements necessary to address the great variety of decisions to be under- taken, the system so built would be virtually unusable. Environmental managers are already confronted with a vastly complex problem space; one of the first jobs of the decision support system is to simplify this space, offering them everything that they need to make the decision at hand—but only those things. Therefore, while our definition of EDSS includes the integration of multiple supporting technologies (such as simulation and GIS), we further restrict this definition to stipulate that EDSSs are focused on a particular decision problem and decision maker. Thus, they are not general-purpose tools with which anything can be done—if only you knew how to do it. Rather, they are particularly tailored to the problem facing the analyst, and offer a user interface which is optimized for this problem. The focused nature of such EDSSs improves the user’s interaction with the computer system, allowing the user to concentrate on the problem at hand and the information and tools needed to solve it. It also dictates a software architecture that facilitates the development of sibling systems embracing different decision problems with an essentially common user and data interface (2). Such a family of focused EDSS siblings offers user interface simplicity, in that the siblings share interaction style, organization, and fundamental approaches (where appropriate), while maintaining the focus each sibling has on its particular decision problem. 3.7 Task Analysis of Environmental Decision Making The focused approach to EDSS design advocated here dictates the use of a human factors engineering technique, called task analysis, to support the specification of a particular EDSS for a particular problem. As defined in the human factors community, “task analysis breaks down and evaluates a human function in terms of the abilities, skills, knowledge and attitudes required for performance of the function” (3). The EDSS designer must endeavor to understand the decision problem, and all of the factors which must be considered in solving it. In addition, the “social history” of the problem must be understood, since there will (in general) already be a number of different approaches to solving a given environmental management problem. For a system to support an analyst in arriving at a credible decision, the various competing approaches must be considered, and possibly accommodated. A major stumbling block in task analysis is the fact that very few individ- uals can accurately explain the way in which they actually arrive at a particular decision. They can tell you how they think they should do it, and they can often develop a post-hoc analytical rationale for their decision, but people are generally Copyright 2002 by Marcel Dekker, Inc. All Rights Reserved. unaware of the actual process by which they make decisions. Thus, other instruments must be used to understand the decision process, ranging from observation and interview up through controlled experimentation to determine the influence of different variables on decisions. In the environmental arena, this is further complicated by the fact that there are often guidelines or regulations dictating the way in which decisions are supposed to be made about a particular problem. These do indeed dictate certain aspects of the process, but often leave a great deal unspecified. For example, the U.S. Resource Conservation and Recovery Act (RCRA) requires that a waste facility be monitored by a network including at least one upgradient and three downgradient wells in order to assure that no hazard to the public health results from the facility. However, though the legislature was specific about this detail, it made little effort to assist the manager in deciding where or how many (above four) wells are to be installed. Furthermore, the language of the act would suggest that certainty is required with respect to the detection of leaks, though no reasonable person would argue that this is either theoretically or economically achievable. Implicit in this example is the issue of uncertainty, which, because of its importance in environmental management, deserves further attention. 3.8 Management of Uncertainty Uncertainty is implicit in environmental decision making. Complex technical decisions must be made regarding events—in both the past and the present— which depend on many different variables. Solutions to such problems often depend on the use of various mathematical modeling techniques. These tech- niques, in the main, attempt to predict the future performance of a complex system on the basis of relatively sparse empirical data. The predictions drawn from these modeling studies form the basis for the entire process to follow, including such expensive decisions as the design of a product and its associated manufacturing processes. Ultimately, the environmental effectiveness of the product throughout its life cycle, in terms of protection of human health and reduction of environmental risk, depends on these results. However, these modeling studies are unavoidably visited by uncertainty of various types, ranging from conceptual model uncertainty—associated with the selection of assumptions necessary to choose the model(s)—to parameter uncer- tainty resulting from sparse empirical data, noisy measurements, and the general difficulty associated with measuring critical parameters. 3.9 Sources of Uncertainty Uncertainty in such environmental management problems exists because of a lack of empirical data, errors in the data, incorrect models, and the general non- determinism of nature. Copyright 2002 by Marcel Dekker, Inc. All Rights Reserved. The first of these, a lack of empirical data, is easy to understand; we routinely live with imperfect knowledge of the current state of systems, owing to lack of data (in a usable form). This and the second (errors in the data) are the ones typically addressed in scientific and engineering studies when the goal is to reduce uncertainty. The usual approach is to collect more data, and to attempt to reduce the measurement error in the data collected. The third reason, the use of incorrect models, is recently receiving more attention in environmental management. As environmental managers come to accept that model building (whether mental or mathematical) is an essential part of problem solving, the disagreements as to which models are correct become more apparent. Some would argue that a model is correct to the extent that it accurately predicts the future behavior of the system; the limiting factor for environmental problems is the complexity of the system in question. And here is where an interesting human factor emerges. As mathematical models are expanded to attempt to account for more of the fine details of the natural system under study, the mental models of the analyst become inadequate. While humans are capable of recognizing and apprehending in a gestalt sense the breadth of complex systems, they are ill equipped to mentally manage the myriad simultaneous details attending such systems. It can be argued that we build mathematical models precisely because we cannot manage such details mentally. Yet, as we build these models, they too become more complex than we can fully grasp, resulting in a great deal of effort and controversy associated with the development of the mathematical models. Many environmental modelers spend more time studying their models than studying the natural systems they emulate. This problem becomes especially acute when the decision maker is not the developer of the mathematical model, because an opportunity exists for mismatch between the analyst’s mental model and the quantitative mathematical model he or she is attempting to use. This results in uncertainty, both subjective (i.e., lack of confidence on the part of the analyst) and objective (i.e., a measurable variability in the decisions made by several analysts or by one analyst on several occasions). Ultimately, this uncertainty finds its way into public perception, causing the public at large to wonder how to interpret the products of science and engineering (the public’s awareness of the modeling debate surrounding global warming is a good example of this). Finally, the fourth cause of uncertainty in environmental problems arises out of the nondeterministic character of the natural environment, at least as it is currently understood. We should not expect to eliminate uncertainty entirely in solving environmental problems. Like the other three, this cause of uncertainty applies to both spatial and aspatial data, and some adaptive approaches have been proposed to help analysts arrive at accurate descriptions of the uncertain natural parameters (e.g., Ref. 4). Copyright 2002 by Marcel Dekker, Inc. All Rights Reserved. [...]... environmental decision support These models, both analytical and empirical, assist with such tasks as dose–response calculation and uptake prediction 4.2 Information Systems Engineering Information systems engineering (meant here to include computer science and its kin) is also a multidisciplinary field Not surprisingly, information systems engineering and several of its associated technologies plays a key... accidental release prevention and emergency response policies at the source, the regulated substances handled, and the worstcase release scenarios and alternative release scenarios, including administrative controls and mitigation measures to limit the distances for each reported scenario This regulation provides a natural application for environmental decision support systems To plan for a release of chlorine... information regarding alternative materials, components, and processes available for consideration by the designer Such information is notoriously difficult to find, and when available its applicability to different situations is quite variable To support the designer adequately, the system must make this information available for ready access, but it must also help the user to select only the information... probabilities and expected value Folding back the path probabilities and expected values of chance nodes (by multiplication), one can arrive at expected values for the decision nodes, and make an optimal decision based on this value However, to do this one requires some metrics for expected value of each outcome, and probabilities for each branch from each chance node Furthermore, the decision and chance... The foregoing has described a wide range of information systems designed to help improve the quality of environmental management The integrated approach to environmental management information systems, while based largely on conventional relational database technology, still offers the prospect of real risk reduction and performance improvement when information maintenance and management is required For. .. Community and with the ratification of the Treaty of Amsterdam, the European Community receives wide legislative rights The following states are members of the European Community: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, and the United Kingdom of Great Britain and Northern Ireland In order to carry out their tasks and in... about (and through) the three-dimensional plume in order to get a better feel for its shape and character; contour plots fail to communicate this information Computing and displaying such volumetric renderings rests squarely within the domain of information systems engineering The algorithms required to efficiently draw, shade, and cast virtual light upon three-dimensional objects drawn on a two-dimensional... optimization, to classical and Bayesian probability theory While such formal decision methods are only sparingly applied in current environmental decision frameworks, it can be expected that this will increase in the future, if for no other reason than they provide some accountability for the decision process and remove some of the air of subjectivity from it There is a formalism associated with decision... criteria for evaluation and action, one employs decision rules These include procedures for aggregating criteria into a single index, along with an algorithm for comparing alternatives according to this index Decision rules can be choice functions (sometimes called objective functions) or choice heuristics The former provide a mathematical method for alternative comparison, typically involving some form... approaches the study of our environment with an eye toward human needs and use of the environment, and therefore addresses the science, engineering, and management practices which will help to conserve environmental resources for human benefit This is not to imply that environmental scientists as a whole do not place value on nature in and of itself, but that their professional lives are more focused on . Application 1 Process 1 Process 2 Software Application M Software Application 2 Data Object 2 Data Object L Process N … … … FIGURE 1 An exemplary relationship between data, processes, and software applications. Copyright. be the so-called database of record for the company, and will not be under the control of the EH&S organization. For example, much informa- Copyright 20 02 by Marcel Dekker, Inc. All Rights. models, both ana- lytical and empirical, assist with such tasks as dose–response calculation and uptake prediction. 4 .2 Information Systems Engineering Information systems engineering (meant here