Development of an Expert System for the Remediation of Petroleum-Contaminated Sites

Groundwater and soil contamination caused by light nonaqueous phase liquids (LNAPLs) spills and leakage in petroleum industry is currently one of the major environmental concerns in North America. Numerous site remediation technologies, generally classified as ex situ and in situ remediation techniques, have been developed and implemented to clean up the contaminated sites in the last two decades. One of the problems associated with ex situ remediation is the cost of operation. In recent years, in situ techniques have acquired popularity. However, the selection process of the desired techniques needs a large amount of knowledge. Insufficient expertise in the process may result in unnecessary inflation of expenses. In this study, petroleum waste management experts and Artificial Intelligence (AI) researchers worked together to develop an expert system (ES) for the management of petroleum contaminated sites. Various AI techniques were used to construct a useful tool for site remediation decision-making. This paper presents the knowledge engineering processes of knowledge acquisition, conceptual design, and system implementation in the project. The expert system was applied to a real-world case study and the results show that the expert system can generate desired remediation alternatives to assist decision-makers. The application case study constitutes partial validation of the prototype expert system.     

Selection and Calibration of Numerical Modeling in Flow and Water Quality

Numerical modeling is frequently used in coastal engineering research and application. One possible issue associated with using this method however, is that initial outcomes might differ from expectation. Modeling manipulation addresses this issue by changing the initial parameters which in turn affects the final output results.The advancement of modeling techniques in recent years has seen a gradual but steady accruement of knowledge regarding the relationship between input parameters and possible outputs, which has resulted in improved accuracy and efficiency methods. The advancements within this field have predominantly occurred on an individual basis and as such lack standardisation. An expert numerical modeler may use this knowledge subconsciously, yet may not know how to convey it to the model users. This paper has been written to introduce a systematic intelligent coding schema designed to organise current coastal engineering modeling manipulation knowledge into a standardised format. This system is relevant to the development of appropriate strategies for improved accuracy and efficiency as well as model modification to simulate specific real phenomena in prototype application cases.     

Selection and Calibration of Numerical Modeling in Flow and Water Quality

Numerical modeling is frequently used in coastal engineering research and application. One possible issue associated with using this method however, is that initial outcomes might differ from expectation. Modeling manipulation addresses this issue by changing the initial parameters which in turn affects the final output results. The advancement of modeling techniques in recent years has seen a gradual but steady accruement of knowledge regarding the relationship between input parameters and possible outputs, which has resulted in improved accuracy and efficiency methods. The advancements within this field have predominantly occurred on an individual basis and as such lack standardisation. An expert numerical modeler may use this knowledge subconsciously, yet may not know how to convey it to the model users. This paper has been written to introduce a systematic intelligent coding schema designed to organise current coastal engineering modeling manipulation knowledge into a standardised format. This system is relevant to the development of appropriate strategies for improved accuracy and efficiency as well as model modification to simulate specific real phenomena in prototype application cases.     

Assessment of anthropogenic threats to Chilean Mediterranean freshwater ecosystems: Literature review and expert opinions

Anthropogenic threats and their impacts on aquatic ecosystems have traditionally been evaluated only through literature reviews. Within the last decade, however, expert opinions have become an increasingly popular alternative for gaining complementary information to literature reviews. This study employed a standardized method for collecting bibliographic data combined with synthesize expert opinions to establish a current knowledge baseline on threats to Mediterranean freshwater ecosystems in Chile.We revised 79 scientific articles that included a variety of taxonomic groups containing fish (n = 32); macroinvertebrates (n = 17); amphibians (n = 18); freshwater plants (n = 8); and two taxonomic groups simultaneously (n = 4). From this review, we identified 14 threats, with land-use change, species introductions, and industrial/domestic effluents as salient factors. These findings were in agreement with results from expert opinion surveys (n = 46 researchers). An analysis by taxonomic group indicated freshwater fishes as most threatened by hydropower plants. Aquatic plants were most threatened by mining. Amphibians were most threatened by changes in soil use whereas aquatic macroinvertebrates by industrial/domestic effluents. Remarkably, the magnitude of threats identified in the literature review was of similar order than those catalogued by experts. Moreover, the primary threats identified here align with those reported in other Mediterranean regions of the world. Our combined approach using literature review and expert opinion was useful in determining the consistency of the main threats to each taxonomic group. Our findings can be used as a baseline to further research and in prioritizing management strategies for threatened Mediterranean ecosystems worldwide.     

A knowledge-based approach to regional acidification modelling

Data from over 2000 stations and knowledge from experts on atmospheric transport, soil geochemistry, lake chemistry, wetland processes and acidification modelling were assembled in an expert system. The data were grouped by aggregates of tertiary watersheds based on water chemistry knowledge. A set of expert rules was used to determine which of six existing models was most appropriate for a given set of data. Comparison of computed and observed alkalinity indicated median relative errors from 11.3–17.9%, with regression slopes ranging from 0.91–1.18 and regression coefficients between 0.82 and 0.99. The expert model performance was further confirmed with paleolimnological data and other independent sets of data. The sensitivity of the predicted alkalinity was illustrated by changing some of the rules. Given that the rules were acceptable by experts and produced reasonable agreement with observations, the knowledge-based system seemed a viable approach to the impact assessment of acidic deposition.     

Accounting for intracultural variability in first nation environmental knowledge: A requisite for environmental monitoring and impact assessments

In many environmental monitoring and impact assessment processes, Indigenous communities are treated as intellectually homogenous and intracultural variation in environmental knowledge often goes unaccounted for. This not only poses obvious risks to the effectiveness of environmental impact assessments but also gives standing to those who question the credibility of traditional ecological knowledge and its contribution to environmental monitoring and assessment programs altogether. In this paper we describe the steps that were taken to account for intracultural variability in First Nation knowledge of fish and the potential impacts associated with the Peace River oil sands development in Alberta, Canada. Involving the delivery of a household survey to 1,127 First Nation households in 11 Peace River communities, our approach was successful in identifying regional, community, and household variability in fishing activity, and has allowed us to differentiate novice from expert knowledge holders. This research demonstrates the need to account for intracultural variability in First Nations environmental knowledge in order for traditional ecological knowledge to make meaningful contributions to environmental monitoring and assessment programs.     

Biomass and number of fish impinged at a nuclear power plant by the Baltic Sea

The main aim of this study was to investigate the number and biomass of impinged fish at Forsmark Nuclear Power Plant in Sweden, located on the coast of the Baltic Sea. Of particular interest was the number of impinged individuals of the critically endangered European eel (Anguilla anguilla) which is regularly caught in the cooling system. Another aim was to determine the comparability of the results from Forsmark and results from impingement studies in other types of waters. Cross-systems studies make it possible to (1) estimate fish loss at plants where fish is not counted, and (2) to predict changes in fish loss from changes in electricity production or cooling water use. In 2010, 31,300,000 fish with a total biomass of 62,600 kg were impinged at Forsmark. In 2011, 27,300,000 fish weighing 38,500 kg were impinged. The maximum peak in total fish number and biomass occurred in spring. The most critical period for herring was in late summer and early autumn. Regarding eel, the largest impingement losses were recorded in November. The number of fish agreed with earlier established quantities of impinged fish in both freshwater and marine ecosystems. The study also estimated that 1,300 critically endangered eels could survive at Forsmark each year if a fish return system would be constructed to allow the passage of fish from the plant back to the Baltic Sea.     

Selection of suitable alternatives to reduce the environmental impact of road traffic noise using a fuzzy multi-criteria decision model

Road traffic noise is one of the most significant environmental impacts generated by transport systems. To this regard, the recent implementation of the European Environmental Noise Directive by Public Administrations of the European Union member countries has led to various noise action plans (NAPs) for reducing the noise exposure of EU inhabitants. Every country or administration is responsible for applying criteria based on their own experience or expert knowledge, but there is no regulated process for the prioritization of technical measures within these plans. This paper proposes a multi-criteria decision methodology for the selection of suitable alternatives against traffic noise in each of the road stretches included in the NAPs. The methodology first defines the main criteria and alternatives to be considered. Secondly, it determines the relative weights for the criteria and sub-criteria using the fuzzy extended analytical hierarchy process as applied to the results from an expert panel, thereby allowing expert knowledge to be captured in an automated way. A final step comprises the use of discrete multi-criteria analysis methods such as weighted sum, ELECTRE and TOPSIS, to rank the alternatives by suitability. To illustrate an application of the proposed methodology, this paper describes its implementation in a complex real case study: the selection of optimal technical solutions against traffic noise in the top priority road stretch included in the revision of the NAP of the regional road network in the province of Almeria (Spain).     

An evidential reasoning-based AHP approach for the selection of environmentally-friendly designs

Due to the stringent environmental regulatory requirements being imposed by cross-national bodies in recent years, manufacturers have to minimize the environmental impact of their products. Among those environmental impact evaluation tools available, Life Cycle Assessment (LCA) is often employed to quantify the product's environmental impact throughout its entire life cycle. However, owing to the requirements of expert knowledge in environmental science and vast effort for data collection in carrying out LCA, as well as the common absence of complete product information during product development processes, there is a need to develop a more suitable tool for product designers. An evidential reasoning-based approach, which aims at providing a fast-track method to perform design alternative evaluations for non-LCA experts, is therefore introduced as a new initiative to deal with the incomplete or uncertain information. The proposed approach also enables decision makers to quantitatively assess the life cycle phases and design alternatives by comparing their potential environmental impacts, thus effectively and efficiently facilitates the identification of greener designs. A case application is carried out to demonstrate the applicability of the proposed approach.     

Uncertain spatial reasoning of environmental risks in GIS using genetic learning algorithms

Modeling the impact of air pollution is one of the most important approaches for managing damages to the ecosystem. This problem can be solved by sensing and modeling uncertain spatial behaviors, defining topological rules, and using inference and learning capabilities in a spatial reasoning system. Reasoning, which is the main component of such complex systems, requires that proper rules be defined through expert judgments in the knowledge-based part. Use of genetic fuzzy capabilities enables the algorithm to learn and be tuned to proper rules in a flexible manner and increases the preciseness and robustness of operations. The main objective of this paper was to design and evaluate a spatial genetic fuzzy system, with the goal of assessing environmental risks of air pollution due to oil well fires during the Persian Gulf War. Dynamic areas were extracted and monitored through images from NOAA, and the data were stored in an efficient spatial database. Initial spatial knowledge was determined by expert consideration of the application characteristics, and the inference engine was performed with genetic learning (GL) algorithms. Finally, GL (0.7 and 0.03), GL (0.7 and 0.08), GL (0.98 and 0.03), GL (0.98 and 0.08), and Cordon learning methods were evaluated with test and training data related to samples extracted from Landsat thematic mapper satellite images. Results of the implementation showed that GL (0.98, 0.03) was more precise than the other methods for learning and tuning rules in the concerned application.     

An expert system for water quality modelling

The RAISON-micro (Regional Analysis by Intelligent System ON a micro-computer) expert system is being used to predict the effects of mine effluents on receiving waters in Ontario. The potential of this system to assist regulatory agencies and mining industries to define more acceptable effluent limits was shown in an initial study. This system has been further developed so that the expert system helps the model user choose the most appropriate model for a particular application from a hierarchy of models. The system currently contains seven models which range from steady state to time dependent models, for both conservative and nonconservative substances in rivers and lakes. The menu driven expert system prompts the model user for information such as the nature of the receiving water system, the type of effluent being considered, and the range of background data available for use as input to the models. The system can also be used to determine the nature of the environmental conditions at the site which are not available in the textual information database, such as the components of river flow. Applications of the water quality expert system are presented for representative mine sites in the Timmins area of Ontario.     

Distinguishing Between Interference and Exploitation Competition for Shelter in a Mobile Fish Population

Understanding the functional significance of shelter for animal populations requires knowledge of the behavioural mechanisms that govern the dynamics of shelter use. Exploitation of shelters may be impeded by mutual interference, yet interference competition can be difficult to distinguish from exploitation competition. We used bullheads (Cottus gobio) as a model system of mobile fish to investigate the effect of intraspecific competition on shelter use. A series of field experiments was conducted under controlled conditions of shelter availability and population density. For each experiment, the location of each individual fish was observed over a period of 10 days. We then constructed a continuous-time Markov-chain model for the movement of fish between shelters and the open stream, which explicitly parameterised exploitation competition and interference competition for shelter and which accounted for two different size-classes of fish. By using a stochastic rather than a deterministic model, we were able to account for the distribution of fish across shelters, and not just the average occupation. Analysis of the model showed strong evidence of exploitation competition, which was highly dependent on body size, and an increased departure rate from shared shelters. Over and above exploitation, interference competition limited the ability of unsheltered fish to colonise vacant shelters at high population densities. Different formulations of the interference competition were compared using the Akaike Information Criterion. The formulation that best fitted the observations modelled interference competition as an increasing function of average shelter occupancy rather than population density per se.     

A moving target—incorporating knowledge of the spatial ecology of fish into the assessment and management of freshwater fish populations

Freshwater fish move vertically and horizontally through the aquatic landscape for a variety of reasons, such as to find and exploit patchy resources or to locate essential habitats (e.g., for spawning). Inherent challenges exist with the assessment of fish populations because they are moving targets. We submit that quantifying and describing the spatial ecology of fish and their habitat is an important component of freshwater fishery assessment and management. With a growing number of tools available for studying the spatial ecology of fishes (e.g., telemetry, population genetics, hydroacoustics, otolith microchemistry, stable isotope analysis), new knowledge can now be generated and incorporated into biological assessment and fishery management. For example, knowing when, where, and how to deploy assessment gears is essential to inform, refine, or calibrate assessment protocols. Such information is also useful for quantifying or avoiding bycatch of imperiled species. Knowledge of habitat connectivity and usage can identify critically important migration corridors and habitats and can be used to improve our understanding of variables that influence spatial structuring of fish populations. Similarly, demographic processes are partly driven by the behavior of fish and mediated by environmental drivers. Information on these processes is critical to the development and application of realistic population dynamics models. Collectively, biological assessment, when informed by knowledge of spatial ecology, can provide managers with the ability to understand how and when fish and their habitats may be exposed to different threats. Naturally, this knowledge helps to better evaluate or develop strategies to protect the long-term viability of fishery production. Failure to understand the spatial ecology of fishes and to incorporate spatiotemporal data can bias population assessments and forecasts and potentially lead to ineffective or counterproductive management actions.     

Outline of an environmental information system

The development of an environmental information system necessitates a phased implementation approach. Phase 1 includes the elements that are traditionally viewed as comprising monitoring and assessment activities. Analysis tools for interpretive work are identified including statistics, modelling, and GIS. Phase 2 follows the information flow beyond project reporting to examine the process of decision making. The inclusion of other forms of knowledge beyond the strictly scientific is necessary where the development of multi-sectoral decisions must be made. Phase 3 extends the decision-making process to the policy development and implementation field. This is accomplished by the inclusion of expert systems as advanced decision support systems which enable the manager to test various hypotheses and policy options prior to commitment. In addressing water resource issues, the importance of setting achievable and enforceable sectoral criteria and standards for industrial, agricultural, and drinking water supplies is discussed with reference to both usage and effluent criteria. Quality assurance and control is an area which must be critically addressed in any water resource project. The implementation of quality control programs must extend from the field sampling procedures and laboratory standard methods to both inter- and intra-laboratory tests and the development and maintenance of databases.     

The mini mobile environmental monitoring unit: a novel bio-assessment tool

This paper introduces a new bio-assessment tool, the mini-mobile environmental monitoring unit (MMU). The MMU is a portable, lightweight, energy-efficient, miniaturized laboratory that provides a low-flow system for on-site exposure of aquatic animals to local receiving waters in a protected, controllable environment. Prototypes of the MMU were tested twice in week-long studies conducted during the summers of 2008 and 2009, and in a 12-day study in 2010. In 2008, fathead minnows and polar organic chemical integrative samplers (POCIS) were deployed downstream from the Hastings, Nebraska wastewater treatment plant (WWTP), a waterway known to contain estrogenic contaminants in biologically active concentrations. In 2009, minnows and POCIS were deployed downstream, upstream and within the Grand Island, Nebraska WWTP, a site where the estrogenic contaminants had been detected, but were found at levels below those necessary to directly impact fish. In 2010, an advanced prototype was tested at the Sauk Center, Minnesota WWTP to compare its performance with that of traditional fish exposure methods including caged fish and static-renewal laboratory testing of effluent. Results from the prototype illustrate the capabilities of the MMU and offer an inexpensive monitoring tool to integrate the effects of pollutant sources with temporally varying composition and concentration.     

An expert system for assessing safety and security of heterogeneous public water sources

We have implemented a diagnostic system designed to advise on the likely causes of sanitary problems with public water sources. The approach to the problem makes extensive use of rule based expert systems and multi media information (maps, data, text, expert knowledge). The rules were based on actual water survey data, with a weighting scheme designed to highlight causes of health risks approximately in reverse order of importance (where such order may be presumed to exist). Out put is a vailable in one of several languages.     

Multimedia Integrated Modeling for Environmental Protection: Introduction to a Collaborative Framework

The EPA's Office of Research and Development is embarking on a long term project to develop a Multimedia Integrated Modeling System (MIMS). The system will have capabilities to represent the transport and fate of nutrients and chemical stressors over multiple spatial and temporal scales. MIMS will be designed to improve the environmental management community's ability to evaluate the impact of air and water quality and watershed management practices on stream and estuarine conditions. The system will provide a computer-based problem-solving environment for testing understanding of multimedia (atmosphere, land, water) environmental problems, such as the movement of chemicals through the hydrologic cycle, and the response of aquatic ecological systems to land-use change, with initial emphasis on the fish health endpoint. The design will attempt to combine the state-of-the-art in computer science, system design, and numerical analysis (i.e., object-oriented design, parallel processing, advanced numerical libraries including analytic elements) with the latest advancements in process level science (hydrology, atmospheric sciences, chemistry, ecology). The purpose of this paper is to introduce a vision for a MIMS and anticipate the challenges to its development.     

Land cover classification with an expert system approach using Landsat ETM imagery: a case study of Trabzon

The main objective of this study is to generate a knowledge base which is composed of user-defined variables and included raster imagery, vector coverage, spatial models, external programs, and simple scalars and to develop an expert classification using Landsat 7 (ETM+) imagery for land cover classification in a part of Trabzon city. Expert systems allow for the integration of remote-sensed data with other sources of geo-referenced information such as land use data, spatial texture, and digital elevation model to obtain greater classification accuracy. Logical decision rules are used with the various datasets to assign class values for each pixel. Expert system is very suitable for the work of image interpretation as a powerful means of information integration. Landsat ETM data acquired in the year 2000 were initially classified into seven classes for land cover using a maximum likelihood decision rule. An expert system was constructed to perform post-classification sorting of the initial land cover classification using additional spatial datasets such as land use data. The overall accuracy of expert classification was 95.80%. Individual class accuracy ranged from 75% to 100% for each class.     

Effect of diel period and season on seining effort required to detect changes in Lake Erie beach fish assemblages

Using beach seining data from Lake Erie beaches, we examined the effect of diel period and season on two common study objectives considered sensitive to sampling effort: (1) has fish assemblage composition changed between two sampling time periods?, and (2) how many samples are necessary to provide a representative sample of the fish assemblage? Across a range of effect sizes and power levels, sample sizes were estimated for species richness, total fish abundance, round goby (Neogobius melanostomus) abundance and emerald shiner (Notropis atherinoides) abundance. For most sampling periods and effect sizes, several hundred to several thousand seine hauls were estimated to be required for detecting large-scale (e.g. basin-wide) changes in round goby and emerald shiner abundance. Depending on the indicator of interest, night seining (either spring or fall) was interpreted as the most efficient approach. For both seasons, sampling effort beyond 40 to 50 night seine hauls results in few additional species being detected. Both species accumulation curves and estimated sample sizes indicate that less effort is required to detect species richness changes than fish abundance. For most effect sizes and power levels, estimates of the number of seine hauls required to detect changes in fish abundance are unrealistically large for most monitoring programs. More practical monitoring strategies could be achieved by adopting alternative indicators (e.g. guilds), a more liberal significance level (p=0.1), a paired-site sampling design, or including reference sites.