A fuzzy knowledge-based decision support system for groundwater pollution risk evaluation

In this paper we propose a decision support system that can provide information on the environmental impact of anthropic activities by examining their effects on groundwater quality. We use the combined value of both intrinsic vulnerability of a specific local aquifer, obtained by implementing a parametric managerial model (SINTACS), and a degree of hazard value, which takes into account specific human activities. Incomplete information is notoriously common in environmental planning. To overcome this deficiency we apply an algorithmic and a qualitative approach, based on expert judgment incorporated into the system's knowledge base. The decision support system takes into account the uncertainty of the environmental domain by using fuzzy logic and evaluates the reliability of the results according to information availability.     

Tools based on multivariate statistical analysis for classification of soil and groundwater in Apulian agricultural sites

In this paper, the results obtained from multivariate statistical techniques such as PCA (Principal component analysis) and LDA (Linear discriminant analysis) applied to a wide soil data set are presented. The results have been compared with those obtained on a groundwater data set, whose samples were collected together with soil ones, within the project “Improvement of the Regional Agro-meteorological Monitoring Network (2004–2007)”. LDA, applied to soil data, has allowed to distinguish the geographical origin of the sample from either one of the two macroaeras: Bari and Foggia provinces vs Brindisi, Lecce e Taranto provinces, with a percentage of correct prediction in cross validation of 87%. In the case of the groundwater data set, the best classification was obtained when the samples were grouped into three macroareas: Foggia province, Bari province and Brindisi, Lecce and Taranto provinces, by reaching a percentage of correct predictions in cross validation of 84%. The obtained information can be very useful in supporting soil and water resource management, such as the reduction of water consumption and the reduction of energy and chemical (nutrients and pesticides) inputs in agriculture.     

Optimal extension of the rain gauge monitoring network of the Apulian Regional Consortium for Crop Protection

The goal of this paper is to provide a methodology for assessing the optimal localization of new monitoring stations within an existing rain gauge monitoring network. The methodology presented, which uses geostatistics and probabilistic techniques (simulated annealing) combined with GIS instruments, could be extremely useful in any area where an extension of whatever existing environmental monitoring network is planned. The methodology has been applied to the design of an extension to a rainfall monitoring network in the Apulia region (South Italy). The considered monitoring network is managed by the Apulian Regional Consortium for Crop Protection (ARCCP), and, currently consists of 45 gauging stations distributed over the regional territory, mainly located on the basis of administrative needs. Fifty new stations have been added to the existing monitoring network, split in two groups: 15 fixed and 35 mobile stations. Two different methods were applied and tested: the Minimization of the Mean of Shortest Distances method (MMSD) and Ordinary Kriging (OK) whose related objective function is estimation variance. The MMSD, being a purely geometric method, produced a spatially uniform configuration of the gauging stations. On the contrary, the approach based on the minimization of the average of the kriging estimation variances, produced a less regular configuration, though a more reliable one from a spatial standpoint. Nevertheless, the MMSD approach was chosen, since the ARCCP's intention was to create a new monitoring network characterized by uniform spatial distribution throughout the regional territory. This was the most important constraint given to the project by the ARCCP, whose main objective was to accomplish a territorial network capable of detecting hazardous events quickly. A seasonal aggregation of the available rainfall data was considered. The choice of the temporal aggregation in quarterly averages allowed four different optimal configurations to be determined per season. The overlapping of the four configurations allowed a number of new station locations, which tended to remain fixed season after season, to be identified. Other stations, instead, changed their coordinates considerably over the four seasons. Constraints were defined in order to avoid placing new monitoring locations either near existing stations, belonging to other Agencies, or near the coast line.     

Vulnerability and risk evaluation of agricultural nitrogen pollution for Hungary's main aquifer using DRASTIC and GLEAMS models

In recent years, the significant improvement in point source depuration technologies has highlighted problems regarding, in particular, phosphorus and nitrogen pollution of surface and groundwater caused by agricultural non-point (diffuse) sources (NPS). Therefore, there is an urgent need to determine the relationship between agriculture and chemical and ecological water quality.This is a worldwide problem, but it is particularly relevant in countries, such as Hungary, that have recently become members of the European Community. The Italian Foreign Ministry has financed the PECO (Eastern Europe Countries Project) projects, amongst which is the project that led to the present paper, aimed at agricultural sustainability in Hungary, from the point of view of NPS. Specifically, the aim of the present work has been to study nitrates in Hungary's main aquifer. This study compares a model showing aquifer intrinsic vulnerability to pollution (using the DRASTIC parameter method; Aller et al. [Aller, L., Truman, B., Leher, J.H., Petty, R.J., 1986. DRASTIC: A Standardized System for Evaluating Ground Water Pollution Potential Using Hydrogeologic Settings. US NTIS, Springfield, VA.]) with a field-scale model (GLEAMS; Knisel [Knisel, W.G. (Ed.), 1993. GLEAMS—Groudwater Leaching Effects of Agricultural Management Systems, Version 3.10. University of Georgia, Coastal Plain Experimental Station, Tifton, GA.]) developed to evaluate the effects of agricultural management systems within and through the plant root zone. Specifically, GLEAMS calculates nitrate nitrogen lost by runoff, sediment and leachate.Groundwater monitoring probes were constructed for the project to measure: (i) nitrate content in monitored wells; (ii) tritium (³H) hydrogen radioisotope, as a tool to estimate the recharge conditions of the shallow groundwater; (iii) nitrogen isotope ratio δ¹⁵N, since nitrogen of organic and inorganic origin can easily be distinguished.The results obtained are satisfactory, above all regarding the DRASTIC evaluation method, which is shown to satisfactorily explain both low and high aquifer vulnerability, and furthermore proves to be a good tool for zoning hydrogeological regions in terms of natural system susceptibility to pollution. The GLEAMS model, however, proves not to be immediately usable for predictions, above all due to the difficulty in finding sufficient data for the input parameters. It remains a good tool, but only after an accurate validation, for decision support systems, in the specific case to integrate intrinsic vulnerability, from DRASTIC (or similar methods), with land use nitrate loads from GLEAMS, or similar methods.The PECO project has proved a positive experience to highlight the fundamental points of a decision support system, aimed to mitigate the nitrate risk for groundwater coming from Hungarian agricultural areas.     

Integrating conflict analysis and consensus reaching in a decision support system for water resource management

The importance of shared decision processes in water management derives from the awareness of the inadequacy of traditional--i.e. engineering--approaches in dealing with complex and ill-structured problems. It is becoming increasingly obvious that traditional problem solving and decision support techniques, based on optimisation and factual knowledge, have to be combined with stakeholder based policy design and implementation. The aim of our research is the definition of an integrated decision support system for consensus achievement (IDSS-C) able to support a participative decision-making process in all its phases: problem definition and structuring, identification of the possible alternatives, formulation of participants' judgments, and consensus achievement. Furthermore, the IDSS-C aims at structuring, i.e. systematising the knowledge which has emerged during the participative process in order to make it comprehensible for the decision-makers and functional for the decision process. Problem structuring methods (PSM) and multi-group evaluation methods (MEM) have been integrated in the IDSS-C. PSM are used to support the stakeholders in providing their perspective of the problem and to elicit their interests and preferences, while MEM are used to define not only the degree of consensus for each alternative, highlighting those where the agreement is high, but also the consensus label for each alternative and the behaviour of individuals during the participative decision-making. The IDSS-C is applied experimentally to a decision process regarding the use of treated wastewater for agricultural irrigation in the Apulia Region (southern Italy).