1,721,025 research outputs found
A decision support system for the assessment and management of surface waters
Full text linkWater protection is one of the priorities in the preservation of human health and environmental sustainable conditions. Since 2000, the European Union Water Framework Directive (EU-WFD), which establishes a framework for community action in the field of water policy, asks Member States to adopt appropriate assessment methods and management solutions to reach a good ecological status of water bodies by 2015.
In this context the lack of appropriate risk-based methodologies and decision support tools was identified. For this reason, we developed a novel Multi Criteria Decision Analysis (MCDA) based methodology inside a Geographic Information System (GIS) tool for surface water quality and socio-economic evaluations. The resulting Decision Support System (DSS) is called MODELKEY.
The MODELKEY DSS is a freeware application built as a Rich Client Platform and Eclipse plug-in for a Java/Eclipse based open source GIS framework (uDig). It has been successfully applied to three case studies representing European key areas: the rivers Llobregat (Spain), Elbe (Czech Republic, Germany) and Scheldt (France, Belgium and The Netherlands). This work is part of the FP6 European project MODELKEY (Contract-No. 511237 (GOCE)).La protezione dell'acqua è una delle priorità nella conservazione della salute umana e di condizioni ambientali sostenibili. A partire dal 2000, la direttiva quadro sulle acque emanata dall'Unione Europea (EU-WFD), la quale stabilisce un insieme comune di azioni da intraprendere nell'ambito delle politiche relative alle acque, richiede agli stati membri di adottare modelli di valutazione e soluzioni manageriali appropriate al fine di raggiungere uno stato ecologico buono delle copri idrici entro il 2015. In questo contesto è stata identificata la mancanza di metodologie e sistemi di supporto alle decisioni appropriati che siano basati sul rischio. Per questo motivo abbiamo sviluppato una metodologia innovativa basata sull'analisi multicriteriale (MCDA) sviluppata all'interno di un Geographic Information System (GIS) allo scopo di valutare lo stato delle acque anche in relazione alle condizioni socio-economiche circostanti. Il sistema di supporto alle decisioni (DSS) che ne è scaturito si chiama MODELKEY. Il Modelkey DSS è un'applicazione gratuita creata come Rich Client Platform (RCP) e plug-in per nel sistema Java/Eclipse che si appoggia ad un GIS open source (uDig). Il DSS è stato applicato con successo a tre casi di studio che rappresentano altrettante aree di interesse a livello europeo: i fiumi Llobregat (Spagna), Elba (Repubblica Ceca e Germania) e Schelda (Francia, Belgio, Olanda). Questo lavoro è parte del progetto europeo Modelkey all'interno dell'FP6 (Contract-No. 511237 (GOCE))
“AMORE” Decision Support System for probabilistic Ecological Risk Assessment - Part II: Effect assessment of the case study on cyanide
No full textEcotoxicological data are highly important for risk assessment processes and are used for deriving environmental quality criteria, which are enacted for assuring the good quality of waters, soils or sediments and achieving desirable environmental quality objectives. Therefore, it is of significant importance the evaluation of the reliability and relevance of available data for analysing their possible use in the aforementioned processes. In this context, a new methodology which has been developed based on Multi-Criteria Decision Analysis (MCDA) techniques, is being used, demonstrated and tested for analysing the reliability and relevance of ecotoxicological data of cyanide (which are produced through laboratory biotests for individual effects). The proposed methodology is also used for the production of Weighted by Data Quality Species Sensitivity Distributions (SSD-WDQ), as a component of the Ecological Risk Assessment of chemicals in aquatic systems. The SSD-WDQ production resulted in the estimation of environmental quality criteria (hazard concentration affecting 5% and 50% of the species). The proposed work is part of the development of the AMORE Decision Support System (DSS) for the application of probabilistic Ecological Risk Assessment (ERA), presented in the companion paper (Isigonis et al., 2019). The DSS has been tested through a case study on ERA of cyanide in the watershed of river Selune in France. The paper presents the ‘Effect Assessment’ of cyanide, based on the aforementioned methodologies. The main results presented in the paper are the probabilistic analysis of the estimated species sensitivity on cyanide (Effect Assessment) and the calculation of Hazardous Concentration (HCx) of the same contaminant in the Selune river area, based on the functionalities of the DSS. The results are described and discussed in detail, with the use of various graphs and indices. The indices are calculated for all the available ecotoxicological data, as well as for the data on trophic levels or taxonomic groups separately. An effect comparison is presented between the innovative methodologies included in the DSS and the currently existing methodologies
CARBON FOOTPRINT OF MUNICIPAL SOLID WASTE COLLECTION IN THE TREVISO AREA (ITALY)
Full text linkCarbon Footprint (CF) is an environmental indicator used in Life Cycle Assessment (LCA) that allows measuring the total amount of CO2 emissions caused directly or indirectly by an activity or accumulated through the life cycle stages of a product (ISO 14064-14067).
In this article CF was used to analyse and assess the environmental impacts of the resources used for the collection of municipal solid waste by the company Contarina S.p.A. Contarina oversees waste management for part of the Treviso province (Italy), serving about 260,000 appliances in 50 municipalities distributed in the territory.
The presented case study assessed CF of year 2015 related the whole fleet involved in door-to-door collection of municipal solid waste without taking into account treatment processes. In addition, a future scenario, in which part of the current fleet is replaced by compressed natural gas engine (CNG) based vehicles, was assessed and compared to the current status. The CF was performed by adapting the SimaPro software from PRè, one of the most widely used LCA software since the nineties, by introducing fuel based analysis and creating CNG lorries. The analysis aimed at improving sustainability of Contarina’s services while fostering an informed development and testing of new technologies aimed at reducing its overall greenhouse gas emissions
A Weight of Evidence approach to classify nanomaterials according to the EU Classification, Labelling and Packaging Regulation criteria
Full text linkIn the context of the European Union (EU) Horizon 2020 GRACIOUS project (Grouping, Read-Across, Characterisation and classification framework for regulatory risk assessment of manufactured nanomaterials and Safer design of nano-enabled products), we proposed a quantitative Weight of Evidence (WoE) approach for hazard classification of nanomaterials (NMs). This approach is based on the requirements of the European Regulation on Classification, Labelling and Packaging of Substances and Mixtures (the CLP regulation), which implements the United Nations' Globally Harmonized System of Classification and Labelling of Chemicals (UN GHS) in the European Union. The goal of this WoE methodology is to facilitate classification of NMs according to CLP criteria, following the decision trees defined in ECHA's CLP regulatory guidance. In the WoE, results from heterogeneous studies are weighted according to data quality and completeness criteria, integrated, and then evaluated by expert judgment to obtain a hazard classification, resulting in a coherent and justifiable methodology. Moreover, the probabilistic nature of the proposed approach enables highlighting the uncertainty in the analysis. The proposed methodology involves the following stages: (1) collection of data for different NMs related to the endpoint of interest: each study related to each NM is referred as a Line of Evidence (LoE); (2) computation of weighted scores for each LoE: each LoE is weighted by a score calculated based on data quality and completeness criteria defined in the GRACIOUS project; (3) comparison and integration of the weighed LoEs for each NM: A Monte Carlo resampling approach is adopted to quantitatively and probabilistically integrate the weighted evidence; and (4) assignment of each NM to a hazard class: according to the results, each NM is assigned to one of the classes defined by the CLP regulation. Furthermore, to facilitate the integration and the classification of the weighted LoEs, an online R tool was developed. Finally, the approach was tested against an endpoint relevant to CLP (Aquatic Toxicity) using data retrieved from the eNanoMapper database, results obtained were consistent to results in REACH registration dossiers and in recent literature
Identifying sustainability communicators in urban regeneration: Integrating individual and relational attributes
No full textThe paper advances a conceptualization of sustainability in urban regeneration as communicative practice taking place within networks of social actors. To demonstrate the potential of this perspective, we propose an interdisciplinary methodology integrating social network analysis from sociology and multi-criteria decision analysis (fuzzy logic) from operations research to calculate a sustainability communicator score for each actor involved in a regeneration network. The score is based on three dimensions: a sustainability vision (relying on the three pillar model of sustainability), a formal network influence dimension (based on organizational practice and decision-making position) and an informal network influence dimension (drawing on degree, betweenness, eigenvector and closeness centrality measures from social network analysis). The framework allows the identification and ranking of sustainability communicators, based on the preferences of specific users, while also allowing for variable degrees of vagueness. We illustrate the methodology by means of a case study of a social network of actors (N = 28) involved in the sustainable regeneration of a brownfield site in Porto Marghera, Venice, Italy. The methodology is expandable beyond the actor level to allow for the ranking of more complex network configurations for promoting sustainability
Normalised similarity assessment to inform grouping of advanced multi-component nanomaterials by means of an Asymmetric Sigmoid function
No full textThis manuscript presents a procedure for similarity assessment as a basis for grouping of multi component nanomaterials (MCNMs). This methodology is an adaptation of the approach by Zabeo et al. (2022), which includes an impactful change: the calculated similarities are normalised in the [0,1] domain by means of asymmetric Logistic scaling to simplify comparisons among properties' distances. This novel approach allows for grouping of nanomaterials that is not affected by the dataset, so that group membership will not change when new candidates are included in the set of assessed materials. It can be applied to assess groups of MCNMs as well as mixed groups of multi and single component nanomaterials as well as chemicals. To facilitate the application of the proposed methodology, a software script was developed by using the Python programming language, which is currently undergoing migration to a user-friendly web-based tool. The presented approach was tested against a real industrial case study provided by the Andalusian Innovation Centre for Sustainable Solution (CIAC): SiO2-ZnO hybrid nanocomposite used in building coatings, which is designed to facilitate photocatalytic removal of NOx gases from the atmosphere. The results of applying the methodology in the case study demonstrated that ZnO is dissimilar from the other candidates mainly due to its different dissolution profiles
Multi-risk evaluation of climate change impacts in coastal zones
No full textClimate change-related impacts (e.g. storm surges, coastal erosion, sea-level rise) in coastal zone are likely to be exacerbated by changes in exposure and vulnerability, mainly driven by the rapid growth of population and urbanization in coastal zones. Accordingly, in the future, coastal zone could be potentially affected by interactions, synergies and trade-offs of multiple hazards and multi-risk approaches are needed to support decision-makers toward a new paradigm of multi-hazard and risk management. An advanced multi-risk methodology was developed for estimating cumulative impacts related to climate change at the regional (i.e. sub-national) scale allowing a quick scan assessment and ranking of natural systems and human assets at risk. The methodology was applied to the North Adriatic coast (Italy) producing a range of GIS-based multi-hazard, exposure, multi-vulnerability and multi-risk maps which provides useful information for local public authorities to set future priorities for adaptation and define future plans for shoreline and coastal management in view of climate change
DESYCO: A decision support system for the regional risk assessment of climate change impacts in coastal zones
Full text linkSeveral decision support systems were developed in recent years to encourage climate adaptation planning in coastal areas, especially at a national to global scale. However, few prototypes are easy to use and accessible for decision-makers to evaluate and manage risks locally. DESYCO is a GIS based decision support system specifically designed to better understand the risks that climate change poses at the regional/subnational scale (e.g. the effect of sea level rise and coastal erosion on human assets and ecosystems) and set the context of strategic adaptation planning within Integrated Coastal Zone Management. It implements a Regional Risk Assessment (RRA) methodology allowing the spatial assessment of multiple climate change impacts in coastal areas and the ranking of key elements at risk (beaches, wetlands, protected areas, urban and agricultural areas). The core of the system is a Multi-Criteria Decision Analysis (MCDA) model used to operationalize the steps of the RRA (hazard, exposure, susceptibility, risk and damage assessment) by integrating a blend of information from climate scenarios (global/regional climate projections and hydrodynamic/hydrological simulations) and from non-climate vulnerability factors (physical, environmental and socio-economic features of the analysed system). User-friendly interfaces simplify the interaction with the system, providing guidance for risk mapping, results communication and understanding.\ud
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DESYCO was applied to low-lying coastal plains and islands (the North Adriatic Sea, the Gulf of Gabes and the Republic of Mauritius), river basins and groundwater systems (Upper Plain of Veneto and Friuli-Venezia Giulia, Marche Region). The paper presents the RRA methodology, the structure of DESYCO and its software architecture, showing the capabilities of the tool to support decision making and climate proofing in a wide range of situations (e.g. shoreline planning, land use and water resource management, flood risk reduction)
“AMORE” Decision Support System for probabilistic Ecological Risk Assessment - Part I: Exposure and risk assessment of the case study on cyanide
No full textEcological Risk Assessment of chemicals in fluvial systems is a highly researched topic, but its importance for the environmental protection of our planet is vital. Thus, new developments and improvements to existing methodologies are proposed constantly, for providing more advanced tools and more accurate results to researchers and other interested parties. In the field of probabilistic Ecological Risk Assessment, a new Decision Support System is proposed, developed, tested and evaluated. The AMORE DSS is a modular DSS, which incorporates a series of new methodologies, and is built upon the notions of ‘Exposure Assessment’ ‘Effect Assessment’ and ‘Risk Assessment’. The AMORE Decision Support System has been developed as part of the AMORE research project (French National Research Agency project). The DSS provides a set of tools for analysing and integrating both exposure and effect information in order to evaluate the risk for species living on a given contaminated aquatic system in terms of the Potentially Affected Fraction. The DSS has been tested through a case study on ERA of cyanide in the watershed of river Selune in France. The paper presents the ‘Exposure Assessment’ and ‘Risk Assessment’ of the cyanide case study, as well as the complete functionalities of the AMORE DSS. The main results presented in the paper are the statistical analysis of the measured environmental concentrations of cyanide (Exposure Assessment) and the probabilistic ‘Risk assessment’ of the same contaminant in the area of interest, based on the functionalities of the DSS. The results are described and discussed in detail with the use of various graphs and risk indices. The risk indices are calculated for all the available ecotoxicological data, as well as for the data on trophic levels or taxonomic groups separately. A risk comparison is presented between the innovative methodologies included in the DSS and the currently existing methodologies
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