Suchergebnisse

A variety of chemicals is included in the household hazardous waste and the dental waste fractions that are sadly both parts of municipal solid waste in Greece. These chemical compounds have hazardous properties according to international and European regulations. In Greece, the categorization of household hazardous waste is not indicated by any legislation, whereas for dental waste the legislation is existent since May 2012, but the development of a management plan undertaken by the Hellenic Dental Association is not yet active. Given that both waste fractions are managed with other municipal solid wastes, they are spotted in solid waste management facilities causing multiple impacts and challenging the labors' health and safety status. Desk research involving literature and commercial research was conducted in order for the hazardous substances of each of the aforementioned waste stream to be pinpointed; collected data were compiled into databases for those two specific waste streams and were categorized based on their hazardous properties and the waste facility they are most likely to be found in. Parallel field researches were conducted to: (i) determine the uncertainty level of the fractions, composition, and health/environmental impacts, and (ii) specific parameters were introduced to determine their impact due to the status of health and safety conditions within the management facilities in Greece. Despite the fact that HHW is almost 10% of the total MSW, it was found that 4.00% of their compounds involve a toxic risk and 7.16% of them involve combination risks for humans working in treatment facilities; ten chemical compounds, which are included in this fraction, are categorized as R39/23/24/25 (toxic). On the other hand, in DW, 8.82% of the included chemical compounds involve a toxic risk and 11.76% of them involve combination risks for humans. The qualitative and quantitative analysis of those waste fractions presented in this paper will pave the way toward organization of both waste streams' management plan followed by compiled strategies and recommendations to divert them from the municipal waste stream and lead them to safe and sustainable management paths.

The present paper addresses the problem of locating solid waste management facilities.Specifically, it studies and proposes optimal alternative solutions for the Greek Region of Peloponnese,by examining facilities for transferring, sorting, treating and landfilling of wastes. Quantitative and qualitative databases concerning the current solid waste management at the Region have been created and used by the model. A customized mixed-integer linear network model has been developed and solved for various evaluation criteria on a single-criterion basis by the use of a location-allocation modeling framework.The solutions resulting from the parametrical application of the multicriterial method ELECTRE III are then ranked for the entire criteria-spectrum. The best alternative scenario is presented for the Region in accordance with current legislation on waste management, which maximizes environmental benefits and promotes recycling, in the frame of sustainable waste management.

We summarise results from a workshop on 'Model Benchmarking and Quality Assurance' of the EU-Network of Excellence ACCENT, including results from other activities (e.g. COST Action 732) and publications. A formalised evaluation protocol is presented, i.e. a generic formalism describing the procedure of how to perform a model evaluation. This includes eight steps and examples from global model applications which are given for illustration. The first and important step is concerning the purpose of the model application, i.e. the addressed underlying scientific or political question. We give examples to demonstrate that there is no model evaluation per se, i.e. without a focused purpose. Model evaluation is testing, whether a model is fit for its purpose. The following steps are deduced from the purpose and include model requirements, input data, key processes and quantities, benchmark data, quality indicators, sensitivities, as well as benchmarking and grading. We define 'benchmarking' as the process of comparing the model output against either observational data or high fidelity model data, i.e. benchmark data. Special focus is given to the uncertainties, e.g. in observational data, which have the potential to lead to wrong conclusions in the model evaluation if not considered carefully.

We summarise results from a workshop on "Model Benchmarking and Quality Assurance" of the EU-Network of Excellence ACCENT, including results from other activities (e.g. COST Action 732) and publications. A formalised evaluation protocol is presented, i.e. a generic formalism describing the procedure of how to perform a model evaluation. This includes eight steps and examples from global model applications which are given for illustration. The first and important step is concerning the purpose of the model application, i.e. the addressed underlying scientific or political question. We give examples to demonstrate that there is no model evaluation per se, i.e. without a focused purpose. Model evaluation is testing, whether a model is fit for its purpose. The following steps are deduced from the purpose and include model requirements, input data, key processes and quantities, benchmark data, quality indicators, sensitivities, as well as benchmarking and grading. We define "benchmarking" as the process of comparing the model output against either observational data or high fidelity model data, i.e. benchmark data. Special focus is given to the uncertainties, e.g. in observational data, which have the potential to lead to wrong conclusions in the model evaluation if not considered carefully.

We summarise results from a workshop on "Model Benchmarking and Quality Assurance" of the EU-Network of Excellence ACCENT, including results from other activities (e.g. COST Action 732) and publications. A formalised evaluation protocol is presented, i.e. a generic formalism describing the procedure of how to perform a model evaluation. This includes eight steps and examples from global model applications which are given for illustration. The first and important step is concerning the purpose of the model application, i.e. the addressed underlying scientific or political question. We give examples to demonstrate that there is no model evaluation per se, i.e. without a focused purpose. Model evaluation is testing, whether a model is fit for its purpose. The following steps are deduced from the purpose and include model requirements, input data, key processes and quantities, benchmark data, quality indicators, sensitivities, as well as benchmarking and grading. We define "benchmarking" as the process of comparing the model output against either observational data or high fidelity model data, i.e. benchmark data. Special focus is given to the uncertainties, e.g. in observational data, which have the potential to lead to wrong conclusions in the model evaluation if not considered carefully.

We summarise results from a workshop on "Model Benchmarking and Quality Assurance" of the EU-Network of Excellence ACCENT, including results from other activities (e.g. COST Action 732) and publications. A formalised evaluation protocol is presented, i.e. a generic formalism describing the procedure of how to perform a model evaluation. This includes eight steps and examples from global model applications which are given for illustration. The first and important step is concerning the purpose of the model application, i.e. the addressed underlying scientific or political question. We give examples to demonstrate that there is no model evaluation per se, i.e. without a focused purpose. Model evaluation is testing, whether a model is fit for its purpose. The following steps are deduced from the purpose and include model requirements, input data, key processes and quantities, benchmark data, quality indicators, sensitivities, as well as benchmarking and grading. We define "benchmarking" as the process of comparing the model output against either observational data or high fidelity model data, i.e. benchmark data. Special focus is given to the uncertainties, e.g. in observational data, which have the potential to lead to wrong conclusions in the model evaluation if not considered carefully. ; published

International audience ; This chapter provides a review, derived from the extended survey conducted within the APPRAISAL project, of the integrated assessment methodologies used in different countries to design air quality plans and to estimate the effects of emission abatement policy options on human health. The final purpose of this review is to foster the dissemination of knowledge on integrated assessment for air quality planning at regional and local scales, and to provide policy makers and regulatory bodies across EU member states with a broader understanding of the underlying scientific concepts.

International audience ; This chapter provides a review, derived from the extended survey conducted within the APPRAISAL project, of the integrated assessment methodologies used in different countries to design air quality plans and to estimate the effects of emission abatement policy options on human health. The final purpose of this review is to foster the dissemination of knowledge on integrated assessment for air quality planning at regional and local scales, and to provide policy makers and regulatory bodies across EU member states with a broader understanding of the underlying scientific concepts.