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Thomas Fruergaard Astrup - One of the best experts on this subject based on the ideXlab platform.
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statistical analysis of solid Waste Composition data arithmetic mean standard deviation and correlation coefficients
Waste Management, 2017Co-Authors: Maklawe Essonanawe Edjabou, J A Martinfernandez, Charlotte Scheutz, Thomas Fruergaard AstrupAbstract:Abstract Data for fractional solid Waste Composition provide relative magnitudes of individual Waste fractions, the percentages of which always sum to 100, thereby connecting them intrinsically. Due to this sum constraint, Waste Composition data represent closed data, and their interpretation and analysis require statistical methods, other than classical statistics that are suitable only for non-constrained data such as absolute values. However, the closed characteristics of Waste Composition data are often ignored when analysed. The results of this study showed, for example, that unavoidable animal-derived food Waste amounted to 2.21 ± 3.12% with a confidence interval of (−4.03; 8.45), which highlights the problem of the biased negative proportions. A Pearson’s correlation test, applied to Waste fraction generation (kg mass), indicated a positive correlation between avoidable vegetable food Waste and plastic packaging. However, correlation tests applied to Waste fraction Compositions (percentage values) showed a negative association in this regard, thus demonstrating that statistical analyses applied to Compositional Waste fraction data, without addressing the closed characteristics of these data, have the potential to generate spurious or misleading results. Therefore, ¨Compositional data should be transformed adequately prior to any statistical analysis, such as computing mean, standard deviation and correlation coefficients.
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importance of Waste Composition for life cycle assessment of Waste management solutions
Journal of Cleaner Production, 2017Co-Authors: Valentina Bisinella, Ramona Gotze, Knut Conradsen, Anders Damgaard, Thomas Hojlund Christensen, Thomas Fruergaard AstrupAbstract:Abstract The Composition of Waste materials has fundamental influence on environmental emissions associated with Waste treatment, recycling and disposal, and may play an important role also for the Life Cycle Assessment (LCA) of Waste management solutions. However, very few assessments include effects of the Waste Composition and Waste LCAs often rely on poorly justified data from secondary sources. This study systematically quantifies the influence and uncertainty on LCA results associated with selection of Waste Composition data. Three archetypal Waste management scenarios were modelled with the Waste LCA model EASETECH based on detailed Waste Composition data from the literature. The influence from Waste Composition data on the LCA results was quantified with a step-wise Global Sensitivity Analysis (GSA) approach involving contribution, sensitivity, uncertainty and discernibility analyses. The Waste Composition data contributed significantly to the LCA results and the uncertainty associated with these results. The importance of 405 individual Waste properties was evaluated in comparison with 345 technology parameters. Overall, less than 10 physico-chemical properties dominated the output uncertainty of the LCA results, although these properties had low sensitivity in the model. Moreover, the uncertainties associated with the physico-chemical properties were responsible for output uncertainties that spanned from impacts to benefits. The GSA approach applied in this study constitutes a valuable tool for systematically assessing the importance of Waste Composition and for consciously collecting and using Waste Composition data within LCAs of Waste management systems.
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physico chemical characterisation of material fractions in household Waste overview of data in literature
Waste Management, 2016Co-Authors: Ramona Gotze, Charlotte Scheutz, Alessio Boldrin, Thomas Fruergaard AstrupAbstract:State-of-the-art environmental assessment of Waste management systems rely on data for the physico-chemical Composition of individual material fractions comprising the Waste in question. To derive the necessary inventory data for different scopes and systems, literature data from different sources and backgrounds are consulted and combined. This study provides an overview of physico-chemical Waste characterisation data for individual Waste material fractions available in literature and thereby aims to support the selection of data fitting to a specific scope and the selection of uncertainty ranges related to the data selection from literature. Overall, 97 publications were reviewed with respect to employed characterisation method, regional origin of the Waste, number of investigated parameters and material fractions and other qualitative aspects. Descriptive statistical analysis of the reported physico-chemical Waste Composition data was performed to derive value ranges and data distributions for element concentrations (e.g. Cd content) and physical parameters (e.g. heating value). Based on 11,886 individual data entries, median values and percentiles for 47 parameters in 11 individual Waste fractions are presented. Exceptional values and publications are identified and discussed. Detailed datasets are attached to this study, allowing further analysis and new applications of the data.
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municipal solid Waste Composition sampling methodology statistical analyses and case study evaluation
Waste Management, 2015Co-Authors: Maklawe Essonanawe Edjabou, Ramona Gotze, Charlotte Scheutz, Morten Jensen, Kostyantyn Pivnenko, Claus Petersen, Thomas Fruergaard AstrupAbstract:Abstract Sound Waste management and optimisation of resource recovery require reliable data on solid Waste generation and Composition. In the absence of standardised and commonly accepted Waste characterisation methodologies, various approaches have been reported in literature. This limits both comparability and applicability of the results. In this study, a Waste sampling and sorting methodology for efficient and statistically robust characterisation of solid Waste was introduced. The methodology was applied to residual Waste collected from 1442 households distributed among 10 individual sub-areas in three Danish municipalities (both single and multi-family house areas). In total 17 tonnes of Waste were sorted into 10–50 Waste fractions, organised according to a three-level (tiered approach) facilitating comparison of the Waste data between individual sub-areas with different fractionation (Waste from one municipality was sorted at “Level III”, e.g. detailed, while the two others were sorted only at “Level I”). The results showed that residual household Waste mainly contained food Waste (42 ± 5%, mass per wet basis) and miscellaneous combustibles (18 ± 3%, mass per wet basis). The residual household Waste generation rate in the study areas was 3–4 kg per person per week. Statistical analyses revealed that the Waste Composition was independent of variations in the Waste generation rate. Both, Waste Composition and Waste generation rates were statistically similar for each of the three municipalities. While the Waste generation rates were similar for each of the two housing types (single-family and multi-family house areas), the individual percentage Composition of food Waste, paper, and glass was significantly different between the housing types. This indicates that housing type is a critical stratification parameter. Separating food leftovers from food packaging during manual sorting of the sampled Waste did not have significant influence on the proportions of food Waste and packaging materials, indicating that this step may not be required.
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biogenic carbon in combustible Waste Waste Composition variability and measurement uncertainty
Waste Management & Research, 2013Co-Authors: Anna Warberg Larsen, Helmut Rechberger, Karsten Fuglsang, Niels Hald Pedersen, Johann Fellner, Thomas Fruergaard AstrupAbstract:Obtaining accurate data for the contents of biogenic and fossil carbon in thermally-treated Waste is essential for determination of the environmental profile of Waste technologies. Relations between the variability of Waste chemistry and the biogenic and fossil carbon emissions are not well described in the literature. This study addressed the variability of biogenic and fossil carbon in combustible Waste received at a municipal solid Waste incinerator. Two approaches were compared: (1) radiocarbon dating ((14)C analysis) of carbon dioxide sampled from the flue gas, and (2) mass and energy balance calculations using the balance method. The ability of the two approaches to accurately describe short-term day-to-day variations in carbon emissions, and to which extent these short-term variations could be explained by controlled changes in Waste input Composition, was evaluated. Finally, the measurement uncertainties related to the two approaches were determined. Two flue gas sampling campaigns at a full-scale Waste incinerator were included: one during normal operation and one with controlled Waste input. Estimation of carbon contents in the main Waste types received was included. Both the (14)C method and the balance method represented promising methods able to provide good quality data for the ratio between biogenic and fossil carbon in Waste. The relative uncertainty in the individual experiments was 7-10% (95% confidence interval) for the (14)C method and slightly lower for the balance method.
Charlotte Scheutz - One of the best experts on this subject based on the ideXlab platform.
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statistical analysis of solid Waste Composition data arithmetic mean standard deviation and correlation coefficients
Waste Management, 2017Co-Authors: Maklawe Essonanawe Edjabou, J A Martinfernandez, Charlotte Scheutz, Thomas Fruergaard AstrupAbstract:Abstract Data for fractional solid Waste Composition provide relative magnitudes of individual Waste fractions, the percentages of which always sum to 100, thereby connecting them intrinsically. Due to this sum constraint, Waste Composition data represent closed data, and their interpretation and analysis require statistical methods, other than classical statistics that are suitable only for non-constrained data such as absolute values. However, the closed characteristics of Waste Composition data are often ignored when analysed. The results of this study showed, for example, that unavoidable animal-derived food Waste amounted to 2.21 ± 3.12% with a confidence interval of (−4.03; 8.45), which highlights the problem of the biased negative proportions. A Pearson’s correlation test, applied to Waste fraction generation (kg mass), indicated a positive correlation between avoidable vegetable food Waste and plastic packaging. However, correlation tests applied to Waste fraction Compositions (percentage values) showed a negative association in this regard, thus demonstrating that statistical analyses applied to Compositional Waste fraction data, without addressing the closed characteristics of these data, have the potential to generate spurious or misleading results. Therefore, ¨Compositional data should be transformed adequately prior to any statistical analysis, such as computing mean, standard deviation and correlation coefficients.
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physico chemical characterisation of material fractions in household Waste overview of data in literature
Waste Management, 2016Co-Authors: Ramona Gotze, Charlotte Scheutz, Alessio Boldrin, Thomas Fruergaard AstrupAbstract:State-of-the-art environmental assessment of Waste management systems rely on data for the physico-chemical Composition of individual material fractions comprising the Waste in question. To derive the necessary inventory data for different scopes and systems, literature data from different sources and backgrounds are consulted and combined. This study provides an overview of physico-chemical Waste characterisation data for individual Waste material fractions available in literature and thereby aims to support the selection of data fitting to a specific scope and the selection of uncertainty ranges related to the data selection from literature. Overall, 97 publications were reviewed with respect to employed characterisation method, regional origin of the Waste, number of investigated parameters and material fractions and other qualitative aspects. Descriptive statistical analysis of the reported physico-chemical Waste Composition data was performed to derive value ranges and data distributions for element concentrations (e.g. Cd content) and physical parameters (e.g. heating value). Based on 11,886 individual data entries, median values and percentiles for 47 parameters in 11 individual Waste fractions are presented. Exceptional values and publications are identified and discussed. Detailed datasets are attached to this study, allowing further analysis and new applications of the data.
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municipal solid Waste Composition sampling methodology statistical analyses and case study evaluation
Waste Management, 2015Co-Authors: Maklawe Essonanawe Edjabou, Ramona Gotze, Charlotte Scheutz, Morten Jensen, Kostyantyn Pivnenko, Claus Petersen, Thomas Fruergaard AstrupAbstract:Abstract Sound Waste management and optimisation of resource recovery require reliable data on solid Waste generation and Composition. In the absence of standardised and commonly accepted Waste characterisation methodologies, various approaches have been reported in literature. This limits both comparability and applicability of the results. In this study, a Waste sampling and sorting methodology for efficient and statistically robust characterisation of solid Waste was introduced. The methodology was applied to residual Waste collected from 1442 households distributed among 10 individual sub-areas in three Danish municipalities (both single and multi-family house areas). In total 17 tonnes of Waste were sorted into 10–50 Waste fractions, organised according to a three-level (tiered approach) facilitating comparison of the Waste data between individual sub-areas with different fractionation (Waste from one municipality was sorted at “Level III”, e.g. detailed, while the two others were sorted only at “Level I”). The results showed that residual household Waste mainly contained food Waste (42 ± 5%, mass per wet basis) and miscellaneous combustibles (18 ± 3%, mass per wet basis). The residual household Waste generation rate in the study areas was 3–4 kg per person per week. Statistical analyses revealed that the Waste Composition was independent of variations in the Waste generation rate. Both, Waste Composition and Waste generation rates were statistically similar for each of the three municipalities. While the Waste generation rates were similar for each of the two housing types (single-family and multi-family house areas), the individual percentage Composition of food Waste, paper, and glass was significantly different between the housing types. This indicates that housing type is a critical stratification parameter. Separating food leftovers from food packaging during manual sorting of the sampled Waste did not have significant influence on the proportions of food Waste and packaging materials, indicating that this step may not be required.
Ramona Gotze - One of the best experts on this subject based on the ideXlab platform.
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importance of Waste Composition for life cycle assessment of Waste management solutions
Journal of Cleaner Production, 2017Co-Authors: Valentina Bisinella, Ramona Gotze, Knut Conradsen, Anders Damgaard, Thomas Hojlund Christensen, Thomas Fruergaard AstrupAbstract:Abstract The Composition of Waste materials has fundamental influence on environmental emissions associated with Waste treatment, recycling and disposal, and may play an important role also for the Life Cycle Assessment (LCA) of Waste management solutions. However, very few assessments include effects of the Waste Composition and Waste LCAs often rely on poorly justified data from secondary sources. This study systematically quantifies the influence and uncertainty on LCA results associated with selection of Waste Composition data. Three archetypal Waste management scenarios were modelled with the Waste LCA model EASETECH based on detailed Waste Composition data from the literature. The influence from Waste Composition data on the LCA results was quantified with a step-wise Global Sensitivity Analysis (GSA) approach involving contribution, sensitivity, uncertainty and discernibility analyses. The Waste Composition data contributed significantly to the LCA results and the uncertainty associated with these results. The importance of 405 individual Waste properties was evaluated in comparison with 345 technology parameters. Overall, less than 10 physico-chemical properties dominated the output uncertainty of the LCA results, although these properties had low sensitivity in the model. Moreover, the uncertainties associated with the physico-chemical properties were responsible for output uncertainties that spanned from impacts to benefits. The GSA approach applied in this study constitutes a valuable tool for systematically assessing the importance of Waste Composition and for consciously collecting and using Waste Composition data within LCAs of Waste management systems.
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physico chemical characterisation of material fractions in household Waste overview of data in literature
Waste Management, 2016Co-Authors: Ramona Gotze, Charlotte Scheutz, Alessio Boldrin, Thomas Fruergaard AstrupAbstract:State-of-the-art environmental assessment of Waste management systems rely on data for the physico-chemical Composition of individual material fractions comprising the Waste in question. To derive the necessary inventory data for different scopes and systems, literature data from different sources and backgrounds are consulted and combined. This study provides an overview of physico-chemical Waste characterisation data for individual Waste material fractions available in literature and thereby aims to support the selection of data fitting to a specific scope and the selection of uncertainty ranges related to the data selection from literature. Overall, 97 publications were reviewed with respect to employed characterisation method, regional origin of the Waste, number of investigated parameters and material fractions and other qualitative aspects. Descriptive statistical analysis of the reported physico-chemical Waste Composition data was performed to derive value ranges and data distributions for element concentrations (e.g. Cd content) and physical parameters (e.g. heating value). Based on 11,886 individual data entries, median values and percentiles for 47 parameters in 11 individual Waste fractions are presented. Exceptional values and publications are identified and discussed. Detailed datasets are attached to this study, allowing further analysis and new applications of the data.
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municipal solid Waste Composition sampling methodology statistical analyses and case study evaluation
Waste Management, 2015Co-Authors: Maklawe Essonanawe Edjabou, Ramona Gotze, Charlotte Scheutz, Morten Jensen, Kostyantyn Pivnenko, Claus Petersen, Thomas Fruergaard AstrupAbstract:Abstract Sound Waste management and optimisation of resource recovery require reliable data on solid Waste generation and Composition. In the absence of standardised and commonly accepted Waste characterisation methodologies, various approaches have been reported in literature. This limits both comparability and applicability of the results. In this study, a Waste sampling and sorting methodology for efficient and statistically robust characterisation of solid Waste was introduced. The methodology was applied to residual Waste collected from 1442 households distributed among 10 individual sub-areas in three Danish municipalities (both single and multi-family house areas). In total 17 tonnes of Waste were sorted into 10–50 Waste fractions, organised according to a three-level (tiered approach) facilitating comparison of the Waste data between individual sub-areas with different fractionation (Waste from one municipality was sorted at “Level III”, e.g. detailed, while the two others were sorted only at “Level I”). The results showed that residual household Waste mainly contained food Waste (42 ± 5%, mass per wet basis) and miscellaneous combustibles (18 ± 3%, mass per wet basis). The residual household Waste generation rate in the study areas was 3–4 kg per person per week. Statistical analyses revealed that the Waste Composition was independent of variations in the Waste generation rate. Both, Waste Composition and Waste generation rates were statistically similar for each of the three municipalities. While the Waste generation rates were similar for each of the two housing types (single-family and multi-family house areas), the individual percentage Composition of food Waste, paper, and glass was significantly different between the housing types. This indicates that housing type is a critical stratification parameter. Separating food leftovers from food packaging during manual sorting of the sampled Waste did not have significant influence on the proportions of food Waste and packaging materials, indicating that this step may not be required.
Maklawe Essonanawe Edjabou - One of the best experts on this subject based on the ideXlab platform.
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statistical analysis of solid Waste Composition data arithmetic mean standard deviation and correlation coefficients
Waste Management, 2017Co-Authors: Maklawe Essonanawe Edjabou, J A Martinfernandez, Charlotte Scheutz, Thomas Fruergaard AstrupAbstract:Abstract Data for fractional solid Waste Composition provide relative magnitudes of individual Waste fractions, the percentages of which always sum to 100, thereby connecting them intrinsically. Due to this sum constraint, Waste Composition data represent closed data, and their interpretation and analysis require statistical methods, other than classical statistics that are suitable only for non-constrained data such as absolute values. However, the closed characteristics of Waste Composition data are often ignored when analysed. The results of this study showed, for example, that unavoidable animal-derived food Waste amounted to 2.21 ± 3.12% with a confidence interval of (−4.03; 8.45), which highlights the problem of the biased negative proportions. A Pearson’s correlation test, applied to Waste fraction generation (kg mass), indicated a positive correlation between avoidable vegetable food Waste and plastic packaging. However, correlation tests applied to Waste fraction Compositions (percentage values) showed a negative association in this regard, thus demonstrating that statistical analyses applied to Compositional Waste fraction data, without addressing the closed characteristics of these data, have the potential to generate spurious or misleading results. Therefore, ¨Compositional data should be transformed adequately prior to any statistical analysis, such as computing mean, standard deviation and correlation coefficients.
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municipal solid Waste Composition sampling methodology statistical analyses and case study evaluation
Waste Management, 2015Co-Authors: Maklawe Essonanawe Edjabou, Ramona Gotze, Charlotte Scheutz, Morten Jensen, Kostyantyn Pivnenko, Claus Petersen, Thomas Fruergaard AstrupAbstract:Abstract Sound Waste management and optimisation of resource recovery require reliable data on solid Waste generation and Composition. In the absence of standardised and commonly accepted Waste characterisation methodologies, various approaches have been reported in literature. This limits both comparability and applicability of the results. In this study, a Waste sampling and sorting methodology for efficient and statistically robust characterisation of solid Waste was introduced. The methodology was applied to residual Waste collected from 1442 households distributed among 10 individual sub-areas in three Danish municipalities (both single and multi-family house areas). In total 17 tonnes of Waste were sorted into 10–50 Waste fractions, organised according to a three-level (tiered approach) facilitating comparison of the Waste data between individual sub-areas with different fractionation (Waste from one municipality was sorted at “Level III”, e.g. detailed, while the two others were sorted only at “Level I”). The results showed that residual household Waste mainly contained food Waste (42 ± 5%, mass per wet basis) and miscellaneous combustibles (18 ± 3%, mass per wet basis). The residual household Waste generation rate in the study areas was 3–4 kg per person per week. Statistical analyses revealed that the Waste Composition was independent of variations in the Waste generation rate. Both, Waste Composition and Waste generation rates were statistically similar for each of the three municipalities. While the Waste generation rates were similar for each of the two housing types (single-family and multi-family house areas), the individual percentage Composition of food Waste, paper, and glass was significantly different between the housing types. This indicates that housing type is a critical stratification parameter. Separating food leftovers from food packaging during manual sorting of the sampled Waste did not have significant influence on the proportions of food Waste and packaging materials, indicating that this step may not be required.
Felicitas Schneider - One of the best experts on this subject based on the ideXlab platform.
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discussion on the methodology for determining food Waste in household Waste Composition studies
Waste Management, 2011Co-Authors: Sandra Lebersorger, Felicitas SchneiderAbstract:Food Waste has become an increasingly discussed topic in recent years. However, there is little authoritative data on food Waste quantities and Composition and systematic and comparable data are missing. Household Waste Composition analyses, which are often carried out routinely at regular or irregular intervals, provide an opportunity for obtaining data about food Waste at both local and regional levels. The results of prior Waste Composition studies are not really comparable due to the different classifications, definitions and methods used; in addition, these are mostly insufficiently described and not reproducible by a third party. The aim of this paper is to discuss a methodology for determining the proportion of food Waste in household Waste Composition studies, by analysing specific problems and possible solutions. For that purpose, findings from the literature are analysed and the approach and results of a Composition analysis of residual Waste of a stratified sample (urban, rural area) are presented. The study suggests that in order to avoid a significant loss of information, Waste should not be sieved before sorting and packed food Waste should be classified into the relevant food Waste category together with its packaging. The case study showed that the overall influence of the proportion of food packaging included in the food Waste category, which amounted to only 8%, did not significantly influence the results and can therefore be disregarded.
