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Uncertainty bounds for metal levels in waste wood
Linköping University, Department of Mathematics, Statistics. Linköping University, Faculty of Arts and Sciences.
Linköping University, Department of Mathematics, Statistics. Linköping University, Faculty of Arts and Sciences.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Estimates of the mean levels of various metals in wastes are normally presented without any measures of uncertainty, and, if such measures are given, they are almost always based on subjective judgments. Here, we show that adequate statistical techniques exist for calculating uncertainty bounds. If data are sparse, Bayesian statistical methods can be employed to combine expert knowledge with the information provided by actually measuring metal concentrations in wastes. If larger datasets are available, a viable alternative is to use bias-corrected bootstrap intervals to compute uncertainty bounds. To facilitate selection of a method, we illustrate how sensitive the Bayesian inference is to the prior knowledge that is incorporated into the analysis, and how outliers influence the obtained uncertainty bounds. Inasmuch as levels of metals in solid wastes can vary substantially and the underlying probability distributions are often highly skewed, we examine the performance of lognormal models.

Keyword [en]
uncertainty, Bayesian inference, bootstrap, lognormal, metals, waste wood
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:liu:diva-86477OAI: oai:DiVA.org:liu-86477DiVA: diva2:577850
Available from: 2012-12-17 Created: 2012-12-17 Last updated: 2012-12-17
In thesis
1. Organizing physical flow data: from input-output tables to data warehouses
Open this publication in new window or tab >>Organizing physical flow data: from input-output tables to data warehouses
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Data on flows of materials and substances through the economy and the environment are collected by many different organizations and play a key role in the science of industrial ecology. In this thesis, a framework is suggested for structuring and organizing such data. First, the investigation focuses on the quantities of primary interest in material flow studies and how they can be stored and organized in a data warehouse. This process is shown to provide easy access to data, well-structured data management, a basis for knowledge discovery, and effective analysis of collected data. Secondly, a theoretical framework is proposed for handling and structuring multidimensional flow data, and for facilitating mathematics-assisted modeling in industrial ecology. In particular, it is shown how mathematical operations can be used to merge and compare flow data originating from different studies. Finally, it is illustrated how bootstrap analysis, Bayesian models and balancing procedures can be employed to systematize the quality and uncertainty assessment of physical flow data. Together, these three different aspects of handling physical flow data constitute a new framework that offers better knowledge, quality, and consistency of the data used in industrial ecology.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2005. 42 p.
Series
Linköping Studies in Statistics, ISSN 1651-1700 ; 5
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-30083 (URN)15549 (Local ID)91-85297-55-0 (ISBN)15549 (Archive number)15549 (OAI)
Public defence
2005-09-30, Glashuset, B-huset, Campus Valla, Linköping, 10:15 (Swedish)
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2012-12-17

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Löfving, ErikGrimvall, Anders

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