Description and Propagation of Uncertainty in Input Parameters in Environmental Fate Models

Today, chemical risk and safety assessments rely heavily on the estimation of environmental fate by models. The key compound‐related properties in such models describe partitioning and reactivity. Uncertainty in determining these properties can be separated into random and systematic (incompleteness) components, requiring different types of representation. Here, we evaluate two approaches that are suitable to treat also systematic errors, fuzzy arithmetic, and probability bounds analysis. When a best estimate (mode) and a range can be computed for an input parameter, then it is possible to characterize the uncertainty with a triangular fuzzy number (possibility distribution) or a corresponding probability box bound by two uniform distributions. We use a five‐compartment Level I fugacity model and reported empirical data from the literature for three well‐known environmental pollutants (benzene, pyrene, and DDT) as illustrative cases for this evaluation. Propagation of uncertainty by discrete probability calculus or interval arithmetic can be done at a low computational cost and gives maximum flexibility in applying different approaches. Our evaluation suggests that the difference between fuzzy arithmetic and probability bounds analysis is small, at least for this specific case. The fuzzy arithmetic approach can, however, be regarded as less conservative than probability bounds analysis if the assumption of independence is removed. Both approaches are sensitive to repeated parameters that may inflate the uncertainty estimate. Uncertainty described by probability boxes was therefore also propagated through the model by Monte Carlo simulation to show how this problem can be avoided.     

Sense of coherence and ill health among the unemployed and re-employed after closure of an assembly plant

The aim of this study was to investigate the relationship between employment status, ill health and sense of coherence(SOC). A cross-sectional sample of the former workers at the closed down Volvo Kalmar plant in Sweden was used. A questionnaire was mailed to all identified subjects 2 years after the closure of the plant. Responses were obtained from 344 persons, of whom 173 were re-employed and 92 were unemployed. They answered the questionnaire anonymously on background variables, SOC and ill health (physical and psychological symptoms). A notable difference was found between the re-employed and unemployed, where the unemployed reported symptoms significantly more often as well as a significantly weaker SOC. A main finding in a four cell model indicated that unemployment had a rather low impact on symptoms in the presence of a strong SOC. Structural equation modelling (SEM) was performed with simultaneous analysis of the unemployed and reemployed. The SEM analysis indicated that SOC seemed to have a greater impact on psychological symptoms among unemployed compared with re-employed persons. However, it should be noted that the present study does not allow for the determination of cause-effect relationships.     

Supplier Rationalization: A Sourcing Decision Model

We draw on extant literature on strategic sourcing and supply base rationalization to anchor our argument that measuring supplier performance diversity is germane to executing an effective supply base rationalization strategy. We explicate how a novel approach to data envelopment analysis (DEA) efficiency assessment can be utilized to measure this performance diversity. More specifically, our methods are anchored in cross efficiency analysis in DEA that allows for evaluating the efficiency of a supplier with respect to the optimal weights (strengths) of its peers. This methodology is applied to an actual supplier dataset of a large multinational telecommunications company in categorizing their supply base into groups for effective supplier rationalization. We conclude that measuring and analyzing the performance diversity within the framework of DEA provides a mechanism for firms to better balance a rationalized and diversified supply base with unique skills.     

Children with disabilities in Swedish child welfare – a differentiating and disabling practice

ABSTRACT

Research shows that children with disabilities are victims of violence and abuse to a higher extent than other children and thus need support from social services. In Sweden, cooperation between two different social services units is required to support children with disabilities in socially vulnerable families. In this study, we have examined the intersection between children and disability in a Swedish social services context from the perspective of childhood studies and disability studies. The reasoning of the two units including the child perspective emerged during focus group interviews based on two vignettes. The results show two different rationalities, which has consequences for the disabled child. In spite of a social policy where the ‘best interests of the child’ are meant to prevail and disabilities are meant to be interpreted as barriers in society, children with disabilities seem to be reduced to individuals who are lacking in ability and competency and who are profoundly victimised by power structures that favour the adult perspective in social services.     

Variability and Uncertainty in Swedish Exposure Factors for Use in Quantitative Exposure Assessments

Information of exposure factors used in quantitative risk assessments has previously been compiled and reported for U.S. and European populations. However, due to the advancement of science and knowledge, these reports are in continuous need of updating with new data. Equally important is the change over time of many exposure factors related to both physiological characteristics and human behavior. Body weight, skin surface, time use, and dietary habits are some of the most obvious examples covered here. A wealth of data is available from literature not primarily gathered for the purpose of risk assessment. Here we review a number of key exposure factors and compare these factors between northern Europe—here represented by Sweden—and the United States. Many previous compilations of exposure factor data focus on interindividual variability and variability between sexes and age groups, while uncertainty is mainly dealt with in a qualitative way. In this article variability is assessed along with uncertainty. As estimates of central tendency and interindividual variability, mean, standard deviation, skewness, kurtosis, and multiple percentiles were calculated, while uncertainty was characterized using 95% confidence intervals for these parameters. The presented statistics are appropriate for use in deterministic analyses using point estimates for each input parameter as well as in probabilistic assessments.     

Effective local governance assisting vulnerable groups: The case of youth not in employment,education or training (NEETs) in Sweden

Youth not in employment, education or training (NEETs) have been analyzed from either individual or macro-structural perspectives, while policy discussions have emphasized national policy. This disregards (i) the substantial variation in NEET rates within countries, and (ii) the importance of local governance for this variation. We examine these issues in Sweden through the lens of interactive governance. Theoretically, four aspects of collective action are highlighted: identification of local NEET subgroups, perceptions of problems and of solutions, and stakeholder relationships. Empirically, an initial multi-level regression analysis of all 290 Swedish municipalities provided the basis for semi-structured interviews regarding local work with NEETs in 20 strategically selected municipalities. The qualitative data are here analyzed using fuzzy-set qualitative comparative analysis. The results suggest that municipalities where local governance combines three of the four aspects, namely identifying NEET subgroups and sharing perceptions of problems and of solutions, have lower NEET shares than predicted.