Coronapandemin-en smygande kris vintern 2020

Why did the Swedish Government fail to act earlier against the Covid-19-virus in the light of the many foreshadowing outbreaks in China and in Italy and other EU Member States? With the help of the concept creeping crisis (smygande kris), this article analyses the tardiness with which the Swedish authorities acted to prevent the spread of the virus in the early stages of the pandemic (January – February 2020). The term refers to the phenomenon of belated measures despite extensive knowledge of slow-acting threats with sudden outbursts such as pandemics and global warming. The article explains the procrastination of Swedish actions as a result of psychological repression (“it couldn’t happen here in our country”), as well as cognitive delays that meant that understanding the threat evolution in the abstract did not spur action in proportion to the insight (“we saw it coming, but didn’t act until we felt it in our everyday life”). It ends by discussing possible ways to create more practically and temporally informed knowledge (“know-how”, “know-when”) of creeping crises for the generation of timely action able to stop these before they explode into acute crises.Smygande krise

Coronapandemin-en smygande kris vintern 2020

Why did the Swedish Government fail to act earlier against the Covid-19-virus in the light of the many foreshadowing outbreaks in China and in Italy and other EU Member States? With the help of the concept creeping crisis (smygande kris), this article analyses the tardiness with which the Swedish authorities acted to prevent the spread of the virus in the early stages of the pandemic (January – February 2020). The term refers to the phenomenon of belated measures despite extensive knowledge of slow-acting threats with sudden outbursts such as pandemics and global warming. The article explains the procrastination of Swedish actions as a result of psychological repression (“it couldn’t happen here in our country”), as well as cognitive delays that meant that understanding the threat evolution in the abstract did not spur action in proportion to the insight (“we saw it coming, but didn’t act until we felt it in our everyday life”). It ends by discussing possible ways to create more practically and temporally informed knowledge (“know-how”, “know-when”) of creeping crises for the generation of timely action able to stop these before they explode into acute crises.Smygande krise

WEBAP - Wave Energized Baltic Aeration Pump - final report

This report is only available in English

Use of congener profiles to trace sources of chlorinated dibenzo-p-dioxins and dibenzofurans in environmental samples

In this study different multivariate methods were used to link the content of dioxins and furans in biota to different sources. The data used in the study were mainly from already existing analysis off dioxins and furans but also some data has been generated by sampling and analysis within the project. Lack of data, especially from biological samples, limited the modelling to the set of 17 toxic congeners comprised of seven dioxins and 10 furans. Two different multivariate modelling methods were used, principal component analysis (PCA) and positive matrix factorisation (PMF) for the source apportionment. The PCA shows that herrings from the Bothnian Bay and Gulf of Finland has well defined pattern. The spread in pattern is larger for the Herrings caught in The Baltic proper and Bothnian bay. Also the age of the Herring effect the patterns, 2-year old Herring are well grouped and separated from older Herrings. When adding source samples (flue gas, combustion, background) to the PCA for biota the only well defined overlap were between biota and flue gas. The PMF model for Baltic Herring points to three significant sources. One of them is identified to be a background/combustion. One of the other sources is not fully identified but some parts of the pattern are likely flue gas. The last source can not be identified by use of data collected in this project.In this study different multivariate methods were used to link the content of dioxins and furans in biota to different sources. The data used in the study were mainly from already existing analysis off dioxins and furans but also some data has been generated by sampling and analysis within the project. Lack of data, especially from biological samples, limited the modelling to the set of 17 toxic congeners comprised of seven dioxins and 10 furans. Two different multivariate modelling methods were used, principal component analysis (PCA) and positive matrix factorisation (PMF) for the source apportionment. The PCA shows that herrings from the Bothnian Bay and Gulf of Finland has well defined pattern. The spread in pattern is larger for the Herrings caught in The Baltic proper and Bothnian bay. Also the age of the Herring effect the patterns, 2-year old Herring are well grouped and separated from older Herrings. When adding source samples (flue gas, combustion, background) to the PCA for biota the only well defined overlap were between biota and flue gas. The PMF model for Baltic Herring points to three significant sources. One of them is identified to be a background/combustion. One of the other sources is not fully identified but some parts of the pattern are likely flue gas. The last source can not be identified by use of data collected in this project