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Abstract: The modelling of land use change is a way to analyse future scenarios by modelling different pathways. Application of spatial data of different scales coupled with socio-economic data makes it possible to explore and test the understanding of land use change relations. In the EU-FP7 research project PASHMINA (Paradigm Shift modelling and innovative approaches), three storylines of future transportation paradigm shifts towards 2040 are created. These storylines are translated into spatial planning strategies and modelled using the cellular automata model LUCIA. For the modelling, an Eastern Danish case area was selected, comprising of the Copenhagen metropolitan area and its hinterland. The different scenarios are described using a range of different descriptive GIS datasets. These include mapping of accessibility based on public and private transportation, urban density and structure, and distribution of jobs and population. These indicators are then incorporated in the model calculations as factors determining urban development, related to the scenario outlines. The results calculated from the scenarios reveals the great difference in urban distribution that different spatial planning strategies can produce, changing the shape of the urban landscape. The scenarios visualized showed to outline different planning strategies that could be implemented, creating a more homogenous urban structure targeted at a reduction of transportation work and thus energy consumption. This will lead to less impact on climate from transportation based on a more optimal localization and transport infrastructure strategy. [Copyright &y& Elsevier]

Abstract: Ecological risk and vulnerability maps can be used to improve the analysis of pollutant risks and communication to stakeholders. Often, such maps are made for one pollutant at the time. We used the results of wildlife vulnerability analysis, a novel trait-based risk assessment approach, to map overall vulnerability of habitats in Denmark to various metals and one insecticide. These maps were combined with maps of estimated soil concentrations for the same compounds divided by their Maximum Permissible Concentrations. This combination yielded relative risk maps that can be used to assess where the highest risk conditions to wildlife from these individual pollutants in Denmark occur (hot spot identification). In order to show how cumulative risk maps can be made, the maps of the individual pollutants were combined assuming different mechanisms of joint toxicity: no addition, concentration addition, antagonism and synergism. The study demonstrated that with an accurate set of geographical and ecological data one can use the results of vulnerability analysis to make relevant ecological risk maps that show hot spot areas for risks of single or cumulative risks from soil pollutants. [Copyright &y& Elsevier]

Abstract: This paper illustrates, by a case study, how to apply the conceptual Worst-Case Definition (WCD) model, developed in the methodological paper in the current journal, by Sørensen et al. (2010-this issue). The case is about eco-toxicological risk assessment of pesticides under Danish conditions. Cumulative aspects are included on a conceptual basis as elements of the worst-case conditions. This defines factors that govern the risk assessment, including location in time and space of risk “hotspots”. Two pillars of concern drive the conceptual modelling: (1) What to protect (denoted Protected Units (PUs)) and (2) the reason for increased risk level (denoted Causes of Risks (CRs)). Both PUs and CRs are analysed using hierarchical procedures that facilitate a complete listing of concrete factors governing increased risk for adverse effect due to agricultural usage of pesticide. The factors governing pesticide risk are combined in a context that combines the protection of relevant groupings of organisms with the factors for increased risk level for each of these. Identification of the most important relations between defined types of PUs and CRs is illustrated using expert knowledge. Existing databases are used to form spatial distributed risk indicators as estimators for a selection of important relations between PUs and CRs. This paper illustrates how the WCD model can break down the complex issue of uncertainty into fractions that are more open for evaluations. Finally, it shows application of risk indicators in a multi-criterion analysis using respectively self organizing mapping and partial order technique in a comparative analysis that highlights critical aspects of uncertainty, due to the ambiguity between single risk indicator rankings. [Copyright &y& Elsevier]