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Siloxane-based compounds are widely used in personal care, pharmaceutical and household products as well as in industrial applications. Among the wide variety of these chemicals, special attention has been given to volatile methylsiloxanes (VMSs). These compounds have been extensively detected in several environmental compartments, as they are not effectively removed from wastewater and may migrate through different matrices and being lipophilic, bioaccumulate and biomagnify in living organisms. In this work, a prioritisation methodology for several VMSs in different environmental matrices was applied, estimating a hazard quotient by combining exposure evaluation through measured or predicted environmental concentrations (MEC or PEC) and effects using ecotoxicity data to establish no effect concentrations (PNEC). VMSs show quite different hazard potentials in the environment: for linear VMSs it is not considerable, while for cyclic VMSs the hazard is disperse. D4 and D5 may have adverse effects in water, as well as D5 and D6 in sediments. This first multi-matrix approach for the prioritisation of VMSs sets the ground for more accurate studies in the future, provided that more field-based data are reported. [ABSTRACT FROM AUTHOR]

Poor connection between data on emerging issues and credible policy decisions continues to challenge governments, and is only likely to grow as demands on time and resources increase. Here we summarise recent efforts to integrate horizon scanning and risk prioritisation approaches to better connect emerging issues to the political discourse on environmental and food-related issues. Our categorisation of insights including potential future risks and opportunities to inform policy discussions has emerged from a structured three-year programme of horizon scanning for a UK pan-governmental futures partnership led by the Department for Environment, Food and Rural Affairs (Defra). Our efforts to integrate horizon scanning and risk prioritisation, utilising a qualitative weight of evidence framework, has created a systematic process for identifying all signals of potential future change with significant impact for the strategic mission and underlying values of policy actors. Our approach encourages an exploration of factors out of the control of organisations, recognising that resilience depends on the flexibility of management strategies and the preparedness to deal with a variety of unexpected outcomes. We discuss how this approach addresses key cultural and evaluative challenges that policy actors have had in embedding horizon scanning in evidence-based policy processes, and suggest further developments to build confidence in the use of horizon scanning for strategic planning. [ABSTRACT FROM AUTHOR]

Abstract: The presence of pharmaceuticals in the aquatic environment, and the concerns for negative effects on aquatic organisms, has gained increasing attention over the last years. As ecotoxicity data are lacking for most active pharmaceutical ingredients (APIs), it is important to identify strategies to prioritise APIs for ecotoxicity testing and environmental monitoring. We have used nine previously proposed prioritisation schemes, both risk- and hazard-based, to rank 582 APIs. The similarities and differences in overall ranking results and input data were compared. Moreover, we analysed how well the methods ranked seven relatively well-studied APIs. It is concluded that the hazard-based methods were more successful in correctly ranking the well-studied APIs, but the fish plasma model, which includes human pharmacological data, also showed a high success rate. The results of the analyses show that the input data availability vary significantly; some data, such as logP, are available for most API while information about environmental concentrations and bioconcentration are still scarce. The results also suggest that the exposure estimates in risk-based methods need to be improved and that the inclusion of effect measures at first-tier prioritisation might underestimate risks. It is proposed that in order to develop an adequate prioritisation scheme, improved data on exposure such as degradation and sewage treatment removal and bioconcentration ability should be further considered. The use of ATC codes may also be useful for the development of a prioritisation scheme that includes the mode of action of pharmaceuticals and, to some extent, mixture effects. [Copyright &y& Elsevier]

Barriers associated to human infrastructure are a widespread impact in freshwater ecosystems worldwide, disrupting connectivity along river networks and key processes. Restoration of connectivity has risen in the last decade, with thousands of dams, weirs and culverts removed. Spatial optimisation methods can help inform decision on what barriers to remove to maximise gain in connectivity under limited budgets. However, current optimisation approaches rely on programming skills that are not easily accessible to stakeholders, which restrict the use of these methods. We demonstrate how Marxan, a publicly available tool, can be used to prioritise the allocation of barrier removal projects. We mapped the distribution of >900 barriers in the Tagus River (Iberian Peninsula) and 29 freshwater fish species with different movement abilities and needs. We assessed the passability of each barrier by all species and relative removal cost. We then identified priority barriers for removal to increase connectivity of populations of all species simultaneously. We tested two alternative scenarios: i) locking out barriers assesses as non-removable for their high strategic value or removal cost and ii) making all barriers available for removal. We found that connectivity recovery targets could be achieved by removing a small proportion of barriers, and avoiding large infrastructure. However, for some species, large recovery targets could only be achieved by removing some of these large infrastructures at high increases in cost. We also found some spatial differences in the recovery value of particular barriers for improving upstream and downstream connectivity. Our study demonstrates how to use a robust optimisation approach in an accessible tool, to address the complexity of prioritisation exercises commonly faced by stakeholders when deciding where to invest in barrier removal projects. This will improve decision-making for river connectivity restoration through a transparent, reproducible, and better-informed approach than traditional opportunistic or ranking-based approaches. Unlabelled Image • Barrier removal is gaining attention in river restoration projects. • Complexity of decision-making on barrier removal projects needs optimisation methods. • Public data and tools can be used to plan for restoration of river connectivity. • River connectivity can be greatly enhanced if adequately planned at catchment scale. [ABSTRACT FROM AUTHOR]

Industrial wastewater is characterised by a complex composition of trace organic compounds (TrOC) in a difficult matrix. The identification of unknown pollutants is of high interest. On the one hand to ensure protection of the environment by elucidating contaminations and on the other hand to protect the biological treatment step in the wastewater treatment plant (WWTP). Due to the high variability of the matrix, the identification of compounds of interest is very time consuming and often unsuccessful. To overcome this limitation, a prioritisation method was developed to identify so called ' known unknowns ', i.e. compounds frequently detected but not identified, as prioritised compounds in industrial wastewater. The method based on an offline two-dimensional (offline 2D) liquid chromatography (LC) approach with ultra violet (UV) detection in the first and high-resolution mass spectrometry (HRMS) in the second dimension. As a proof of concept, an identification process of one ' known unknown ' is described. The compound was identified as a dichlorodinitrophenol isomer by retention time in two dimensions, UV spectrum, exact mass, mass fragmentation and 1H- NMR. As prioritisation method, the offline 2D LC in combination with non-target analysis provides a powerful workflow to determine tentative structures of unknown organic compounds in industrial wastewater. Unlabelled Image • Frequently observed contaminants named ' known unknowns ' serve as prioritised compounds. • 2D offline LC-UV/LC-HRMS was used for identification. • 1st dimension provides clean-up of interfering signals and UV absorption. • 2nd dimension provides HRMS for elucidation of unknown wastewater compounds. [ABSTRACT FROM AUTHOR]