Economic assessment tool for greywater recycling systems

The implementation of water demand management strategies, particularly in urban environments, can contribute towards improved sustainability (or at least reduce unsustainability) in the water sector. Greywater treatment, and its subsequent use for toilet flushing, is one of the demand management options offering considerable water-saving potential. The uptake of greywater recycling systems (GRSs), particularly in the UK, is low. One of the reasons for such a low uptake is the perception that GRSs have a high (unsustainable) cost/benefit ratio. This paper presents progress on the development of a whole-life cost (WLC) model, aimed at facilitating decision making for the implementation of GRSs in relation to their economic viability

Enhancement of urban pluvial flood risk management and resilience through collaborative modelling: a UK case study

This paper presents the main findings and lessons learned from the development and implementation of a new methodology for collaborative modelling, social learning and social acceptance of flood risk management technologies. The proposed methodology entails three main phases: (1) stakeholder analysis and engagement; (2) improvement of urban pluvial flood modelling and forecasting tools; and (3) development and implementation of web-based tools for collaborative modelling in flood risk management and knowledge sharing. The developed methodology and tools were tested in the Cranbrook catchment (London Borough of Redbridge, UK), an area that has experienced severe pluvial (surface) flooding in the past. The developed methodologies proved to be useful for promoting interaction between stakeholders, developing collaborative modelling and achieving social acceptance of new technologies for flood risk management. Some limitations for stakeholder engagement were identified and are discussed in the present paper

Enabling the uptake of circular water solutions

This study advances the discourse on the transition from a linear to a circular water paradigm, within which water is reused and resources such as nutrients and energy can be recovered. The research provides an empirical evidence from demonstrative cases, identifying the technological, economic, socio-cultural, and regulatory factors that facilitate or impede the broader adoption of circular solutions in the water sector. It proposes an integrated system approach, which encompasses a comprehensive set of enabling instruments, including (a) the demonstration of the sustainability of circular water technologies at a system level, thereby providing a robust proof of concept; (b) a shift from a conventional financial cost-benefit approach to a business model predicated on circular value chains, underscoring the economic feasibility of these solutions; (c) the enhancement of social acceptance through active stakeholder engagement, thereby fostering a supportive community for these transformative changes; and (d) the adaptation of the regulatory framework to incentivise circular water solutions, such as the establishment of dedicated end-of-waste criteria to facilitate market access for recovered resources. The study concludes that a concerted effort is required to reconceptualise our water systems as circular systems, and to legitimise the role of circular water within our society and economy

Diffusion tensor driven image registration: a deep learning approach

Tracking microsctructural changes in the developing brain relies on accurate inter-subject image registration. However, most methods rely on either structural or diffusion data to learn the spatial correspondences between two or more images, without taking into account the complementary information provided by using both. Here we propose a deep learning registration framework which combines the structural information provided by T2-weighted (T2w) images with the rich microstructural information offered by diffusion tensor imaging (DTI) scans. We perform a leave-one-out cross-validation study where we compare the performance of our multi-modality registration model with a baseline model trained on structural data only, in terms of Dice scores and differences in fractional anisotropy (FA) maps. Our results show that in terms of average Dice scores our model performs better in subcortical regions when compared to using structural data only. Moreover, average sum-of-squared differences between warped and fixed FA maps show that our proposed model performs better at aligning the diffusion data