Circular construction is an emerging paradigm aimed at minimizing waste and reducing environmental impacts associated with the construction industry. To support this transition, building circularity indicators have been developed to assess the circularity of construction. While these indicators provide valuable insights into circularity, they lack the capacity to evaluate the environmental impacts of circular interventions. Addressing this gap, this paper presents a novel integration of the Whole Building Circularity Indicator (WBCI) with a comprehensive Cradle-to-Cradle Life Cycle Assessment (C2C-LCA), creating a unified WBCI-LCA framework for assessing circularity and Environmental Sustainability (ES) in circular construction. Applying the framework to a typical Dutch residential building and developing four circular scenarios, this study demonstrates how WBCI and C2C-LCA approaches can be effectively combined to examine the complex trade-offs between circularity and ES. The findings reveal that while increased circularity (from 0.309 to 0.488 in developed scenarios) reduces environmental impacts in most categories, it may also shift burdens across categories. For instance, biobased materials like CLT reduce global warming potential but increase eutrophication potential depending on the end-of-life scenario. This integrated approach demonstrates that WBCI and C2C-LCA effectively complement each other: WBCI captures essential circularity aspects like material circulation and component disassembly, while C2C-LCA provides insights into the environmental impacts of manufacturing, recycling, and transportation processes. These findings advance the literature on building circularity indicators by extending their applicability to C2C-LCA and contribute to the broader discourse on sustainability and circularity by providing empirical evidence on their interrelationship.@en

The internal fouling of membranes is typically presumed and inferred, but the direct characterization of representative samples is challenging. This study targeted to assess internal fouling using the Optical Coherence Tomography (OCT) and Evapoporometry (EP) techniques for real-time monitoring during filtration and off-line measurement of the pore size distributions (PSDs) of the fouled membranes, respectively. The results were validated by atomic force microscopy (AFM) measurements and also the three-mechanism fouling model. The foulant used was the well-studied bovine serum albumin (BSA), while the membranes were three commercially available polymeric microfiltration membranes with similar nominal pore sizes and porosities. Although the OCT affirmed the progressive worsening of internal fouling in real-time, the quantitative comparison of the extents of internal fouling among the three membranes was not possible. As for EP, it was able to quantitatively compare the pore size distributions and average pore diameters to ascertain the different extents of internal fouling. The phenomenological model available was effective in quantitatively comparing the extents of pore-constriction among the three membranes, while AFM tied the worst internal fouling to the most attractive BSA-membrane affinity. More in-depth understanding of internal fouling is warranted to not only recommend better membranes but also facilitate the advancement of models.

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Industrial land is under transition globally. Insights into this transition are important to plan a sustainable future. Since industrial land follows parcel shapes and the transition process requires multi-year data to observe the impacts of such changes, multi-year vector data should be used to analyse industrial land transition. This paper presents a framework to analyse path-dependent regional industrial land transition processes using vector data. A step by step instruction is presented. In the analysis, the changed percentages of land use in the surroundings of appeared or disappeared industrial land are visualized. The visualized surrounding land use compositions give planners an idea on what causes land use transitions, the most frequent transition forms and their impacts on the surroundings, purely from a land use point of view to reduce data collection efforts. The North Brabant region in the Netherlands is used as a case study. The region is split into urban and non-urban areas to show the generic applicability of this framework.

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Abandoned industrial sites could be redeveloped in a sustainable way with the help of previous experience. This paper presents a case-based reasoning (CBR) approach to support sustainable industrial site redevelopment. For a target site that needs to be redeveloped, qualitative important key concerns are identified and quantitative attributes, which are important for sustainability, are calculated. The key concerns are generated from zoning documents and the attributes are calculated from spatial data sets. Machine learning techniques are used to find the most influential attributes to determine transition forms. Similar cases from the constructed case base are retrieved based on the algorithm the authors have proposed. The North Brabant region in the Netherlands is used as a case study. A web application is presented to illustrate the approach. The e-planning method provides a straightforward way to retrieve transition forms from similarly redeveloped cases for new regional planning tasks with a focus on sustainability.@en