Sweco Inc.

Extensive green roofs can mitigate the adverse effects of urban nature loss by providing ecosystem services, such as biodiversity increase, stormwater management and temperature regulation. They also sequester carbon, retain nutrients and potentially improve urban air and rainwater quality. This study examined how roof age, substrate depth, fertilization and vegetation type affect carbon, nitrogen and phosphorus substrate stocks, as well as two nitrogen fluxes (mineralization and nitrification). We hypothesized that vegetation type (Sedum-only vs. diverse vegetation), substrate depth and fertilization would be the main drivers of these stocks and fluxes. Twelve extensive green roofs in three cities in Flanders, Belgium, were sampled across four seasons. Results suggest limited carbon sequestration potential. Roofs with diverse vegetation, particularly those with mosses and herbs, had higher total carbon and nitrogen substrate stocks. Carbon stocks ranged from 1400 to 2880 g m− 2 (mean: 1600 g m− 2) in diverse roofs, compared to 700–1050 g m− 2 (mean: 860 g m− 2; p < 0.001) in species-poor roofs. Nitrogen stocks ranged from 62 to 100 g m− 2 (mean: 83 g m− 2) in diverse roofs and between 38 and 60 g m− 2 (mean: 50 g m− 2; p < 0.001) in species-poor roofs. Fertilization had no significant effect, while substrate depth only influenced phosphorus substrate stocks (p = 0.012). Overall, extensive green roofs offer limited carbon sequestration and nutrient retention. However, optimizing substrate composition and increasing plant diversity could enhance these benefits. This study highlights the potential for improving green roof performance through better design and management.

Transitioning to a resource-efficient and sustainable circular economy is vital for tackling climate- and environmental-related challenges. This study demonstrates a closed-loop strategy for upcycling agricultural biowaste eggshells. Water-insoluble proteins were extracted from both shell membranes and shell fragments by boiling in water using protein denaturants. The ground eggshells also were used to prepare calcium acetate (Ca(CH 3 COO) 2 ) and to treat Scots pine ( Pinus sylvestris L.) sapwood. Mineralisation of the wood was achieved by performing a two-step impregnation process using aqueous solutions of ammonium dihydrogen phosphate (NH 4 H 2 PO 4 ) and Ca(CH 3 COO) 2 salts. Morphological studies revealed the relatively low saturation of wood matrix with mineral, with cell lumina mostly unfilled, while elemental mapping confirmed homogeneous distribution of Ca and P within the wood matrix. Powder X-ray diffraction (XRD) analysis revealed that wood treatment resulted in the in-situ co-precipitation of low-crystallinity hydroxyapatite (Ca 10 (PO 4 ) 6 (OH) 2 ), and spectroscopic analysis indicated carbonate substitution within the Ca 10 (PO 4 ) 6 (OH) 2 crystal lattice, suggesting the formation of carbonated hydroxyapatite (Ca 10-x (PO 4 ) 6-x (CO 3 ) x (OH) 2-x-2y (CO 3 ) y ). Microscale combustion calorimeter (MCC) and cone calorimeter (CC) measurements of mineralised wood revealed a reduction in the total heat release (THR) compared with untreated wood, indicating potential for further optimisation of wood modification process. Results suggest that the proposed aqueous solution-based processing approach for converting an abundant resource, chicken eggshells, into value-added products has potential for new technology and bioeconomy development and represents a promising pathway towards improved sustainability.

Road authorities are systematically expanding 30 km/h zones to enhance safety. This requires understanding how built environment characteristics are associated with driving speeds, but only a few studies, typically based on small samples, focus on 30 km/h streets. Using a spatial error model, this study examines the relationship between built environment factors and 85th percentile speeds on 47,000 km of Dutch 30 km/h streets (N=159.000). Driving speed and traffic volume data were estimated using floating car data, while built environment characteristics were collected from public sources. The results show that higher driving speeds are associated with greater traffic volumes, longer street lengths, closed pavement, separated bicycle tracks, visually marked bicycle lanes, and longer road sections. Features linked to lower driving speeds include curves, speed humps, raised intersections, exit constructions at zone entrances, narrower carriageways, roadside parking, nearby premises, and higher address densities. Furthermore, the identified interaction effects show that measures like speed humps and raised intersections have greater impacts in high-speed environments (i.e. long and busy streets with closed pavement) but limited effects in low-speed settings. These findings emphasize the need to consider combinations of road design elements and their context-dependent effects to understand driving speed on 30 km/h streets. Out study provides valuable insights into the effectiveness of speed-reduction measures, offering guidance for interventions targeting streets with excessive speeds.

Recent technologies for recording and storing data, as well as advancements in data processing techniques, have opened up novel possibilities for urban planners to design a more optimal public transport network. This study aims to initially develop a robust framework for making an insightful understanding of already recorded and available data sets using machine learning approaches. This will give transportation planners a powerful framework to use great recorded datasets to understand the network better and make datasets more meaningful for transport planners. And then introduces an approach to use Machine Learning algorithms and extract hidden patterns for predicting financial loss during any crisis, which is a novel perspective and application. To do this, seven alternative machine learning algorithms were developed to predict ridership: Multiple Linear Regression, Decision Tree, Random Forest, Bayesian Ridge Regression, Neural Networks, Support Vector Regression, and k-Nearest Neighbors. The developed framework was applied to the available 10 years of historical recorded data from the blue bus line number 4 in Stockholm, Sweden. The best model, kNN, with an average R-squared of 0.65 in 10-fold cross-validation, was accepted as the best model. This model is then used to estimate the financial loss of the network during the pandemic in 2020 and 2021. Results reveal a decline of 49% in 2020 and 82% in 2021 in the studied line. Finally, the results were validated with a similar study that analyzed the ticket validations and passenger counts during the spring of 2020.

Preventing the ignition of combustibles in an underground mine will be one of the decisive actions affecting the risk to mining personnel during a fire. This study focuses on the application of safety distances in underground hard rock mines, where a safety distance will ensure that the incipient heat flux upon the fuel surface will not be sufficient for ignition. No earlier study has been conducted on safety distances in underground mines, and where data from earlier fire experiments and studies were applied. Safety distances were calculated using empirical expressions, accounting for influencing parameters. It was found that safety distances varied very little with the longitudinal flow velocity. The mine drift height was found to have a larger impact due to occurring flame deflection. The safety distances for a site with flammable/combustible liquid or electrical cables were found to be higher for higher drift heights, caused by the other fuel items having higher critical heat fluxes, which are attained closer to the fire, where the flames are tilted closer to the fuel surface for a lower drift height. The tilting effect will decrease with increasing distance, and eventually, the heat flux values will be higher for a higher drift height.