Extensive green roofs: different time approaches to runoff coefficient determination

Stormwater runoff in green roofs (GRs) is represented by the runoff coefficient, which is fundamental to assess their hydraulic performance and to design the drainage systems downstream. Runoff coefficient values in newly installed GR systems should be estimated by models that must be feasible and reproduce the retention behavior as realistically as possible, being thus adjusted to each season and climate region. In this study, the suitability of a previously developed model for runoff coefficient determination is assessed using experimental data, and registered over a 1 year period. Results showed that the previously developed model does not quite fit the experimental data obtained in the present study, which was developed in a distinct year with different climate conditions, revealing the need to develop a new model with a better adjustment, and taking into consideration other variables besides temperature and precipitation (e.g., early-stage moisture conditions of the GR matrix and climate of the study area). Runoff coefficient values were also determined with different time periods (monthly, weekly, and per rain event) to assess the most adequate approach, considering the practical uses of this coefficient. The monthly determination approach resulted in lower runoff coefficient values (0–0.46) than the weekly or per rain event (0.017–0.764) determination. When applied to a long-term performance analysis, this study showed no significant differences when using the monthly, weekly, or per rain event runoff, resulting on a variation of only 0.9 m3 of annual runoff. This indicates that the use of monthly values for runoff coefficient, although not suitable for sizing drainage systems, might be used to estimate their long-term performance. Overall, this pilot extensive GR of 0.4 m2 presented an annual retention volume of 469.3 L, corresponding to a retention rate of 89.6%, in a year with a total precipitation of 1089 mm. The assessment of different time scales for runoff coefficient determination is a major contribution for future GR performance assessments, and a fundamental decision support tool.info:eu-repo/semantics/publishedVersio

Green roofs as an urban NBS strategy for rainwater retention: influencing factors - a review

There has been a rapid development in studies of nature-based solutions (NbS) worldwide, which reveals the potential of this type of solution and the high level of interest in its implementation to improve the resilience of cities. Much relevant information and many important results are being published, and it is now possible to see their diverse benefits and complexity. Several authors highlight their role in urban areas not just in temperature control, but also in human health, ecosystem development and water management. However, in the current reality of cities, where water use is being (and will be) constantly challenged, analyzing NbS advantages for the urban water cycle is crucial. This study performed an intense review of the NbS literature from 2000 to 2021, to identify their contributions to the improvement of urban water cycle management and thus provide a solid information base for distinct entities (public institutions, private investors and the urban population in general) to disseminate, apply and justify their implementation. In general terms, the urban water cycle embraces not only the abstraction of water for urban consumption, but also its return to nature and all the stages in between, including water reuse and stormwater management. This review will highlight the important benefits that NbS in general, and green roofs in particular, provide to urban stormwater control, a key factor that contributes to urban sustainability and resilience in order to face future climate challenges. The novelty of the present review paper falls within the conclusions regarding the crucial role that NbS develop in urban water management and the main features that must be tested and technically enhanced to improve their functioning.info:eu-repo/semantics/publishedVersio

Nature-based solutions contribution for urban resilience and sustainability

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Green roofs as a biotechnological solution to increase water retention in urban areas

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Editorial: urban nature-based solutions and green infrastructure as strategies for climate change adaptation

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Towards precise recognition of pollen bearing bees by convolutional neural networks

Automatic recognition of pollen bearing bees can provide important information both for pollination monitoring and for assessing the health and strength of bee colonies, with the consequent impact on people's lives, due to the role of bees in the pollination of many plant species. In this paper, we analyse some of the Convolutional Neural Networks (CNN) methods for detection of pollen bearing bees in images obtained at hive entrance. In order to show the in uence of colour we preprocessed the dataset images. Studying the results of nine state-of-the-art CNNs, we provide a baseline for pollen bearing bees recognition based in deep learning. For some CNNs the best results were achieved with the original images. However, our experiments showed evidence that DarkNet53 and VGG16 have superior performance against the other CNNs tested, with unsharp masking preprocessed images, achieving accuracy results of 99:1% and 98:6%, respectively.info:eu-repo/semantics/publishedVersio

Green Roofs Influence on Stormwater Quantity and Quality: A Review

This chapter intends to make an extensive review of the influence that Green Roofs (GR) have on the quality and quantity of stormwater. These aspects are very important to define the benefits and the disadvantages of this nature-based solution that is being implemented worldwide to improve the sustainability of urban areas. Previous studies show that the characteristics of GR (such as dimensions, the composition of the different layers and the type of plants) have a major influence on the quality and quantity of the GR runoff. Despite the proven benefits in urban stormwater management, in some reported cases, the quality resulted worst and for some GR conditions, the effect on rainwater retention was minimal. They are key elements to make resilient cities so a clear understanding of their functioning and development is fundamental to avoid and minimize potential impacts of malfunctioning of these nature-based structures

Cadmium removal by two strains of desmodesmus pleiomorphus cells

The capacity of microalgae to accumulate heavy metals has been widely investigated for its potential applications in wastewater (bio)treatment. In this study, the ability of Desmodesmus pleiomorphus (strain L), a wild strain isolated from a polluted environment, to remove Cd from aqueous solutions was studied, by exposing its biomass to several Cd concentrations. Removal from solution reached a maximum of 61.2 mg Cd g−1 biomass by 1 day, at the highest initial supernatant concentration used (i.e., 5.0 mg Cd L−1), with most metal being adsorbed onto the cell surface. Metal removal by D. pleiomorphus (strain ACOI 561), a commercially available ecotype, was also assessed for comparative purposes; a removal of 76.4 mg Cd g−1 biomass was attained by 1 day for the same initial metal concentration. Assays for metal removal using thermally inactivated cells were also performed; the maximum removal extent observed was 47.1 mg Cd g−1 biomass, at the initial concentration of 5 mg Cd L−1. In experiments conducted at various pH values, the highest removal was achieved at pH 4.0. Both microalga strains proved their feasibility as biotechnological tools to remove Cd from aqueous solution.info:eu-repo/semantics/acceptedVersio