The assessment of the effectiveness of the unimodal and bimodal models to evaluate the water flow through nature-based solutions substrates.

Nature-based solutions are popular techniques for managing stormwater. Most of them allow porous media as their main layer. The description of the Soil Water Retention Curve (SWRC) as the Unsaturated Hydraulic Conductivity Curve (UHCC) is often required to run the hydrological simulations with the physically based models. Using the unimodal and bimodal models to assess the SWRC and UHCC of soils is a widespread technique but their evaluation is often present in literature only in terms of curve fitting. Based on these assumptions, this work presents the performance assessment of the van Genuchten unimodal and bimodal models by functional evaluation of them based on the runoff from several substrates. Four substrates were investigated to define the structure, the SWRC, and the UHCC. Results showed that all substrates had a bimodal behaviour with lowest values of RMSE (RMSE_Θ = 0.0023 to 0.0037, RMSE_K = 0.0636 to 0.1284). Finally, a numerical simulation using the HYDRUS-1D model was performed for a three-month data set to check the effectiveness of the unimodal model instead of the bimodal one. The findings have shown that the unimodal model must be preferred instead of the bimodal because it has fewer parameters and assured low discrepancies in runoff volume (ε=0.00% to 6.25%).

Experimental analysis to assess the hydrological efficiency and the nutrient leaching behavior of a new green wall system.

Green Walls represent a sustainable solution to mitigate the effects due to climate change and urbanization. However, although they have been widely investigated in different fields of science, studies on the potential of these systems to manage urban stormwater are still few. Moreover, even if these systems provide multiple benefits, as other nature-based solutions, they leach nutrients due to growing media, decomposed vegetation, and the possibility of fertilizer use. In this regard, several studies have evaluated the nutrient concentrations in the runoff from green roofs, while studies that have analyzed the nutrient-leaching behavior of green walls are still limited. To bridge these scientific gaps, this study presents experimental findings on the hydrological efficiency and nutrient-leaching behavior of an innovative modular living wall system. Some rainfall-runoff tests were carried out to assess the hydrological response of a new green wall system in retaining stormwater. To evaluate the concentration of the nutrients, the collected outflow was analyzed by spectrophotometer UV-visible. The findings show that the developed green wall panel presents good retention capacity by considering different simulated rainfalls and varying the initial soil moisture conditions. The results in terms of nutrient concentrations highlight that the vegetation life cycle and the fertilizer uses affect the quality of the water released from the green wall panel. The concentration of the analyzed nutrients is influenced by the simulated rainfall's hydrological characteristics and the days between the planting phase and the test. However, the overall results show that the concentrations of each analyzed nutrient are low, except after the fertilizer use, highlighting that the choice of vegetation that does not need external nutrients should be preferred during the design of a green wall.

Smart Technologies for Water Resource Management: An Overview.

The latest progress in information and communication technology (ICT) and the Internet of Things (IoT) have opened up new opportunities for real-time monitoring and controlling of cities' structures, infrastructures, and services. In this context, smart water management technology provides the data and tools to help users more effectively manage water usage. Data collected with smart water devices are being integrated with building management systems to show how much water is used by occupants as well as to identify the consumption areas to use water more efficiently. By this approach, smart buildings represent an innovative solution that enhances a city's sustainability and contributes to overcoming environmental challenges due to increasing population and climate change. One of the main challenges is resource-saving and recovery. Water is an all-important need of all living beings, and the concerns of its scarcity impose a transition to innovative and sustainable management starting from the building scale. Thus, this manuscript aims to provide an updated and valuable overview for researchers, consumers, and stakeholders regarding implementing smart and sustainable technologies for water resource management, primarily for building-scale uses.

Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications.

Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters-such as daily average temperature, relative humidity, wind speed-and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

Development of an Assessment Method for Investigating the Impact of Climate and Urban Parameters in Confirmed Cases of COVID-19: A New Challenge in Sustainable Development.

Sustainable development has been a controversial global topic, and as a complex concept in recent years, it plays a key role in creating a favorable future for societies. Meanwhile, there are several problems in the process of implementing this approach, like epidemic diseases. Hence, in this study, the impact of climate and urban factors on confirmed cases of COVID-19 (a new type of coronavirus) with the trend and multivariate linear regression (MLR) has been investigated to propose a more accurate prediction model. For this propose, some important climate parameters, including daily average temperature, relative humidity, and wind speed, in addition to urban parameters such as population density, were considered, and their impacts on confirmed cases of COVID-19 were analyzed. The analysis was performed for three case studies in Italy, and the application of the proposed method has been investigated. The impacts of parameters have been considered with a delay time from one to nine days to find out the most suitable combination. The result of the analysis demonstrates the effectiveness of the proposed model and the impact of climate parameters on the trend of confirmed cases. The research hypothesis approved by the MLR model and the present assessment method could be applied by considering several variables that exhibit the exact delay of them to new confirmed cases of COVID-19.