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.

Hyperspectral Monitoring of Green Roof Vegetation Health State in Sub-Mediterranean Climate: Preliminary Results.

In urban and industrial environments, the constant increase of impermeable surfaces has produced drastic changes in the natural hydrological cycle. Decreasing green areas not only produce negative effects from a hydrological-hydraulic perspective, but also from an energy point of view, modifying the urban microclimate and generating, as shown in the literature, heat islands in our cities. In this context, green infrastructures may represent an environmental compensation action that can be used to re-equilibrate the hydrological and energy balance and reduce the impact of pollutant load on receiving water bodies. To ensure that a green infrastructure will work properly, vegetated areas have to be continuously monitored to verify their health state. This paper presents a ground spectroscopy monitoring survey of a green roof installed at the University of Calabria fulfilled via the acquisition and analysis of hyperspectral data. This study is part of a larger research project financed by European Structural funds aimed at understanding the influence of green roofs on rainwater management and energy consumption for air conditioning in the Mediterranean area. Reflectance values were acquired with a field-portable spectroradiometer that operates in the range of wavelengths 350-2500 nm. The survey was carried out during the time period November 2014-June 2015 and data were acquired weekly. Climatic, thermo-physical, hydrological and hydraulic quantities were acquired as well and related to spectral data. Broadband and narrowband spectral indices, related to chlorophyll content and to chlorophyll-carotenoid ratio, were computed. The two narrowband indices NDVI705 and SIPI turned out to be the most representative indices to detect the plant health status.

Comparison of mathematical methods for the evaluation of wastewater settleability by settling column tests.

Particulate matter (PM), which may serve as a vector of pollutants in both natural and human-impacted waters, is of primary interest in water quality studies, particularly for those conducted in urban areas. Separating solids from the liquid phase is one of the main goals of water treatment practices. Above all, the settling process is the most commonly used for this purpose. Generally, settling column tests are used in the laboratory to assess the total PM removal. Besides the traditional graphical method used for the interpretation of the laboratory results, other mathematical methods were developed - with the goal of simplifying the settling efficiency evaluation procedure and making it less subjective. To make a contribution in that direction, a study based on the comparison of four different mathematical methods for estimating PM removal efficiencies was conducted. The analysis was carried out on 15 samples of wastewater and showed that only two methods gave satisfactory results, and only one of them was physically based. Consequently, it is recommended to assess the overall PM removal, Etot, by a monoparametric exponential function, depending on the settling time (t) and on the first-order decay coefficient (k).

Settling Efficiency of Urban Particulate Matter Transported by Stormwater Runoff.

The main purpose of control measures in urban areas is to retain particulate matter washed out by stormwater over impermeable surfaces. In stormwater control measures, particulate matter removal typically occurs via sedimentation. Settling column tests were performed to examine the settling efficiency of such units using monodisperse and heterodisperse particulate matter (for which the particle size distributions were measured and modelled by the cumulative gamma distribution). To investigate the dependence of settling efficiency from the particulate matter, a variant of the evolutionary polynomial regression (EPR), a Microsoft Excel function based on multi-objective EPR technique (EPR-MOGA), called EPR MOGA XL, was used as a data-mining strategy. The results from this study have shown that settling efficiency is a function of the initial total suspended solids (TSS) concentration and of the median diameter (d50 index), obtained from the particle size distributions (PSDs) of the samples.

Delivery and frequency distributions of combined wastewater collection system wet and dry weather loads.

Combined wastewater collection systems continue to serve as a common urban conveyance method in urban areas of Europe and older urban areas of the United States. This study uses combined wastewater collection system monitoring data from the urbanizing Liguori catchment and channel in Cosenza (Italy) to illustrate event-based delivery and distribution of conveyed pollutant indices. Motivated by recent European Union (EU) discharge control legislation, this study specifically differentiates the event-based delivery of these indices between dry and wet-weather flows. Although the relatively steady to diurnal-variable delivery phenomena in dry weather flows are known, transport limiting phenomena for wet-weather hydrology and mass delivery typically are not known for the same catchment. Limiting categories of transport for a pollutant phase are generated by variables such as flow volume and duration, stream power, hydrograph parameters, and previous dry hours (PDH). Transport limitations of wet and dry weather events from the 414-ha catchment were analyzed and characterized as limited by mass indices (first-order, first flush transport) or limited by flow (zero-order transport). Results indicated significant concentration differences between mass- and flow-limited events. Higher concentrations were associated with mass-limited events. Frequency distributions of flow, total suspended solids (TSS), chemical oxygen demand (COD), and five-day biochemical oxygen demand (BOD5) were consistently exponential for wet-weather and mass-limited events. In contrast, flow, TSS, and BOD5 concentrations were distributed normally for flow-limited events. Results indicated a reasonable linear relationship between discharged TSS, COD, and BOD5 (biochemical oxygen demand) for Liguori Channel discharges into the Crati River. Wet-weather event transport was predominately mass-limited for TSS, COD, and BOD5.