In urban water management, green roofs provide a sustainable solution for flood risk mitigation. Numerous studies have investigated green roof hydrologic effectiveness and the parameters that influence their operation; many have been conducted on the pilot scale, whereas only some of these have been executed on full-scale rooftop installations. Several models have been developed, but only a few have investigated the influence of green roof physical parameters on performance. From this broader context, this paper presents the results of a monitoring analysis of an extensive green roof located at the University of Calabria, Italy, in the Mediterranean climate region. To obtain this goal, the subsurface runoff coefficient, peak flow reduction, peak flow lag-time, and time to the start of runoff were evaluated at an event scale by considering a set of data collected between October 2015 and September 2016 consisting of 62 storm events. The mean value of subsurface runoff was 32.0% when considering the whole dataset, and 50.4% for 35 rainfall events (principally major than 8.0 mm); these results indicate the good hydraulic performance of this specific green roof in a Mediterranean climate, which is in agreement with other studies. A modeling approach was used to evaluate the influence of the substrate depth on green roof retention. The soil hydraulics features were first measured using a simplified evaporation method, and then modeled using HYDRUS-1D software (PC-Progress s.r.o., Prague, Czech Republic) by considering different values of soil depth (6 cm, 9 cm, 12 cm, and 15 cm) for six months under Mediterranean climate conditions. The results showed how the specific soil substrate was able to achieve a runoff volume reduction ranging from 22% to 24% by increasing the soil depth.

a b s t r a c tMechanistic models have proven to be accurate tools for the numerical analysis of the hydraulic behavior of Low Impact Development (LIDs) techniques. However, their widespread adoption has been limited by their computational cost. In this view, surrogate modeling is focused on developing and using a computationally inexpensive surrogate of the original model. While having been previously applied to various water-related and environmental modeling problems, no studies have used surrogate models for the analysis of LIDs. The aim of this research thus was to investigate the benefit of surrogate-based modeling in the numerical analysis of LIDs. The kriging technique was used to approximate the deterministic response of the widely used mechanistic model HYDRUS-2D, which was employed to simulate the variably-saturated hydraulic behavior of a contained stormwater filter. The Nash-Sutcliffe efficiency (NSE) index was used to compare the simulated and measured outflows and as the variable of interest for the construction of the response surface. The validated kriging model was first used to carry out a Global Sensitivity Analysis of the unknown soil hydraulic parameters of the filter layer, revealing that only the shape parameter a and the saturated hydraulic conductivity K s significantly affected the model response. Next, the Particle Swarm Optimization algorithm was used to estimate their values. The NSE value of 0.85 indicated a good accuracy of estimated parameters. Finally, the calibrated model was validated against an independent set of measured outflows with a NSE value of 0.8, which again corroborated the reliability of the surrogate-based optimized parameters.

The simulation of the ventilation and the heating, ventilation, and air conditioning (HVAC) systems of vehicles could be used in the energy demand management of vehicles besides improving the air quality inside their cabins. Moreover, traveling by public transport during a pandemic is a concerning factor, and analysis of the vehicle’s cabin environments could demonstrate how to decrease the risk and create a safer journey for passengers. Therefore, this article presents airflow analysis, air changes per hour (ACH), and respiration aerosols’ trajectory inside three vehicles, including a typical car, bus, and airplane. In this regard, three vehicles’ cabin environment boundary conditions and the HVAC systems of the selected vehicles were determined, and three-dimensional numerical simulations were performed using computational fluid dynamic (CFD) modeling. The analysis of the airflow patterns and aerosol trajectories in the selected vehicles demonstrate the critical impact of inflow, outflow, and passenger’s locations in the cabins. The CFD model results exhibited that the lowest risk could be in the airplane and the highest in the bus because of the location of airflows and outflows. The discrete CFD model analysis determined the ACH for a typical car of about 4.3, a typical bus of about 7.5, and in a typical airplane of about 8.5, which were all less than the standard protocol of infection prevention, 12 ACH. According to the results, opening windows in the cars could decrease the aerosol loads and improve the low ACH by the HVAC systems. However, for the buses, a new design for the outflow location or an increase in the number of outflows appeared necessary. In the case of airplanes, the airflow paths were suitable, and by increasing the airflow speed, the required ACH might be achieved. Finally, in the closed (recirculating) systems, the role of filters in decreasing the risk appeared critical.

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The role of the industrial sector in total greenhouse gas (GHG) emissions and resource consumption is well-known, and many industrial activities may have a negative environmental impact. The solution to decreasing the negative effects cannot be effective without the consideration of sustainable development. There are several methods for sustainability evaluation, such as tools based on products, processes, or plants besides supply chain or life cycle analysis, and there are different rating systems suggesting 80, 140, or more indicators for assessment. The critical point is the limits such as required techniques and budget in using all indicators for all factories in the beginning. Moreover, the weight of each indicator might change based on the selected alternative that it is not a fixed value and could change in a new case study. In this regard, to determine the impact and weight of different indicators in sustainable factories, a multi-layer Triangular Fuzzy Analytic Hierarchy Process (TFAHP) approach was developed, and the application of the method was described and verified. The defined layers are six; for each layer, the pairwise comparison matrix was developed, and the total aggregated score concerning the sustainability goal for each alternative was calculated that shows the Relative Importance Coefficient (RIC). The method is formulated in a way that allows adding the new indicators in all layers as the verification shows, and thus, there are no limits for using any green rating systems. Therefore, the presented approach by TFAHP would provide an additional tool toward the sustainable development of factories.