Outdoor thermal comfort optimization through vegetation parameterization : species and tree layout

The optimization of outdoor thermal comfort has become the keystone to guarantee the healthy and comfortable use of outdoor spaces. This study aims to optimize the outdoor thermal comfort through vegetation parameterization in a boulevard located in Guelma city, Algeria during summertime. However, two main parameters were investigated, species and tree layout, through a numerical simulation. We first collected microclimate data of a sunny summer day. Second, we used real microclimate data in different simulations using the Envi-met atmospheric model. The findings reveal that Ficus Nitida is the most significant species to intercept solar radiation and provide shade over the day in Souidani Boudjemaa Boulevard, with a maximum reduction of Ta = 0.3 °C and UTCI = 2.6 °C at 13:00 p.m. Tree layout is a determining parameter in the creation of shaded paths, based on the quality of the shadows cast by the trees, namely, their size. Thereby, planting the washingtonia palm trees along the center of the boulevard is the best option to maximize the shaded area within the boulevard, with maximum reduction of Ta = 1.8 °C and UTCI = 3.5 °C at 16:00 p.m.https://www.mdpi.com/journal/sustainabilityMechanical and Aeronautical Engineerin

Selective Membrane Sensor for Aluminum Determination in Food Products, Real Samples and Standard Alloys

The study involves the fabrication of an aluminum liquid membrane sensor based on the association of aluminum ions with the cited reagent 2,9-dimethyl-4,11-diphenyl -1,5,8,12-tetraaza cyclote tradeca-1,4,8,11-tetraene [DDTCT]. The characteristics slope (58 mV), rapid and linear response for aluminum ion was displayed by the proposed sensor within the concentration range 2.5 × 10−7–1.5 × 10−1 M, the detection limit (1.6 × 10−7) M, the selectivity behavior toward some metal cations, the response time 10 s), lifetime (150 days), the effect of pH on the suggested electrode potential and the requisite analytical validations were examined. The suitable pH range was (5.0–8.0), in this range the proposed electrode response is independent of pH. The suggested electrode was applied to detect the aluminum ions concentration in food products, real samples and standard alloys. The resulting data by the suggested electrode were statistically analyzed, and compared with the previously reported aluminum ion-selective electrodes in the literature

Heavy Metal Removal from the Water of the River Nile Using Riverbank Filtration

Riverbank filtration (RBF) is considered as a natural treatment process. During this process, a group of chemical, physical, and biological processes occur when water moves through the soil along the bank of the River Nile, which can act as a conventional treatment process. RBF is one of the most effective solutions that the Egyptian government and responsible parties should embrace. Egypt has started to use the RBF technique widely in many sites through the path of the River Nile. This study provides a detailed analysis of the RBF technique; it represents the outlet quality of the water in a study performed on the River Nile. The effect of RBF on water quality can be measured using the software designed for this study. The study’s main aim is to improve the water quality of the River Nile by removing heavy metals from the water by using an effective and fast method of treatment, which is riverbank filtration. The results of the research’s experimental study show the average percentage of metal removal for iron, cobalt, lead, zinc, and copper are 74.04%, 74.44%, 70.72%, 75.1%, and 70.8%, respectively. These results have proved that RBF acts as a substantial barrier versus heavy metals

Combined Zircon/Apatite U-Pb and Fission-Track Dating by LA-ICP-MS and Its Geological Applications: An Example from the Egyptian Younger Granites

Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) is classically used in U-Pb dating to measure U and Pb isotopic concentrations. Recently, it has become frequently used in fission-track (FT) chronometry too. As an advantage, the U-Pb and FT double dating will enable efficiently determining the crystallization ages and the thermo-tectonic history concurrently as samples volume, analytical time, efforts, and cost will be greatly reduced. To demonstrate the validity of this approach, a Younger granite (Ediacaran age) sample from North Eastern Desert (NED), Egypt was analyzed for U-Pb and FT double dating. The integration of multiple geochronologic data yielded a zircon U-Pb crystallization age of 599 ± 30 Ma, after emplacement, the rock cooled /uplifted rapidly to depths of 9–14 km as response to the post-Pan African Orogeny erosional event as indicated by apatite U-Pb age of 474 ± 9 Ma. Afterwards, the area experienced a slow cooling/exhumation for a short period, most-likely as response to denudation effect. During the Devonian, the area was rapidly exhumed to reach depths of 1.5–3 km as response to the Hercynian tectonic event, as indicated by a zircon FT age of 347 ± 16 Ma. Then the studied sample has experienced a relatively long period of thermal stability between the Carboniferous and the Eocene. During the Oligocene-Miocene, the Gulf of Suez opening event affected the area by crustal uplift to its current elevation. This integration of Orogenic and thermo-tectonic information reveals the validity, efficiency, and importance of double dating of U-Pb and FT techniques using LA-ICP-MS methodology

Influence of Temperature in Degradation of Organic Pollution Using Corona Discharge Plasma

Dye solution temperature influences the elimination efficiency of water-soluble and anionic acid dye. Acid Blue 25 dye, using a gas–liquid electrical discharge system, was successfully investigated. The results showed an increase in the percentage of dye decolorization from 91.16% to 96.12% when the dye solution temperature was increased from 278 K to 308 K. However, the initial dye decolorization percentage was decreased with the further increase in dye solution temperature from 318 K to 358 K. The 2D simulation model was introduced to consider the influence of temperature and the electric field generated by corona discharge plasma in air and water. Results also showed a great match between the experimental and the simulation results. The reaction rates of dye degradation were analyzed using the Arrhenius equation. Furthermore, pseudo-zero-, pseudo-first-, and pseudo-second-order models were used to determine the reaction kinetics. The best fit for the experimental data would follow the pseudo-first-order model. Finally, electrical energy per order, energy yield, and experimental degradation data were calculated to investigate the cost analysis

Data Intelligence Model and Meta-Heuristic Algorithms-Based Pan Evaporation Modelling in Two Different Agro-Climatic Zones: A Case Study from Northern India

Precise quantification of evaporation has a vital role in effective crop modelling, irrigation scheduling, and agricultural water management. In recent years, the data-driven models using meta-heuristics algorithms have attracted the attention of researchers worldwide. In this investigation, we have examined the performance of models employing four meta-heuristic algorithms, namely, support vector machine (SVM), random tree (RT), reduced error pruning tree (REPTree), and random subspace (RSS) for simulating daily pan evaporation (EPd) at two different locations in north India representing semi-arid climate (New Delhi) and sub-humid climate (Ludhiana). The most suitable combinations of meteorological input variables as covariates to estimate EPd were ascertained through the subset regression technique followed by sensitivity analyses. The statistical indicators such as root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), Willmott index (WI), and correlation coefficient (r) followed by graphical interpretations, were utilized for model evaluation. The SVM algorithm successfully performed in reconstructing the EPd time series with acceptable statistical criteria (i.e., NSE = 0.937, 0.795; WI = 0.984, 0.943; r = 0.968, 0.902; MAE = 0.055, 0.993 mm/day; and RMSE = 0.092, 1.317 mm/day) compared with the other applied algorithms during the testing phase at the New Delhi and Ludhiana stations, respectively. This study also demonstrated and discussed the potential of meta-heuristic algorithms for producing reasonable estimates of daily evaporation using minimal meteorological input variables with applicability of the best candidate model vetted in two diverse agro-climatic settings

Exploiting IoT and Its Enabled Technologies for Irrigation Needs in Agriculture

The increase in population growth and demand is rapidly depleting natural resources. Irrigation plays a vital role in the productivity and growth of agriculture, consuming no less than 75% of fresh water utilization globally. Irrigation, being the largest consumer of water across the globe, needs refinements in its process, and because it is implemented by individuals (farmers), the use of water for irrigation is not effective. To enhance irrigation management, farmers need to keep track of information such as soil type, climatic conditions, available water resources, soil pH, soil nutrients, and soil moisture to make decisions that resolve or prevent agricultural complexity. Irrigation, a data-driven technology, requires the integration of emerging technologies and modern methodologies to provide solutions to the complex problems faced by agriculture. The paper is an overview of IoT-enabled modern technologies through which irrigation management can be elevated. This paper presents the evolution of irrigation and IoT, factors to be considered for effective irrigation, the need for effective irrigation optimization, and how dynamic irrigation optimization would help reduce water use. The paper also discusses the different IoT architecture and deployment models, sensors, and controllers used in the agriculture field, available cloud platforms for IoT, prominent tools or software used for irrigation scheduling and water need prediction, and machine learning and neural network models for irrigation. Convergence of the tools, technologies and approaches helps in the development of better irrigation management applications. Access to real-time data, such as weather, plant and soil data, must be enhanced for the development of effective irrigation management applications

Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces

The boiling heat transfer performance of porous surfaces greatly depends on the morphological parameters, liquid thermophysical properties, and pool boiling conditions. Hence, to develop a predictive model valid for diverse working fluids, it is necessary to incorporate the effects of the most influential parameters into the architecture of the model. In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. The optimized model is then employed to perform sensitivity analysis for finding the most influential parameters in the boiling heat transfer assessment of sintered coated porous surfaces on copper substrate subjected to a variety of high- and low-wetting working fluids, including water, dielectric fluids, and refrigerants, under saturated pool boiling conditions and different surface inclination angles of the heater surface. The model with all the surface morphological features, liquid thermophysical properties, and pool boiling testing parameters demonstrates the highest correlation coefficient, R2 = 0.985, for HTC prediction. The superheated wall is noted to have the maximum effect on the predictive accuracy of the boiling heat transfer coefficient. For example, if the wall superheat is dropped from the modeling parameters, the lowest prediction of R2 (0.893) is achieved. The surface morphological features show relatively less influence compared to the liquid thermophysical properties. The proposed methodology is effective in determining the highly influencing surface and liquid parameters for the boiling heat transfer assessment of porous surfaces