Heavy rainfall is one of major severe weather over Sinai Peninsula and causes many flash floods over the region. The good forecasting of rainfall is very much necessary for providing early warning before the flash flood events to avoid or minimize disasters. In the present study using the Weather Research and Forecasting (WRF) Model, heavy rainfall events that occurred over Sinai Peninsula and caused flash flood have been investigated. The flash flood that occurred on January 18, 2010, over different parts of Sinai Peninsula has been predicted and analyzed using the Advanced Weather Research and Forecast (WRF-ARW) Model. The predicted rainfall in four dimensions (space and time) has been calibrated with the measurements recorded at rain gauge stations. The results show that the WRF model was able to capture the heavy rainfall events over different regions of Sinai. It is also observed that WRF model was able to predict rainfall in a significant consistency with real measurements. In this study, several synoptic characteristics of the depressions that developed during the course of study have been investigated. Also, several dynamic characteristics during the evolution of the depressions were studied: relative vorticity, thermal advection, and geopotential height.

Pesticide exposure poses significant environmental and human health concerns, particularly given its extensive use in agricultural activities. The assessment of pesticide risks is a multifaceted and resource-intensive process, often requiring time-consuming toxicity studies. In response to this challenge, advanced computational models, remote sensing, and GIS (geographic information systems) have emerged as efficient and precise tools for evaluating pesticide exposure risks. This comprehensive review aims to provide an in-depth examination of the latest research methodologies for assessing the risks associated with pesticide exposure and their practical applications. These methodologies encompass the assessment of pesticide exposure in air, soil, and water, offering a comprehensive understanding of potential environmental pathways. The paper also delves into the effective utilization of these tools for pesticide risk assessment and examines the potential implications of their findings. The approaches outlined in this review hold promise for a thorough and insightful assessment of pesticide risks and are positioned to equip researchers and policymakers with valuable knowledge to mitigate the impacts of pesticide exposure on human health and the environment.

Global climate change is expected to impact future precipitation and surface temperature trends and could alter local hydrologic systems. This study assessed the likely hydrologic responses and changes in streamflow due to future climate change within the Alabama River Basin (ARB) for the mid-21st century 2045 (“2030–2060”) and end-21st century 2075 (“2060–2090”). Using an integrated modeling approach, General Circulation Model (GCM) datasets; the Centre National de Recherches Météorologiques Climate Model 5 (CNRM-CM5), the Community Earth System Model, version 1–Biogeochemistry (CESM1- BGC.1), and the Hadley Centre Global Environment Model version 2 (HADGEM2-AO.1), under medium Representative Concentration Pathway (RCP) 4.5, and based on World Climate Research Program (WCRP)’s Couple Model Intercomparison Phase 5 (CMIP5), were assimilated into calibrated Soil and Water Assessment Tool (SWAT). Mann–Kendall and Theil Sen’s slope were used to assess the trends and magnitude of variability of the historical climate data used for setting up the model. The model calibration showed goodness of fit with minimum Nash–Sutcliffe Efficiency (NSE) coefficient values of 0.83 and Coefficient of Determination (R²) of 0.88 for the three gages within the ARB. Next, the research assessed changes in streamflow for the years 2045 and 2075 against that of the reference baseline year of 1980. The results indicate situations of likely increase and decrease in mean monthly streamflow discharge and increase in the frequency and variability in peak flows during the periods from the mid to end of the 21st century. Seasonally, monthly streamflow increases between 50% and 250% were found for spring and autumn months with decreases in summer months for 2045. Spring and summer months for 2075 resulted in increased monthly streamflow between 50% and 300%, while autumn and spring months experienced decreased streamflow. While the results are prone to inherent uncertainties in the downscaled GCM data used, the simulated dynamics in streamflow and water availability provide critical information for stakeholders to develop sustainable water management and climate change adaptation options for the ARB.

Phytoremediation is an important and effective tool to remove different contaminations in the soil, water and air. The main objective of this research was to evaluate the effect of phytoremediation using one species of shrubs, Nerium oleander plants, on reducing the heavy metals (HMs) contamination in the soil. The present study was carried out during 2015 in El-Dakhyla (industrial zone), Alexandria Egypt. A simple Uptake Plant Model (UPM) was used to estimate the contribution of various pathways in remediating ((Lead (Pb), Cadmium (Cd) and Zinc (Zn)) through different parts, in one of the evergreen shrubs (leaves, stem and root). These include soil-root-leaf pathway, soil-air-leaf pathway, and its deposition. The model calculations revealed that the (Root concentration Factor) log RCF of the root equals 0.5, 0.41, and 0.45, respectively. The Translocation Stem Concentration Factor (TSCF) of the upward in the xylem equals 0.85, 0.98 and 0.99. Moreover, the Bioaccumulation Factors (log BCF) of the soil is 1.32, 0.014 and 0.061. In addition, the partition coefficient of Octanol-Water (log Kow) is 4.67, 2.75, and 3.35, respectively. Therefore, one may conclude that Pb was accumulated in the root, while Cd and Zn were concentrated in the aerial parts of the Nerium oleander plant. On the other hand, Pb considered one of the heavy metals where it's movement in the plants is slower than Cd and Zn due to its molecular weight is bigger than the latter HMs.

Irrigation scheduling was used to increase water productivity of wheat (Triticum aestivum L.)-maize (Zea mays L.) rotation under two climate change scenarios. Three wheat varieties and two maize hybrids were planted at in a 2-yr field experiment. CropSyst model was calibrated and validated for the collected field data, then was used to assess the impact of two climate change scenarios (A2 and B2) and three adaptation strategies (early sowing changing, irrigation schedule and the interaction between them) in the year of 2038s. The results revealed that A2 reduced yield more than B2 scenario for both crops. High yield reduction in wheat-maize rotation could be expected under climate change conditions, where wheat and maize yield will be reduced by an average of 41 and 56%, respectively. The most effective adaptation strategy for wheat was sowing 3 wk earlier and irrigation every 21 d, with irrigation water saving and no yield improvement under A2 scenario in both growing seasons. Whereas under B2 scenario yield improvement by 2% occurred with 3% saving in the applied irrigation water in the 1st growing season and in the 2nd growing season yield could improved by 8% with less than 1% increase in the applied irrigation water and higher water productivity. Changing irrigation schedule was an effective adaptation option for maize, where yield improvement could occur under both climate change scenarios in both growing seasons by up to 9% with less than 3% increase in the applied irrigation water and higher water productivity.

Long endemicity of the Highly Pathogenic Avian Influenza (HPAI) H5N1 subtype in Egypt poses a lot of threats to public health. Contrary to what is previously known, outbreaks have been circulated continuously in the poultry sectors all year round without seasonality. These changes call the need for epidemiological studies to prove or deny the influence of climate variability on outbreak occurrence, which is the aim of this study. This work proposes a modern approach to examine the degree to which the HPAI-H5N1disease event is being influenced by climate variability as a potential risk factor using generalized estimating equations (GEEs). GEE model revealed that the effect of climate variability differs according to the timing of the outbreak occurrence. Temperature and relative humidity could have both positive and negative effects on disease events. During the cold seasons especially in the first quarter, higher minimum temperatures, consistently show higher risks of disease occurrence, because this condition stimulates viral activity, while lower minimum temperatures support virus survival in the other quarters of the year with the highest negative effect in the third quarter. On the other hand, relative humidity negatively affects the outbreak in the first quarter of the year as the humid weather does not support viral circulation, while the highest positive effect was found in the second quarter during which low humidity favors the disease event.