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The assessment and prediction of water quality are important aspects of water resource management. Therefore, the groundwater (GW) quality of the Nubian Sandstone Aquifer (NSSA) in El Kharga Oasis was evaluated using indexing approaches, such as the drinking water quality index (DWQI) and health index (HI), supported with multivariate analysis, artificial neural network (ANN) models, and geographic information system (GIS) techniques. For this, physical and chemical parameters were measured for 140 GW wells, which indicated Ca–Mg–SO4, mixed Ca–Mg–Cl–SO4, Na–Cl, Ca–Mg–HCO3, and mixed Na–Ca–HCO3 water facies under the influence of silicate weathering, rock–water interactions, and ion exchange processes. The GW in El Kharga Oasis had high levels of heavy metals, particularly iron (Fe) and manganese (Mn), with average concentrations above the limits recommended by the World Health Organization (WHO) for drinking water. The DWQI categorized most of the samples as not suitable for drinking (poor to very poor class), while some samples fell in the good water class. The results of the HI indicated a potential health risk due to the ingestion of water, with the risk being higher for children in only one location. However, for both children and adults, there was a low risk of dermal and ingestion exposure to the water in all locations. The contaminants could be from natural sources, such as minerals leaching from rocks and soil, or from human activities. Based on the results of ANN modeling, ANN-SC-13 was the most accurate prediction model, since it demonstrated the strongest correlation between the best characteristics and the DWQI. For example, this model’s thirteen characteristics were extremely important for predicting DWQI. The R2 value for the training, cross-validation (CV), and test data was 0.99. The ANN-SC-2 model was the best in measuring HI ingestion in adults. The R2 value for the training, CV, and test data was 1.00 for all models. The ANN-SC-2 model was the most accurate at detecting HI dermal in adults (R2 = 0.99, 0.99, and 0.99 for the training, CV, and test data sets, respectively). Finally, the integration of physicochemical parameters, water quality indices (WQIs), and ANN models can help us to understand the quality of GW and its controlling factors, and to implement the necessary measures that prevent outbreaks of various water-borne diseases that are detrimental to human health.

Irrigation has made a significant contribution to supporting the population’s expanding food demands, as well as promoting economic growth in irrigated regions. The current investigation was carried out in order to estimate the quality of the groundwater for agricultural viability in the Algerian Desert using various water quality indices and geographic information systems (GIS). In addition, support vector machine regression (SVMR) was applied to forecast eight irrigation water quality indices (IWQIs), such as the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), sodium percentage (Na%), soluble sodium percentage (SSP), potential salinity (PS), Kelly index (KI), permeability index (PI), potential salinity (PS), permeability index (PI), and residual sodium carbonate (RSC). Several physicochemical variables, such as temperature (T°), hydrogen ion concentration (pH), total dissolved solids (TDS), electrical conductivity (EC), K+, Na2+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, and NO3−, were measured from 45 deep groundwater wells. The hydrochemical facies of the groundwater resources were Ca–Mg–Cl/SO4 and Na–Cl−, which revealed evaporation, reverse ion exchange, and rock–water interaction processes. The IWQI, Na%, SAR, SSP, KI, PS, PI, and RSC showed mean values of 50.78, 43.07, 4.85, 41.78, 0.74, 29.60, 45.65, and −20.44, respectively. For instance, the IWQI for the obtained results indicated that the groundwater samples were categorized into high restriction to moderate restriction for irrigation purposes, which can only be used for plants that are highly salt tolerant. The SVMR model produced robust estimates for eight IWQIs in calibration (Cal.), with R2 values varying between 0.90 and 0.97. Furthermore, in validation (Val.), R2 values between 0.88 and 0.95 were achieved using the SVMR model, which produced reliable estimates for eight IWQIs. These findings support the feasibility of using IWQIs and SVMR models for the evaluation and management of the groundwater of complex terminal aquifers for irrigation. Finally, the combination of IWQIs, SVMR, and GIS was effective and an applicable technique for interpreting and forecasting the irrigation water quality used in both arid and semi-arid regions.

Precise knowledge of the soil organic carbon (SOC) stocks under various land uses is needed to meet the Kyoto Protocol and for the sustainability of natural resources. The purpose of the present st...

This experiment aimed to study phosphorus efficiency of six wheat genotypes (Triticum aestivum) inoculated with arbuscular mycorrhizal fungi (AMF) and to quantify the contribution of root and mycor...

Assessing soil quality is considered one the most important indicators to ensure planned and sustainable use of agricultural lands according to their potential. The current study was carried out to develop a spatial model for the assessment of soil quality, based on four main quality indices, Fertility Index (FI), Physical Index (PI), Chemical Index (CI), and Geomorphologic Index (GI), as well as the Geographic Information System (GIS) and remote sensing data (RS). In addition to the GI, the Normalized Difference Vegetation Index (NDVI) parameter were added to assess soil quality in the study area (western part of Matrouh Governorate, Egypt) as accurately as possible. The study area suffers from a lack of awareness of agriculture practices, and it depends on seasonal rain for cultivation. Thus, it is very important to assess soil quality to deliver valuable data to decision makers and regional governments to find the best ways to improve soil quality and overcome the food security problem. We integrated a Digital Elevation Model (DEM) with Sentinel-2 satellite images to extract landform units of the study area. Forty-eight soil profiles were created to represent identified geomorphic units of the investigated area. We used the model builder function and a geostatistical approach based on ordinary kriging interpolation to map the soil quality index of the study area and categorize it into different classes. The soil quality (SQ) of the study area, classified into four classes (i.e., high quality (SQ2), moderate quality (SQ3), low quality (SQ4), and very low quality (SQ5)), occupied 0.90%, 21.87%, 22.22%, and 49.23% of the total study area, respectively. In addition, 5.74% of the study area was classified as uncultivated area as a reference. The developed soil quality model (DSQM) shows substantial agreement (0.67) with the weighted additive model, according to kappa coefficient statics, and significantly correlated with land capability R2 (0.71). Hence, the model provides a full overview of SQ in the study area and can easily be implemented in similar environments to identify soil quality challenges and fight the negative factors that influence SQ, in addition to achieving environmental sustainability.

Monitoring and managing water quality parameters (WQPs) in water bodies (e.g., lakes) on a large scale using sampling-point techniques is tedious, laborious, and not highly representative. Hyperspectral and data-driven technology have provided a potentially valuable tool for the precise measurement of WQPs. Therefore, the objective of this work was to integrate WQPs, derived spectral reflectance indices (published spectral reflectance indices (PSRIs)), newly two-band spectral reflectance indices (NSRIs-2b) and newly three-band spectral indices (NSRIs-3b), and artificial neural networks (ANNs) for estimating WQPs in Lake Qaroun. Shipboard cruises were conducted to collect surface water samples at 16 different sites throughout Lake Qaroun throughout a two-year study (2018 and 2019). Different WQPs, such as total nitrogen (TN), ammonium (NH4+), orthophosphate (PO43−), and chemical oxygen demand (COD), were evaluated for aquatic use. The results showed that the highest determination coefficients were recorded with the NSRIs-3b, followed by the NSRIs-2b, and then followed by the PSRIs, which produced lower R2 with all tested WQPs. The majority of NSRIs-3bs demonstrated strong significant relationships with three WQPs (TN, NH4+, and PO43−) with (R2 = 0.70 to 0.77), and a moderate relationship with COD (R2 = 0.52 to 0.64). The SRIs integrated with ANNs would be an efficient tool for estimating the investigated four WQPs in both calibration and validation datasets with acceptable accuracy. For examples, the five features of the SRIs involved in this model are of great significance for predicting TN. Its outputs showed high R2 values of 0.92 and 0.84 for calibration and validation, respectively. The ANN-PO43−VI-17 was the highest accuracy model for predicting PO43− with R2 = 0.98 and 0.89 for calibration and validation, respectively. In conclusion, this research study demonstrated that NSRIs-3b, alongside a combined approach of ANNs models and SRIs, would be an effective tool for assessing WQPs of Lake Qaroun.

The world faces a challenge with the pervasion of multidrug-resistant bacteria that encourages scientists to develop and discover alternative, ecofriendly, and easy-to-produce new antibacterial agents. Our work is part of the greater effort of scientists around the world to achieve this goal by the biological synthesis of silver nanoparticles using cyanobacterial extracellular and intracellular components as nonchemical reducing agents. Two Egyptian cyanobacteria were isolated and identified according to 16S rRNA gene sequencing as

Water quality has deteriorated in recent years as a result of rising population and unplanned development, impacting ecosystem health. The water quality parameters of Qaroun Lake are contaminated to varying degrees, particularly for aquatic life consumption. For that, the objective of this work is to improve the assessments of surface water quality and to determine the different geo-environmental parameters affecting the lake environmental system in Qaroun Lake utilizing the weighted arithmetic water quality index (WAWQI) and four pollution indices (heavy metal pollution index (HPI), metal index (MI), contamination index (Cd), and pollution index (PI), that are enhanced by multivariate analyses as cluster analysis (CA), principal component analysis (PCA), and support vector machine regression (SVMR). Surface water samples were collected at 16 different locations from the lake during years 2018 and 2019. Thirteen physiochemical parameters were measured and used to calculate water quality indices (WQIs). The WQIs of Qaroun Lake such WAWQI, HPI, MI, Cd, PI revealed a different degree of contamination, with respect to aquatic life utilization. The WQIs result revealed that surface water in the lake is unsuitable, high polluted, and seriously affected by pollution for an aquatic environment. The PI findings revealed that surface water samples of Qaroun Lake were significantly impacted by Al, moderately affected by Cd and Cu, and while slightly affected by Zn due to uncontrolled releases of domestic and industrial wastewater. Furthermore, increasing salinity accelerates the deterioration of the lake aquatic environment. Therefore, sewage and drainage wastewater should be treated before discharging into the lake. The SVMR models based on physiochemical parameters presented the highest performance as an alternative method to predict the WQIs. For example, the calibration (Val.) and the validation (Val.) models performed best in assessing the WQIs with R2 (0.99) and with R2 (0.97–0.99), respectively. Finally, a combination of WQIs, CA, PCA, and SVMR approaches could be employed to assess surface water quality in Qaroun Lake.

The current research was carried out to estimate the potential of water hyacinth (WH) for removal of nine heavy metals (HMs) from three irrigation canals in Nile Delta. Sampling was achieved in mon...

The proper assessment of trace element concentrations in the north Nile Delta of Egypt is needed in order to reduce the high levels of toxic elements in contaminated soils. The objectives of this study were to assess the risks of contamination for four trace elements (nickel (Ni), cobalt (Co), chromium (Cr), and boron (B)) in three different layers of the soil using the geoaccumulation index (I-geo) and pollution load index (PLI) supported by GIS, as well as to evaluate the performance of partial least-square regression (PLSR) and multiple linear regression (MLR) in estimating the PLI based on data for the four trace elements in the three different soil layers. The results show a widespread contamination of I-geo Ni, Co, Cr, and B in the three different layers of the soil. The I-geo values varied from 0 to 4.74 for Ni, 0 to 6.56 for Co, 0 to 4.11 for Cr, and 0 to 4.57 for B. According to I-geo classification, the status of Ni, Cr, and B ranged from uncontaminated/moderately contaminated to strongly/extremely contaminated. Co ranged from uncontaminated/moderately contaminated to extremely contaminated. There were no significant differences in the values of I-geo for Ni, Co, Cr, and B in the three different layers of the soil. According to the PLI classification, the majority of the samples were very highly polluted. For example, 4.76% and 95.24% of the samples were unpolluted and very highly polluted, respectively, in the surface layer of the soil profiles. Additionally, 14.29% and 85.71% of the samples were unpolluted and very highly polluted, respectively, in the subsurface layer of the soil profiles. Both calibration (Cal.) and validation (Val.) models of the PLSR and MLR showed the highest performance in predicting the PLI based on data for the four studied trace elements, as an alternative method. The validation (Val.) models performed the best in predicting the PLI, with R2 = 0.89–0.93 in the surface layer, 0.91–0.96 in the subsurface layer, 0.89–0.94 in the lowest layers, and 0.92–0.94 across the three different layers. In conclusion, the integration of the I-geo, PLI, GIS technique, and multivariate models is a valuable and applicable approach for the assessment of the risk of contamination for trace elements, and the PLSR and MLR models could be used through applying chemometric techniques to evaluate the PLI in different layers of the soil.

The use of gold nanorods (GNRs) as a contrast agent in bioimaging and cell tracking has numerous advantages, primarily due to the unique optical properties of gold nanorods which allow for the use of infrared regions when imaging. Owing to their unique geometry, Au NRs exhibit surface plasmon modes in the near-infrared wavelength range, which is ideal for carrying out optical measurements in biological fluids and tissue. Gold nanorod functionalization is essential, since the Cetyltrimethyl ammonium bromide CTAB gold nanorods are toxic, and for further in vitro and in vivo experiments the nanorods should be functionalized to become optically stable and biocompatible. In the present study, gold nanorods with an longitudinal surface plasmon resonance (LSPR) position around 800 nm were synthesized in order to be used for photoacoustic imaging applications for stem cell tracking. The gold nanorods were functionalized using both thiolated poly (ethylene glycol) (PEG) to stabilize the gold nanorods surface and a CALNN–TAT peptide sequence. Both ligands were attached to the gold nanorods through an Au–sulfur bond. CALNN–TAT is known as a cell penetrating peptide which ensures endocytosis of the gold nanorods inside the mesenchymal stem cells of mice (MSCD1). Surface modifications of gold nanorods were achieved using optical spectroscopy (UV–VIS), electron microscopy (TEM), zeta-potential, and FTIR. Gold nanorods were incubated in MSCD1 in order to achieve a cellular uptake that was characterized by a transmission electron microscope (TEM). For photoacoustic imaging, Multi-Spectral Optoacoustic Tomography (MSOT) was used. The results demonstrated good cellular uptake for PEG–CALNN–TAT GNRs and the successful use of modified gold nanorods as both a contrast agent in photoacoustic imaging and as a novel tracking bioimaging technique.

The present study focuses on the risk assessment of heavy metal contamination in aquatic ecosystems by evaluating the current situation of heavy metals in seven locations (North Amer El Bahry, Amer, Bakr, Ras Gharib, July Water Floud, Ras Shokeir, and El Marageen) along the Suez Gulf coast that are well-known representative sites for petroleum activities in Egypt. One hundred and forty-six samples of surface sediments were carefully collected from twenty-seven profiles in the intertidal and surf zone. The hydrochemical parameters, such as pH and salinity (S‰), were measured during sample collection. The mineralogy study was carried out by an X-ray diffractometer (XRD), and the concentrations of Al, Mn, Fe, Cr, Cu, Co, Zn, Cd, and Pb were determined using inductively coupled plasma mass spectra (ICP-MS). The ecological risks of heavy metals were assessed by applying the contamination factor (CF), enrichment factor (EF), geoaccumulation index (Igeo), pollution load index (PLI), and potential ecological risk index (RI). The mineralogical composition mainly comprised quartz, dolomites, calcite, and feldspars. The average concentrations of the detected heavy metals, in descending order, were Al &gt, Fe &gt, Mn &gt, Cr &gt, Pb &gt, Cu &gt, Zn &gt, Ni &gt, Co &gt, Cd. A non-significant or negative relationship between the heavy metal concentration in the samples and their textural grain size characteristics was observed. The coastal surface sediment samples of the Suez Gulf contained lower concentrations of heavy metals than those published for other regions in the world with petroleum activities, except for Al, Mn, and Cr. The results for the CF, EF, and Igeo showed that Cd and Pb have severe enrichment in surface sediment and are derived from anthropogenic sources, while Al, Mn, Fe, Cr, Co, Ni, Cu, and Zn originate from natural sources. By comparison, the PLI and RI results indicate that the North Amer El Bahry and July Water Floud are considered polluted areas due to their petroleum activities. The continuous monitoring and assessment of pollutants in the Suez Gulf will aid in the protection of the environment and the sustainability of resources.

Assessing the environmental hazard of potentially toxic elements in bottom sediments has always been based entirely on ground samples and laboratory tests. This approach is remarkably accurate, but it is slow, expensive, damaging, and spatially constrained, making it unsuitable for monitoring these parameters effectively. The main goal of the present study was to assess the quality of sediment samples collected from Lake Qaroun by using different groups of spectral reflectance indices (SRIs), integrating data-driven (Artificial Neural Networks; ANN) and multivariate analysis such as multiple linear regression (MLR) and partial least square regression (PLSR). Jetty cruises were carried out to collect sediment samples at 22 distinct sites over the entire Lake Qaroun, and subsequently 21 metals were analysed. Potential ecological risk index (RI), organic matter (OM), and pollution load index (PLI) of lake’s bottom sediments were subjected to evaluation. The results demonstrated that PLI showed that roughly 59% of lake sediments are polluted (PLI > 1), especially samples of eastern and southern sides of the lake’s central section, while 41% were unpolluted (PLI < 1), which composed samples of the western and western northern regions. The RI’s findings were that all the examined sediments pose a very high ecological risk (RI > 600). It is obvious that the three band spectral indices are more efficient in quantifying different investigated parameters. The results showed the efficiency of the three tested models to predict OM, PLI, and RI, revealing that the ANN is the best model to predict these parameters. For instance, the determination coefficient values of the ANN model of calibration datasets for predicting OM, PLI, and RI were 0.999, 0.999, and 0.999, while they were 0.960, 0.897, and 0.853, respectively, for the validation dataset. The validation dataset of the PLSR produced R2 values higher than with MLR for predicting PLI and RI. Finally, the study’s main conclusion is that combining ANN, PLSR, and MLR with proximal remote sensing could be a very effective tool for the detection of OM and pollution indices. Based on our findings, we suggest the created models are easy tools for forecasting these measured parameters.

Degradation factors cause serious limitations to agricultural productivity of land of Kafr El-Sheikh Governorate, North Nile Delta. Therefore, there is a need for identifying the soil degradation types due to soil natural pedological process. This research work aims to determine the actual degradation status in the investigated area using Geographic Information Systems (GIS). Based on soil physiographic units, 160 soil profiles were dug, soil samples were collected and analysed, then soil classification was established. The results demonstrated presence of many potential degradation processes; (soil compaction, water logging, salinization and sodification). It was noted that one of major factors of land degradation in the area was soil sealing. Therefore, Fuzzy model were established to calculate the changes of land use land cover in the last three decades to assess quantitative land degradation caused by soil sealing. Land degradation caused inappropriate agricultural practices mainly associated with conservation measurements absence; improper time use of heavy machinery, over irrigation, and human intervention in natural drainage.

In Egypt, the need for accurate information on soil organic carbon (SOC) content has increased due to the importance of SOC stocks for sustainable use of natural resources and to meet the requirements of the Kyoto Protocol. Thus, the objectives of the present study are: (1) to quantify the vertical distribution of the soil bulk density (SBD) and SOC content in the soil of north Nile Delta, Egypt under different types of land-use; (2) to provide estimates of the carbon sequestration rate (CSR) of those soils under different types of land-use; and (3) to establish a baseline data on SOC stocks for future studies on SOC dynamics. Ten sampling stations were selected to represent the north Nile Delta during May 2014. In each of the sampling station, 4 sampling sites were selected to represent the virgin lands, 4 to represent fish farms and 12 to represent crop lands (four cultivation histories: 5, 15, 30 and 50 years × three crop types: clover, Trifolium alexandrinum L., sugar beet, Beta vulgaris L., and rice, Oryza sativa L.). Effect of crop type was significant in relation to SBD, SOC content, and SOC stock. In general, SOC stock increases as the number of years of cultivation increases. The SOC stock under crop land and fish farm were 1.6 and 1.5 times as that of virgin land. Rice was the crop with lowest SBD and highest SOC stock. The average CSR of crop land was 352, 134, 88 and 62 g C m−2 year−1 for 5, 15, 30 and 50 years of cultivation, respectively. The highest CSR (545 g C m−2 year−1) was observed in crop land cultivated for 5 years by rice, while the lowest (21 g C m−2 year−1) was observed in crop land cultivated for 50 years by sugar beet. On the other hand, the average CSR of fish farm was 143 g C m−2 year−1. In conclusion, the conversion of virgin land into crop land or fish farm contributed to SOC sequestration.

Today, the global food security is one of the most pressing issues for humanity, and, according to Food and Agriculture Organisation (FAO), the increasing demand for food is likely to grow by 70% until 2050. In this current condition and future scenario, the agricultural production is a critical factor for global food security and for facing the food security challenge, with specific reference to many African countries, where a large quantities of rice are imported from other continents. According to FAO, to face the Africa’s inability to reach self-sufficiency in rice, it is urgent “to redress to stem the trend of over-reliance on imports and to satisfy the increasing demand for rice in areas where the potential of local production resources is exploited at very low levels” The present study was undertaken to design a new method for land evaluation based on soil quality indicators and remote sensing data, to assess and map soil suitability for rice crop. Results from the investigations, performed in some areas in the northern part of the Nile Delta, were compared with the most common approaches, two parametric (the square root, Storie methods) and two qualitative (ALES and MicrioLEIS) methods. From the qualitative point of view, the results showed that: (i) all the models provided partly similar outputs related to the soil quality assessments, so that the distinction using the crop productivity played an important role, and (ii) outputs from the soil suitability models were consistent with both the satellite Sentinel-2 Normalize Difference Vegetation Indices (NDVI) during the crop growth and the yield production. From the quantitative point of view, the comparison of the results from the diverse approaches well fit each other, and the model, herein proposed, provided the highest performance. As a whole, a significant increasing in R2 values was provided by the model herein proposed, with R2 equal to 0.92, followed by MicroLES, Storie, ALES and Root as R2 with value equal to 0.87, 0.86, 0.84 and 0.84, respectively, with increasing percentage in R2 equal to 5%, 6% and 8%, respectively. Furthermore, the proposed model illustrated that around (i) 44.44% of the total soils of the study area are highly suitable, (ii) 44% are moderately suitable, and (iii) approximately 11.56% are unsuitable for rice due to their adverse physical and chemical soil properties. The approach herein presented can be promptly re-applied in arid region and the quantitative results obtained can be used by decision makers and regional governments.

The current research was carried out to estimate the potential of water hyacinth (WH) for removal of nine heavy metals (HMs) from three irrigation canals in Nile Delta. Sampling was achieved in monospecific and homogeneous WH stands at three irrigation canals in the study area, and WH biomass was sampled at monthly intervals from April 2014 to November 2014 using five randomly distributed quadrats (each 0.5 × 0.5 m) at each canal. All HM concentrations were significantly higher in the roots compared with the other WH organs. The WH was recognized by a bioaccumulation factor1.0 for all HMs. The WH was recognized by translocation factor1.0 for all HMs (except Pb). In many cases, the concentrations of the HMs in the different organs of WH were correlated with the same HMs in the water. Such correlations indicate that WH reflects the cumulative influences of environmental pollution from the water, and thereby suggesting its potential use in the bio-monitoring of most examined HMs. In conclusion, WH is a promising macrophyte for remediation of irrigation canals polluted with Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn.

In site-specific management, rapid and accurate identification of crop stress at a large scale is critical. Radiometric ground-based data and satellite imaging with advanced spatial and spectral resolution allow for a deeper understanding of crop stress and the level of stress in a given area. This research aimed to assess the potential of radiometric ground-based data and high-resolution QuickBird satellite imagery to determine the leaf area index (LAI), biomass fresh weight (BFW) and chlorophyll meter (Chlm) of maize across well-irrigated, water stress and salinity stress areas in the Nile Delta of Egypt. Partial least squares regression (PLSR) and multiple linear regression (MLR) were evaluated to estimate the three measured traits based on vegetation spectral indices (vegetation-SRIs) derived from these methods and their combination. Maize field visits were conducted during the summer seasons from 28 to 30 July 2007 to collect ground reference data concurrent with the acquisition of radiometric ground-based measurements and QuickBird satellite imagery. The results showed that the majority of vegetation-SRIs extracted from radiometric ground-based data and high-resolution satellite images were more effective in estimating LAI, BFW, and Chlm. In general, the vegetation-SRIs of radiometric ground-based data showed higher R2 with measured traits compared to the vegetation-SRIs extracted from high-resolution satellite imagery. The coefficient of determination (R2) of the significant relationships between vegetation-SRIs of both methods and three measured traits varied from 0.64 to 0.89. For example, with QuickBird high-resolution satellite images, the relationships of the green normalized difference vegetation index (GNDVI) with LAI and BFW showed the highest R2 of 0.80 and 0.84, respectively. Overall, the ground-based vegetation-SRIs and the satellite-based indices were found to be in good agreement to assess the measured traits of maize. Both the calibration (Cal.) and validation (Val.) models of PLSR and MLR showed the highest performance in predicting the three measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery. For example, validation (Val.) models of PLSR and MLR showed the highest performance in predicting the measured traits based on the combination of vegetation-SRIs from radiometric ground-based data and high-resolution QuickBird satellite imagery with R2 (0.91) of both methods for LAI, R2 (0.91–0.93) for BFW respectively, and R2 (0.82) of both methods for Chlm. The models of PLSR and MLR showed approximately the same performance in predicting the three measured traits and no clear difference was found between them and their combinations. In conclusion, the results obtained from this study showed that radiometric ground-based measurements and high spectral resolution remote-sensing imagery have the potential to offer necessary crop monitoring information across well-irrigated, water stress and salinity stress in regions suffering lack of freshwater resources.

Under sustainable development conditions, the water quality of irrigation systems is a complex issue which involves the combined effects of several surface water management parameters. Therefore, this work aims to enhance the surface water quality assessment and geochemical controlling mechanisms and to assess the validation of surface water networks for irrigation using six Water Quality Indices (WQIs) supported by multivariate modelling techniques, such as Principal Component Regression (PCR), Support Vector Machine Regression (SVMR) and Stepwise Multiple Linear Regression (SMLR). A total of 110 surface water samples from a network of surface water cannels during the summers of 2018 and 2019 were collected for this research and standard analytical techniques were used to measure 21 physical and chemical parameters. The physicochemical properties revealed that the major ions concentrations were reported in the following order: Ca2+ &gt, Na+ &gt, Mg2+ &gt, K+ and alkalinity &gt, SO42&minus, &gt, Cl&minus, NO3&minus, F&minus, The trace elements concentrations were reported in the following order: Fe &gt, Mn &gt, B &gt, Cr &gt, Pb &gt, Ni &gt, Cu &gt, Zn &gt, Cd. The surface water belongs to the Ca2+-Mg2+-HCO3&minus, and Ca2+-Mg2+-Cl&minus, water types, under a stress of silicate weathering and reverse ion exchange process. The computation of WQI values across two years revealed that 82% of samples represent a high class and the remaining 18% constitute a medium class of water quality for irrigation use with respect to the Irrigation Water Quality (IWQ) value, while the Sodium Percentage (Na%) values across two years indicated that 96% of samples fell into in a healthy class and 4% fell into in a permissible class for irrigation. In addition, the Sodium Absorption Ratio (SAR), Permeability Index (PI), Kelley Index (KI) and Residual Sodium Carbonate (RSC) values revealed that all surface water samples were appropriate for irrigation use. The PCR and SVMR indicated accurate and robust models that predict the six WQIs in both datasets of the calibration (Cal.) and validation (Val.), with R2 values varying from 0.48 to 0.99. The SMLR presented estimated the six WQIs well, with an R2 value that ranged from 0.66 to 0.99. In conclusion, WQIs and multivariate statistical analyses are effective and applicable for assessing the surface water quality. The PCR, SVMR and SMLR models provided robust and reliable estimates of the different indices and showed the highest R2 and the highest slopes values close to 1.00, as well as minimum values of RMSE in all models.

Assessing surface water quality for drinking use in developing countries is important since water quality is a fundamental aspect of surface water management. This study aims to improve surface water quality assessments and their controlling mechanisms using the drinking water quality index (DWQI) and four pollution indices (PIs), which are supported by multivariate statistical analyses, such as principal component analysis, partial least squares regression (PLSR), and stepwise multiple linear regression (SMLR). Twenty-two physicochemical parameters were analyzed using standard analytical methods for 55 surface water sites in the northern Nile Delta, Egypt. The DWQI results indicated that 33% of the tested samples represented good water, and 67% of samples indicated poor to unsuitable water for drinking use. The PI results revealed that surface water samples were strongly affected by Pb and Mn and were slightly affected by Fe and Cr. The SMLR models of the DWQI and PIs, which were based on all major ions and heavy metals, provided the best estimations with R2 = 1 for the DWQI and PIs. In conclusion, integration between the DWQI and PIs is a valuable and applicable approach for the assessment of surface water quality, and the PLSR and SMLR models can be used through applications of chemometric techniques to evaluate the DWQI and PIs.

Potentially toxic elements (PTEs)-induced genotoxicity on aquatic plants is still an open question. Herein, a single clone from a population of water hyacinth covering a large distribution area of Nile River (freshwater) was transplanted in two drainage water resources to explore the hazardous effect of PTEs on molecular, biochemical and anatomical characters of plants compared to those grown in freshwater. Inductivity Coupled Plasma (ICP) analysis indicated that PTEs concentrations in water resources were relatively low in most cases. However, the high tendency of water hyacinth to bio-accumulate and bio-magnify PTEs maximized their concentrations in plant samples (roots in particular). A Random Amplified Polymorphic DNA (RAPD) assay showed the genotoxic effects of PTEs on plants grown in drainage water. PTEs accumulation caused substantial alterations in DNA profiles including the presence or absence of certain bands and even the appearance of new bands. Plants grown in drainage water exhibited several mutations on the electrophoretic profiles and banding pattern of total protein, especially proteins isolated from roots. Several anatomical deteriorations were observed on PTEs-stressed plants including reductions in the thickness of epidermis, cortex and endodermis as well as vascular cylinder diameter. The research findings of this investigation may provide some new insights regarding molecular, biochemical and anatomical responses of water hyacinth grown in drainage water resources.

Waterlogged soils and sediments contaminated with potentially toxic elements (PTEs) constitute a complicated case of degraded areas; their management requires understanding of the dynamic redox-driven PTE mobilization. Such studies about PTE redox-induced dynamics in fishpond sediments are still scarce, but of great importance concerning environmental and human health risk. We studied the redox potential (EH)-induced impacts on the solubility of As, Co, Cu, Mo, Ni, Se, V, and Zn in the sediments of a fish farm in the Nile Delta, Egypt, using an automated apparatus of biogeochemical microcosm. We assessed the fate of elements as affected by the EH-induced changes in pH, Fe, Mn, SO42−, Cl−, and the dissolved aliphatic (DOC) and aromatic (DAC) organic carbon. Sediment redox ranged from −480 mV to +264 mV. Flooding the sediments caused a significant decrease in pH from 8.2 to 5.7. Dissolved concentrations of As, Co, Ni, Se, and Zn, as well as DOC, Fe, and Mn increased under the reducing acidic conditions. The release of As, Co, Ni, Se, and Zn could be attributed to the decrease of EH and the subsequent decrease of pH, as well as to the increase of DOC, and/or the dissolution of Fe–Mn oxides caused by redox reactions. Dissolved concentrations of Cu, Mo, and V increased under oxic conditions and were significantly positive correlated with EH, pH, DAC, and SO42−. This enhancement might be caused by the EH-dependent increase of pH under oxic conditions (particularly for Mo and V), which also led to DAC increase. Sulfide oxidation and the release of the associated elements may have also had a contribution, particularly in the release of Cu. Therefore, the release dynamics of dissolved Cu, Mo, and V in the sediments were controlled, to a certain extent, by the changes of EH/pH, DAC, and sulfur chemistry. We conclude that the biogeochemical differences in the behaviour of the studied elements under variable redox regimes substantially affected the fishponds via possible enhancement of PTE mobilization. Our work shows that the potential environmental risks related to PTE mobilization and fish food security should be taken into consideration for the management of degraded aquaculture systems and waterlogged soils and sediments.

Burullus lagoon is the second largest lake in Egypt. However, there has never been a comprehensive survey which studied nineteen potentially toxic elements in sediments and plants and evaluated the associated potential risk. Thus, we aimed to study the total and potentially available content of As, Al, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Sb, Se, Sn, Tl, V, and Zn in the sediments and common reed (Phragmites australis) at thirty two sites along the entire lagoon and connected drains. Contamination Factor (CF), Pollution Load Index (PLI), Geo-accumulation Index (Igeo), and Enrichment Factor (EF) were calculated to assess the grade of contamination. Element accumulation factor (AF) and bio-concentration ratio (BCR) were also calculated. Aluminum showed the highest median (mg kg−1) total content (41,200), followed by Fe (30,300), Mn (704.7), V (82.0), Zn (75.5), Cr (51.2), Cu (47.8), Ni (44.3), As (31.9), Tl (24.6), Co (21.4), Se (20.3), Sb (17.6), Sn (15.6), Mo (11.3), and Hg (16.6 μg kg−1). Values of the EF, CF, and Igeo showed that the sediments were heavily contaminated with As, Sb, Se, Tl, Mo, Sn, Co, Ni, and Cu. The drained sediment had significantly higher values of total and potentially available element content than the lagoon sediments. Sediments of the middle and western area showed significantly higher contents of total and available elements than the eastern section. The BCR and AF values indicate that the studied plant is efficient in taking up high amounts of Zn, Fe, As, Sn, Tl, Ni, Mo, Mn; then Co, Cu, and V. The results exhibit a dramatic contamination at certain sites of the lagoon, and the studied PTEs have a predominant role in contamination-related ecological risk. Further investigations concerning redox-induced mobilization of PTEs in sediments, the risk of fish contamination and the potential health hazards are highly recommended.

Urban sprawl is threatening the limited highly fertile land in the Nile delta of Egypt. Landsat TM satellite images of 1984, 1992 and ETM+ of 2006 have been used to study the impact of urban sprawl on agricultural land of the Northern Nile delta, Egypt. Visual interpretation using on screen digitizing and change detection techniques were applied for monitoring the urban sprawl. Combining the land capability map and the urban thematic layer using GIS made it possible to point out the risk of urban expansion on the expense of the highly capable soil class. The results show that a total expansion of urban area amounted to 689.20 km2 (6.3% of total area) during the study period 1984–2006. The urban expansion during the 1984–2006 was on the expense of the most fertile soils where, the high capable soils (Class I) lost 247.14 km2 (2.26 % of total area) and the moderate capable soils lost 32.73 km2 (0.3% of total area), while the low capable soils lost only 57.39 km2 (0.53% of total area). The urban encroachment over the non capable soils was very limited during the study period 1984-1992, where 7.33 km2 only was lost. The pattern of urban sprawl has been changed during the 1992 to 2006 whereas much larger area (50.64 km2) of the non capable soils was converted to urban. It can be concluded that the urban sprawl is one of the dominant degradation process on the land of Nile Delta.

In this study, the sorption behaviour of natural (clinoptilolite) zeolites with respect to cadmium (Cd), copper (Cu), nickel (Ni), lead (Pb) and zinc (Zn) has been studied in order to consider its application to purity metal finishing wastewaters. The batch method has been employed, using competitive sorption system with metal concentrations in solution ranging from 50 to 300 mg/l. The percentage sorption and distribution coefficients (Kd) were determined for the sorption system as a function of metal concentration. In addition lability of the sorbed metals was estimated by DTPA extraction following their sorption. The results showed that Freundlich model described satisfactorily sorption of all metals. Zeolite sorbed around 32, 75, 28, 99, and 59 % of the added Cd, Cu, Ni, Pb and Zn metal concentrations respectively. According to the percentage sorption and distribution coefficients values, the selectivity sequence of studied metals by zeolite can be given as Pb > Cu > Zn > Cd > Ni. About 57, 47, 78, 22, and 29 % from the total sorbed Cd, Cu, Ni, Pb and Zn recovered by DTPA indicating that lability of the adsorbed Ni was higher than, Cd, Cu, Zn, and Pb respectively. These results show that natural zeolites hold great potential to remove cationic heavy metal species from industrial wastewater.

Tanta and Quttour are the important districts in Al Gharbiya governorate, Middle of Nile Delta, Egypt. Urban sprawl is one of the main problems that reduce the limited highly fertile land in the Nile Delta of Egypt. Computational urban growth and computer hardware had made possible unprecedented increase in the availability and provide information inputs to planners. Remote sensing technology has shown its great capabilities to solve many earth resources issues.The aim of this study is to produce land use and cover map for the studied area at varied periods to monitor possible changes that may occurred, particularly in the urban areas and agriculture and subsequently predict likely changes. Two land sat images, Multispectral Scanner (MSS) in the 1972 and Enhanced Thematic Mapped (ETM) in the 2005 were used to assess the changes of agricultural lands, urban encroachment and water areas during this period with integration by GIS. The agricultural areas in Tanta and Quttour decreased by 7.17% and 5.84%, respectively from the year 1972 to 2005, while the urban areas increased by 7.17–5.84%, respectively. This urban expansion causes loss of productive agricultural lands. Finding data is useful for decision maker to investigate and monitoring illegal use of agricultural land in Nile Valley and Delta.

The main objective of the current work is to investigate the variation of the soil properties along the landforms of the arid zone around Idku Lake, northwest of the Nile Delta. Remote sensing data and Geographical Information System (GIS) were used as the main tools in this study. Digital Elevation Model (DEM) and Landsat ETM+ image were used for mapping the landforms of the area. Field work has been completed to check the accuracy of the landform units, describe the land surface features and collect soil samples. The recognized landforms comprised; river terraces, river levees, basins, sand sheets, sand dunes, hammocks, clay flats, former lake-bed, fish ponds, sabkhas and swamps. Fifty top-soil samples (0–50 cm) representing the different landforms have been collected and analyzed. The laboratory analyses were linked with their relevant geographical locations, and then the thematic maps of the soil properties were produced using Arc-GIS 9.2 software. The spatial distribution of CaCO3, EC, OM, pH, N, P, K, Fe, Mn, Cu and Zn over the different landforms was discussed in detail. The results indicate that the variation of CaCO3, EC and OM is minimal in the landforms of sand sheets, hammocks, sabkhas, clay flats and former lake-bed. On the other hand the variations of macro and micronutrients are wide-ranging in the landforms of basins river terraces and levees. The interpretation of the spatial distribution could serve as the scientific basis for land use management and the application of variable rate technology in each landform.

The present study aimed to assess the potential of Remote Sensing (RS) and Geographic Information System (GIS) to quantify soil degradation risk in some soils of the Northern Nile Delta. Physiographic units were mapped using Landsat ETM+ image (2003) and Digital Elevation Model (DEM). The obtained map showed that the study area comprised two distinct landscapes i.e. fluvial lacustrine and flood plains. The main landforms of the area under consideration are grouped as decantation basins, levees, recent river terraces, overflow basins, man-made terraces, fish ponds and turtle backs. A simple model was designed for assessing risk of land degradation depending on the equations of soil and climatic factors. The study demonstrated that about 48.09% of the study area has undergone very high risk of chemical degradation, whereas 51.91% of the area has undergone low risk of chemical degradation. About 20.12% of the total area was characterized by high risk of physical degradation. The results indicated that the salinity, alkalinity and water logging are the main common degradation hazards.