Genotoxic and Anatomical Deteriorations Associated with Potentially Toxic Elements Accumulation in Water Hyacinth Grown in Drainage Water Resources

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.</jats:p

Potentially toxic elements in saltmarsh sediments and common reed (Phragmites australis) of Burullus coastal lagoon at North Nile Delta, Egypt: A survey and risk assessment

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

Integration of radiometric ground-based data and high-resolution quickbird imagery with multivariate modeling to estimate maize traits in the nile delta of Egypt

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

Application of Irrigation Water Quality Indices and Multivariate Statistical Techniques for Surface Water Quality Assessments in the Northern Nile Delta, Egypt

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; &gt; NO3&minus; &gt; 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;-SO42&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

Biomass Fatty Acid Profile and Fuel Property Prediction of Bagasse Waste Grown <i>Nannochloropsis oculata</i>

The Chrythophyta alga Nannochloropsis oculata was mixotrophically grown in artificial media enriched with acid-prehydrolyzed bagasse waste as a source of organic carbon. The used growth medium was composed of F2 nutrients, sea salt (22.0 g L−1), and bagasse extract dissolved in sterile tap water. All of the determined growth parameters resulted in their maximums, as the alga was fed with 25% F2 growth medium enriched with 10% bagasse extract, while bagasse-extract-free medium engaged the total chlorophyll and carotenes at the expense of dry weight accumulation during the vegetative growth period. On the contrary, the dry weight under induction growth slightly differed among the different employed treatments; however, all the treatments surpassed the control one, and variation was obviously found in the cases of chlorophyll and carotene. A slight increase in oil content (6.19–11.89%) was observed, as the vegetative cells were grown under induction conditions. The fatty acids ranged between C16 and C20, and the proportions of SFA and MUFA increased from a sum of 63.57% to 88.31%, while the PUFA, including linoleic acid, α-linolenic acid, and arachidonic acid, declined from 36.3 to 11.69%. Concerning the fuel properties, the induction-produced oil surpassed the vegetative one

Polymer-Peptide Modified Gold Nanorods to Improve Cell Conjugation and Cell Labelling for Stem Cells Photoacoustic Imaging

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

Assessment of Soil Degradation and Hazards of Some Heavy Metals, Using Remote Sensing and GIS Techniques in the Northern Part of the Nile Delta, Egypt

Soil degradation and pollution is one of the main problems threatening the sustainable development of agriculture. This study used remote sensing and geographic information system (GIS) techniques to assess the risks of soil degradation and the risks of heavy metals in some soils north of the Nile Delta. The study area suffers from salinity, alkalinity, and water logging, so a spatial degradation model was used. Relying on landsat ETM+ images and the digital elevation model (DEM), it was possible to produce a geomorphological map, and it showed that the studied area consists of two landscapes, i.e., flood plain and lacustrine plain. The results indicated that salinization, alkalization, compaction, and water logging were the main types of soil degradation in the studied area. The spatial land degradation model showed that 16.61% of soils were affected by low degrees of degradation, 74.03% were affected by moderate degrees, and 9.36% were affected by high degrees of degradation. The studied area was affected by chemical degradation risks between low and high at 90.62% and 9.37%, respectively, while the physical degradation risks varied between low, moderate, high, and very high with percentages of 9.37%, 41.53%, 40.14%, and 8.93%, respectively. The environmental risks of heavy metals were assessed in the studied area using pollution indices including, the enrichment factor (EF), the pollution load index (PLI), and the potential ecological risk index (PER)

Environmental Risk Assessment of Petroleum Activities in Surface Sediments, Suez Gulf, Egypt

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

Evaluation of Groundwater Quality for Irrigation in Deep Aquifers Using Multiple Graphical and Indexing Approaches Supported with Machine Learning Models and GIS Techniques, Souf Valley, Algeria

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

A GIS-Based Approach for the Quantitative Assessment of Soil Quality and Sustainable Agriculture

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