Alaa Ahmed

Groundwater quality in arid and semi-arid regions is increasingly affected by salinization, evaporation, abstraction, and agricultural return flow. This study evaluates the hydrochemical evolution, isotopic characteristics, ²²²Rn activity, and water-use suitability of groundwater and associated waters in Karbala Governorate, central Iraq. Seventeen groundwater, lake water, and municipal supply water samples were analyzed for physicochemical parameters, major ions, δ¹⁸O, δ²H, and ²²²Rn. Hydrochemical, isotopic, and water-quality assessment methods were applied to evaluate groundwater evolution, salinization, and suitability for drinking and irrigation. The waters are near-neutral, with pH values of 6.18–7.35, but are strongly mineralized. Electrical conductivity ranges from 1440 to 16,305 µS/cm, and total dissolved solids (TDS) range from 592 to 10,191 mg/L. Most samples belong to a Ca–Mg–SO₄–Cl facies, indicating sulfate- and chloride-rich hard water evolution. The highest mineralization occurs near Karbala proper and lake-influenced sites. Ion ratios and chloro-alkaline indices indicate that evaporite dissolution, gypsum/anhydrite dissolution, carbonate interaction, evaporation, and local ion exchange jointly control groundwater chemistry. Stable isotopes indicate meteoric origin with variable evaporative enrichment; however, highly saline but isotopically depleted water, particularly W8, shows that evaporation alone cannot explain salinization. ²²²Rn activities range from below detection to 11.28 Bq/L and mainly reflect local aquifer contact and degassing. High TDS, sulfate, chloride, and very high hardness limit suitability for drinking-water use. For irrigation, the sodium hazard is low, but salinity, hardness, magnesium hazard, and permeability constraints make most samples unsuitable or restricted. Management should prioritize salinity and hardness control, treatment or blending before domestic use, restricted irrigation of the least saline wells under drainage and soil-salinity monitoring, protection of less mineralized recharge zones, and long-term monitoring of lake-adjacent and agriculturally influenced wells.

Groundwater contamination in industrial areas poses serious environmental and public-health concerns, particularly in rapidly urbanizing regions of Pakistan where aquifers are the primary source of potable and industrial water. This study evaluates the hydrochemical characteristics and fluoride (F⁻)-related health risks of groundwater in two major industrial estates, Hattar (Haripur District) and Gadoon Amazai (Swabi District), northwestern Pakistan. A total of 100 groundwater samples (50 per region) were analyzed for major ions and trace elements using ion chromatography (IC) and inductively coupled plasma-optical emission spectroscopy (ICP-OES). Groundwater exhibited alkaline conditions (pH 8.4–9.6) and moderate to high mineralization, dominated by sodium (Na⁺)-chloride (Cl⁻)-sulfate (SO₄²⁻)-calcium (Ca²⁺) facies. F⁻ concentrations ranged from 0.17–0.53 mg L⁻¹ in Hattar and 0.17–0.37 mg L⁻¹ in Gadoon, remaining below the World Health Organization guideline of 1.5 mg L⁻¹, whereas Cl⁻ varied widely (5.9–71.3 mg L⁻¹ in Hattar; up to 371.7 mg L⁻¹ in Gadoon), reflecting evaporite dissolution and effluent infiltration. Principal component analysis (PCA) extracted four components in Hattar and three in Gadoon, representing salinity-mineralization, industrial/nutrient inputs, and geogenic fluoride-pH controls. Correlation analysis confirmed strong associations among salinity-related parameters and weak coupling of F⁻ with other ions. Health-risk assessment based on the USEPA non-carcinogenic framework showed that oral ingestion dominated exposure (> 99%). Although F⁻ concentrations were within guideline limits, children exhibited comparatively higher exposure levels, while oral hazard quotient (HQ) values remained within acceptable risk limits, indicating no significant non-carcinogenic risk under current conditions. The findings highlight the need for sustained groundwater monitoring, improved industrial wastewater management.

A structurally complex Paleozoic succession in southwestern Sinai hosts uranium and associated metals, and brittle deformation controls fluid flow and ore localization. The study integrates structural mapping with mineralogical, geochemical, and radiometric data to evaluate how fault architecture controls uranium and polymetallic mineral occurrences in the east Abu Zeneima area. Eleven representative samples were collected from major fault zones and host lithofacies, and 652 ground gamma-ray spectrometric measurements were acquired across mineralized localities and Paleozoic stratigraphic units. Heavy mineral separation, SEM–BSE/EDX, X-ray diffraction, and whole-rock geochemistry were used to identify ore and accessory phases and quantify their elemental composition. The middle carbonate member of the Um Bogma Formation is the primary host lithology and contains primary U dispersed within carbonaceous sandy dolostone and locally abundant secondary U phases coexisting with Cu–Fe–Mn phases and REE-bearing silicates and phosphates. Uranium enrichment (locally >2900 ppm eU) in the targeted anomalous samples shows a positive association with P₂O₅ and a weaker positive association with ΣREEs. Together with SEM–BSE/EDX and XRD identification of uranyl phosphates and REE-bearing accessory minerals, these observations suggest that phosphate-bearing secondary phases and REE-rich accessories locally contributed to uranium hosting. Seventy-four radioactive anomalies are predominantly associated with normal faults and are concentrated along fault cores and highly fractured downthrown blocks, especially along a NW–SE trend that forms the main mineralized corridor. The study findings emphasize the importance of fault zone architecture for targeting new uranium resources in Paleozoic basins.

1. Mangrove ecosystems are unique coastal habitats composed of salt-tolerant trees and shrubs thriving primarily between 30°N and 30°S in tropical and subtropical regions (Lovelock et al., 2016; Sa...

Groundwater recharge zones should be identified in order to address water scarcity while ensuring the sustainability of aquifers. The research utilized an integrated methodology that combined Analytic Hierarchy Process (AHP), Remote Sensing (RS), and Geographic Information System (GIS) techniques to identify groundwater recharge potential zones within the Qift–El Quseir area of Egypt’s Eastern Desert. Eight key parameters, including drainage density, slope, topographic wetness index, lineament density, lithology, rainfall, land cover, and soil characteristics, were extracted from Sentinel-2 imagery, an ALOS PALSAR digital elevation model, long term gridded precipitation, and geological and land cover maps. AHP-based multi-criteria evaluation highlights lineament density and lithology as the dominant controls on recharge, followed by topographic and drainage characteristics. The resulting Groundwater Recharge Potential Index map classifies the basin into four categories, with 22.0% of the area characterized as excellent to very good, 34.9% as very good to good, 31.2% as good to moderate, and 11.9% as moderate to low. By combining these classes with class specific recharge ratios and mean annual rainfall (4.3 mm), the basin scale recharge is estimated at approximately 9.67 × 10⁶ m³/year, equivalent to about 26.8% of annual precipitation, with nearly 80% of this recharge contributed by the two highest classes. Monte Carlo sensitivity analysis and Receiver Operating Characteristic evaluation, together with geophysical and hydrochemical data, confirm the robustness and hydrogeological plausibility of the mapped recharge zones. The findings show that multi-criteria spatial modeling accurately locates the optimal groundwater recharge sites, which would aid water resource planning and policymaking in arid regions.

Reliable hydrologic modeling in arid, topographically complex watersheds depends on accurate land-use/land-cover (LULC) representation. This study evaluates how different LULC categorization methods affect simulated runoff for the Wadi Hatta watershed (UAE) using a GIS-driven machine learning framework that combines high-resolution remote sensing with hydrologic modeling. LULC maps were generated in Google Earth Engine using Random Forest (RF) and Support Vector Machine (SVM) classifiers applied to Sentinel-2 (10 m) and Landsat 8/9 (30 m) imageries and compared with the 10 m ESRI predefined LULC dataset. The resulting LULC classifications were converted to SCS Curve Numbers and used in HEC-HMS hydrologic modeling to simulate runoff under a 50-year design storm, under consistent meteorological and physical conditions. Results show that Sentinel-2 + SVM achieved the highest classification accuracy (overall accuracy up to 0.86) and produced the earliest and highest simulated peak discharge (11.4 m³/s), reflecting improved detection of impervious surfaces. In contrast, the Landsat-9 + RF scenario yielded the lowest peak (7.5 m³/s), consistent with a higher proportion of pervious land covers. LULC change analysis between 2017 and 2024 showed increases in forest cover (1.0–3.3%) and built-up areas (6.0–7.9%) driven by afforestation and urban expansion. These results demonstrate that LULC input resolution and classifier selection significantly influence hydrologic model sensitivity and runoff estimates, underscoring the need for carefully selected, high-resolution LULC products in flood risk assessment and water resource planning in data-scarce arid environments.

Urban expansion and Land Use Land Cover (LULC) change pose critical challenges for sustainable urban planning and risks to food security. This study analyzes multi-temporal Landsat imagery from 1990 to 2020 for five major cities, Islamabad, Karachi, Lahore, Peshawar, and Quetta in Pakistan using the Smile Random Forest (SRF) algorithm within the Google Earth Engine (GEE) platform. Classification accuracies ranged from 86–90%, with Cohen’s Kappa coefficients between 0.86 and 0.92, demonstrating substantial to almost perfect agreement. The results reveal significant increases in urban areas: Karachi expanded from 12.4% in 1990 to 41.3% in 2020, Lahore from 15.2% to 39.8%, and Islamabad from 9.1% to 28.6%, primarily at the expense of vegetation and barren land. Elevation also influenced LULC dynamics, with higher-altitude cities like Quetta exhibiting slower but more resource-constrained urban development. A change matrix quantified class transitions, showing that urban land predominantly expanded into agricultural and vegetative land areas, raising concerns about long-term food security. Future projections using the MOLUSCE–ANN model indicate continued urban expansion by 2030, particularly in Karachi and Lahore, where built-up areas are projected to exceed 45% of total land cover. Compared with previous studies that employed Classification and Regression Trees (CART), Support Vector Machine (SVM), and CA–Markov models in single-city or short-term contexts, this study provides a multi-decadal, multi-city analysis with predictive capacity and robust validation, offering novel insights into Pakistan’s urbanization trajectory. By linking LULC change to the implications for natural resources and food security, the study contributes actionable evidence to support actions against disaster risk reduction, sustainable development and SDGs-aligned with urban policies.