Groundwater vulnerability studies are sources of essential information for the management of water resources, aiming at the water quality preservation. Different methodologies for estimating the groundwater vulnerability, in general, or of the karst aquifer, in particular, are known. Among them, DRASTIC is one of the most popular due to its performance and easy-to-use applicability. In this article, we review DRASTIC and some DRASTIC-like methods introduced by different scientists, emphasizing their applications, advantages, and drawbacks.

Adsorption of rare earth metals, Eu (III) and Nd (III) was investigated on a new environmental friendly material, thiourea functionalized cellulose. Before usage, the synthesized material was characterized by Fourrier Transform Infrared spectroscopy and energy dispersive X-ray analysis. The influence of adsorption parameters (adsorbent dosage, time, temperature and initial metal concentration) on adsorption capacity was investigated. Experimental data were fitted by using the pseudo-first-order and pseudo-second-order kinetic models. Simultaneously thermodynamic and equilibrium studies have been carried out using Langmuir, Freundlich and Sips isotherm. Maximum adsorption capacities were reached in 30 minutes at 298 K having the value of 27 mg/g for Eu (III) and 73 mg/g for Nd (III).

The cement industry, a foundation of global infrastructure development, significantly contributes to environmental pollution. Key sources of pollution include dust emissions; greenhouse gases, particularly carbon dioxide; and the release of toxic substances such as heavy metals and particulate matter. These pollutants contribute to air, water, and soil degradation and are linked to severe health conditions in nearby populations, including respiratory disorders, cardiovascular diseases, and increased mortality rates. Noise pollution is also a significant issue, inducing auditory diseases that affect most workers in cement plants, and disturbing the population living in the neighborhoods and fauna behavior. This review explores the pollution paths and the multifaceted impacts of cement production on the environment. It also highlights the social challenges faced by communities, underscoring the urgent need for stricter environmental policies and the adoption of greener technologies to mitigate the adverse effects of cement production on both the environment and human health.

The Liwa area is a primary food production area in the United Arab Emirates (UAE) and has intensively been used for agriculture. This study investigates the pollution levels with heavy metals in agricultural soils from the Liwa area. Thirty-two soil samples were analyzed for Mn, Zn, Cr, Ni, Cu, Pb, Cd, Co, and As. Results revealed that heavy metal levels varied in the ranges 220.02–311.21, 42.39–66.92, 43.43–71.55, 32.86–52.12, 10.29–21.70, 2.83–8.84, 0.46–0.69, 0.03–0.37 mg/kg for Mn, Zn, Cr, Ni, Cu, Pb, Cd, Co, and As, respectively. All samples presented low As concentrations with an average of 0.01 mg/kg. The variations in bulk metal contents in the soil samples were related to multiple sources, including agrochemicals, atmospheric dust containing heavy metals, and traffic-related metals. Enrichment factor analysis indicates that Cd, Ni, Zn, and Cr were highly enriched in soils, and they could originate from non-crustal sources. Based on the geo-accumulation index (I_geo), the soil samples appeared uncontaminated with Mn, Cr, Zn, Pb, Co, As, Cu, uncontaminated to moderately contaminated with Ni and moderately contaminated with Cd. The contamination factors suggest low contamination, except for Ni, which showed moderate contamination. The average pollution load index (PLI) revealed unpolluted to low pollution of all soil samples. The ecological risk assessment (PERI) showed that all heavy metals posed a low risk, except for Cd which exhibited a high ecological risk.

The aim of this study was twofold. Firstly, we performed a land capability class determination of the agricultural soils from the Abu Dhabi Emirate, the United Arab Emirates, based on the concentrations of 17 chemical elements determined in the soil samples collected from 84 locations. Secondly, we assess the soil pollution with different metals, using several pollution indices. The results of Principal Component Analysis (PCA) shows that four principal components (PCs) are responsible for describing the total metals concentrations’ variance, the highest contribution on PC1 being that of Mn, and Cr, on PC2 that of Fe, on PC3 that of Cu, and on PC4 that of Al. After determining the optimal number of clusters, we classified the sites into three clusters, while the studied metals were grouped function on their concentrations. Then, we used five indices to assess the pollution level of the soil at the study sites and in the clusters. The geo—accumulation index (I_geo) indicates uncontamination/moderately contamination with Cu in cluster 1, uncontaminated/moderately contaminate soils with Cd, Cu, and Ni in cluster 2, and uncontaminated/moderately contaminated soil with Cu and moderately contaminated with Pb, Zn, and Ni in cluster 3. By comparison, the enrichment factors overestimate the pollution of the studied sites. The pollution load index (PLI) indicates a baseline level of pollution at 14 sites and the deterioration of the soil quality at four sites. The Nemerow pollution index provides similar results as PLI.

This article proposes a new approach for determining the optimal parameter (β) in the Inverse Distance Weighted Method (IDW) for spatial interpolation of hydrological data series. This is based on a genetic algorithm (GA) and finds a unique β for the entire study region, while the classical one determines different βs for different interpolated series. The algorithm is proposed in four scenarios crossover/mutation: single-point/uniform, single-point/swap, two-point/uniform, and two-point swap. Its performances are evaluated on data series collected for 41 years at ten observation sites, in terms of mean absolute error (MAE) and mean standard error (MSE). The smallest errors are obtained in the two-point swap scenario. Comparisons of the results with those of the ordinary kriging (KG), classical IDW (with β = 2 and the optimum beta found by our algorithm), and the Optimized IDW with Particle Swarm Optimization (OIDW) for each study data series show that the present approach better performs in 70% (80%) cases.