Groundwater salinization risk assessment using combined artificial intelligence models.

Assessing the risk of groundwater contamination is of crucial importance for the management of water resources, particularly in arid regions such as Menzel Habib (south-eastern Tunisia). The aim of this research is to create and validate artificial intelligence models based on the original DRASTIC vulnerability methodology to explain groundwater salinization risk (GSR). To this end, several algorithms, such as artificial neural networks (ANN), support vector regression (SVR), and multiple linear regression (MLR), were applied to the Menzel Habib aquifer system.

Enhancing water quality prediction for fluctuating missing data scenarios: A dynamic Bayesian network-based processing system to monitor cyanobacteria proliferation.

Tackling the impact of missing data in water management is crucial to ensure the reliability of scientific research that informs decision-making processes in public health. The goal of this study is to ascertain the root causes associated with cyanobacteria proliferation under major missing data scenarios. For this purpose, a dynamic missing data management methodology is proposed using Bayesian Machine Learning for accurate surface water quality prediction of a river from Limia basin (Spain).

Seasonal impact of acid mine drainage on water quality and potential ecological risk in an old sulfide exploitation.

Sulfides are usually associated with deposits of metals and coal. The reactive wastes from their exploitation, typically stored in piles and tailings dams, are often the mining sector's primary source of environmental problems. The surrounding river waters can present signs of acid mine drainage, responsible for aquatic ecosystem degradation.

Geochemistry and mineralogy of auriferous tailings deposits and their potential for reuse in Nova Lima Region, Brazil.

Since the mid-nineteenth century, gold ores, mainly hosted in sulfides, have been processed at metallurgical plants located in Nova Lima, Brazil. The generated wastes have been accumulated over the years in tailings dams or in piles. These materials represent wasted from old circuits, as well as from plants still in production.

Groundwater quality for irrigation in an arid region-application of fuzzy logic techniques.

Groundwater is the main source to answer the irrigation supply in several arid and semi-arid areas. In the present work, groundwater quality for irrigation purposes in the arid region of Menzel Habib (Tunisia) for thirty-six groundwater samples is assessed considering the application of different conventional water quality indicators, particularly, electrical conductivity (EC), sodium absorption ratio (SAR), soluble sodium percentage (SSP), magnesium adsorption ratio (MAR), Kelly ratio (KR), and permeability index (PI). The results obtained indicate a variability for EC: 3.

Rare earth elements - Source and evolution in an aquatic system dominated by mine-Influenced waters.

Acid mine drainage (AMD), formed by the instability of sulfides, typically generates acidity and releases potentially toxic elements and sulfate to the environment, among other pollutants. An example is the group of rare earth elements (REE) that may have high toxic behavior. This toxicity leads to degradation of soils, water reservoirs and rivers, promoting serious risks for the ecosystems.

Integration of water contamination indicators and vulnerability indices on groundwater management in Menzel Habib area, south-eastern Tunisia.

Groundwater salinization is a major problem throughout arid and semi-arid areas due to different natural processes and anthropogenic activities and has caused irreparable environmental and economic effects. The objective of this study is to evaluate groundwater vulnerability of Menzel Habib which has been firstly assessed using multiple methods such as DRASTIC, DRASTIC pesticide, SINTACS and SI models. These indices are based on combination of intrinsic and specific characteristics of the aquifer.

Provincial and seasonal influences on heavy metals in the Noyyal River of South India and their human health hazards.

This study was carried out to evaluate the heavy metals (Lead (Pb), Nickel (Ni), Chromium (Cr), Copper (Cu), Cadmium (Cd) and Zinc (Zn)) pollution in the Noyyal River of South India by collecting 130 river water samples (65 each in pre- and post-monsoon). The heavy metals were measured using Atomic Absorption Spectrophotometer (AAS). The data were used to calculate the associated health hazards for the inhabitants consume river water.

Geological and hydrogeological review of a semi-arid region with conflicts to water availability (southeastern Brazil).

Groundwater consumption has become increasingly relevant for urban development. The city of Montes Claros (Minas Gerais, Brazil) is located in the border region of the Brazilian semi-arid polygon and has registered conflicts of water use for decades ago. The study area contains a complex karst system responsible for the heterogeneous permeability of water recharge and groundwater flow.

Photosynthetic pigments in acid mine drainage: Seasonal patterns and associations with stressful abiotic characteristics.

Acid mine drainage represents an extreme type of water pollution. The environments develop especial hydrochemical and ecological characteristics, such as high concentrations of acidity and low biodiversity, with dominance of acidophilic organisms. Ecological criteria are assuming increasing relevance for assessing water quality, including in acid mine drainage-affected systems.