Well planning for every drilling project includes cost estimation. Maximizing the rate of penetration (ROP) reduces the time required for drilling, resulting in reducing the expenses required for the drilling budget. The empirical formulas developed to predict ROP have limited field applications. Since real-time drilling data acquisition and computing technologies have improved over the years, we implemented the data-driven approach for this purpose. We investigated the potential of machine learning and deep learning algorithms to predict the nonlinear behavior of the ROP. The well was drilled to confirm the geothermal reservoir characteristics for the FORGE site. After cleaning and preprocessing the data, we selected two models and optimized their hyperparameters. According to our findings, the random forest regressor and the artificial neural network predicted the behavior of our field ROP with a maximum absolute mean error of 3.98, corresponding to 19% of the ROP’s standard deviation. A tool was created to assist engineers in selecting the best drilling parameters that increase the ROP for future drilling tasks. The tool can be validated with an existing well from the same field to demonstrate its capability as an ROP predictive model.

Riverbank filtration is a cost-effective and efficient method for drinking water production, using the natural filtration capacity of the river gravelbed. Removal efficiency for organic micropollutants (OMP) in field studies is generally calculated by comparing the concentrations measured in surface water and in the wells either on the same day or with a shift of fixed time interval, neither of which can account for the variability of surface water quality and travel time in the aquifer. The present study proposes a novel method based on travel time distribution determined by a numerical transport model with a hypothesis that it will provide more reliable estimate for OMP removal. The model was developed for two production sites of Budapest Waterworks, Hungary on Danube River. River water and riverbank filtered well water were sampled regularly for one year (158 samples each) and analysed for 41 OMPs (pesticides, pharmaceutical residues and industrial pollutants). Nineteen pollutants were detected in >50 % of the well water samples. Median removal rates were 4-97 %, while the concentration of five compounds increased in some wells. Removal rates of telmisartan, tramadol, sulfamethoxazole, 4-methyl-benzotriazole, 5-methyl-benzotriazole and desethyl-terbuthylazine correlated negatively to redox potential (|r|=0.456-0.805). Median travel time increased after high flow events resulting in reduced removal of telmisartan, tramadol, 4-methyl-benzotriazole and desethyl-terbuthylazine (|r|= 0.435-0.661). Removal of diatrizoate, iopamidol, tramadol and benzotriazole increased with distance from the shore (148 vs 395 m) by 25 %, 28 %, 8 %, 16 %, respectively. Background groundwater contamination increased pesticide concentration in the wells located in agricultural areas 1.5-5-fold compared to river water. The model-based method gave more consistent results compared to traditional calculations for OMP removal efficiency during the sampling campaign and allowed for estimating the impact of various environmental factors.

Groundwater is the primary source of potable and irrigation water in Gashua, Northeastern Nigeria, where arid climatic conditions limit surface water availability. Unlike previous studies in the Chad Basin that focused on limited chemical parameters, this study applies integrated geospatial modelling and multivariate statistics to delineate hydrochemical facies and anthropogenic hotspots in a semi-arid aquifer. Twenty-five groundwater samples were analyzed for pH, electrical conductivity, total dissolved solids, total hardness, major cations (Ca²⁺, Mg²⁺, Na⁺, K⁺), and anions (Cl⁻, SO₄²⁻, NO₃⁻, HCO₃⁻). Results showed pH values from 5.7 to 7.9, EC between 415 and 1464 µS/cm, and TDS from 265.6 to 937.0 mg/L. 80% (20/25) of the samples exceeded the Nigerian Standard for Drinking Water Quality (NSDWQ) limit for hardness. Piper and Durov diagrams revealed Ca²⁺–Mg²⁺–Cl⁻–SO₄²⁻ and Ca²⁺–Mg²⁺–HCO₃⁻ facies, reflecting carbonate and sulphate dissolution, cation exchange, and evaporative concentration. Spatial analysis delineated recharge zones in the north and discharge zones in the south, with higher salinity and nitrate levels in peri-urban areas. Nitrate ranged from 10.8 to 88 mg/L, with one sample exceeding the World Health Organization (WHO) limit of 50 mg/L; sampling was conducted during the dry season. Principal Component Analysis (PCA) explained 79% of total variance and identified mineralization, pH buffering, and nitrate pollution as key processes. Hierarchical Cluster Analysis (HCA) classified wells into recharge, transitional, and discharge zones. Irrigation suitability assessed by SAR, Na⁺%, PI, KR, and MH confirmed that most samples are suitable, though moderate salinity (C2S1) and isolated sodicity risks highlight the need for site-specific management. These findings emphasize the vulnerability of groundwater to both natural geochemical evolution and human activities. The integrated hydrogeochemical and multivariate approach provides actionable implications: protecting recharge areas in the north, routine surveillance of southern salinity and nitrate hotspots, and tailored farmer advisories for irrigation practices. Such measures are critical for sustainable aquifer management in arid regions.

In the current study transit time distributions (TTDs) and mixing ratios (MR) are determined and analyzed in the riverbank filtration (RBF) systems of two operating waterworks along the Danube River. Particle tracking method was used based on a calibrated and validated numerical groundwater flow model of both groundwater resources. TTDs and MRs are showing temporal variation and are highly dependent on the instantaneous river stage and pumping rate. The validity of the obtained TTDs is verified by applying a weighted temporal mean method and comparing its result to observed stable isotope data. The water production at the two RBF systems is dominated by water of river origin. The median mixing ratio is above 90% for each studied well. Median transit times can range from around 10 days to 6 months. High temporal resolution analysis of TTDs is used to improve our understanding of the transient flow patterns of RBF systems.

Many environmental disasters occurred in the past due to site contamination caused by leakage of leachate to surround soil, surface water and ground water aquifer. The main concern is focused on the contamination potential due to migration of the leachate produced from the waste disposal or storage sites into the soil and underlying layers. Leachate quality, quantity and properties are directly relevant to waste management methods, environment conditions and waste characteristics, as well as the process of landfill processing, and the leachate may be the major source of various pollutants and emissions Problems associated with the cleanup sites have shown that remediation technologies need to be developed that are feasible, fast, and deployable in a wide range of physical settings. Using an effective landfill liner is a common way of preventing the movement of pollutants (with gas or leachate) from landfill sites or contaminated sites (e.g. brownfields, fuel stations, accidental spills, etc.). In this study different types of Geosynthetic clay liners were reviewed and they were compared in aspects of hydraulic conductivity, strength, material and chemical to improve GCL design to optimum.