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Uddin, M.G.; Diganta, M.T.M.; Sajib, A.M.; Hasan, M.A.; Moniruzzaman, M.; Rahman, A.; Olbert, A.I. |
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Title |
Assessment of hydrogeochemistry in groundwater using water quality index model and indices approaches |
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Journal Article |
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Year |
2023 |
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Heliyon |
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Volume |
9 |
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9 |
Pages |
19668 |
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Keywords |
CCME index, Groundwater quality, Hydrogeochemistry, Irrigation indices, Nuclear power plant, Water quality index |
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Abstract |
Groundwater resources around the world required periodic monitoring in order to ensure the safe and sustainable utilization for humans by keeping the good status of water quality. However, this could be a daunting task for developing countries due to the insufficient data in spatiotemporal resolution. Therefore, this research work aimed to assess groundwater quality in terms of drinking and irrigation purposes at the adjacent part of the Rooppur Nuclear Power Plant (RNPP) in Bangladesh. For the purposes of achieving the aim of this study, nine groundwater samples were collected seasonally (dry and wet season) and seventeen hydro-geochemical indicators were analyzed, including Temperature (Temp.), pH, electrical conductivity (EC), total dissolved solids (TDS), total alkalinity (TA), total hardness (TH), total organic carbon (TOC), bicarbonate (HCO3−), chloride (Cl−), phosphate (PO43−), sulfate (SO42−), nitrite (NO2−), nitrate (NO3−), sodium (Na+), potassium (K+), calcium (Ca2+) and magnesium (Mg2+). The present study utilized the Canadian Council of Ministers of the Environment water quality index (CCME-WQI) model to assess water quality for drinking purposes. In addition, nine indices including EC, TDS, TH, sodium adsorption ratio (SAR), percent sodium (Na%), permeability index (PI), Kelley’s ratio (KR), magnesium hazard ratio (MHR), soluble sodium percentage (SSP), and Residual sodium carbonate (RSC) were used in this research for assessing the water quality for irrigation purposes. The computed mean CCME-WQI score found higher during the dry season (ranges 48 to 74) than the wet season (ranges 40 to 65). Moreover, CCME-WQI model ranked groundwater quality between the “poor” and “marginal” categories during the wet season implying unsuitable water for human consumption. Like CCME-WQI model, majority of the irrigation index also demonstrated suitable water for crop cultivation during dry season. The findings of this research indicate that it requires additional care to improve the monitoring programme for protecting groundwater quality in the RNPP area. Insightful information from this study might be useful as baseline for national strategic planners in order to protect groundwater resources during the any emergencies associated with RNPP. |
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2405-8440 |
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THL @ christoph.kuells @ uddin_assessment_2023 |
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167 |
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Singh, A.; Patel, S.; Bhadani, V.; Kumar, V.; Gaurav, K. |
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Title |
AutoML-GWL: Automated machine learning model for the prediction of groundwater level |
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Journal Article |
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Year |
2024 |
Publication |
Engineering Applications of Artificial Intelligence |
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127 |
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107405 |
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AutoML, Bayesian optimisation, Groundwater, Machine learning |
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Predicting groundwater levels is pivotal in curbing overexploitation and ensuring effective water resource governance. However, groundwater level prediction is intricate, driven by dynamic nonlinear factors. To comprehend the dynamic interaction among these drivers, leveraging machine learning models can provide valuable insights. The drastic increase in computational capabilities has catalysed a substantial surge in the utilisation of machine learning-based solutions for effective groundwater management. The performance of these models highly depends on the selection of hyperparameters. The optimisation of hyperparameters is a complex process that often requires application-specific expertise for a skillful prediction. To mitigate the challenge posed by hyperparameter tuning’s problem-specific nature, we present an innovative approach by introducing the automated machine learning (AutoML-GWL) framework. This framework is specifically designed for precise groundwater level mapping. It seamlessly integrates the selection of best machine learning model and adeptly fine-tunes its hyperparameters by using Bayesian optimisation. We used long time series (1997-2018) data of precipitation, temperature, evaporation, soil type, relative humidity, and lag of groundwater level as input features to train the AutoML-GWL model while considering the influence of Land Use Land Cover (LULC) as a contextual factor. Among these input features, the lag of groundwater level emerged as the most relevant input feature. Once the model is trained, it performs well over the unseen data with a strong correlation of coefficient (R = 0.90), low root mean square error (RMSE = 1.22), and minimal bias = 0.23. Further, we compared the performance of the proposed AutoML-GWL with sixteen benchmark algorithms comprising baseline and novel algorithms. The AutoML-GWL outperforms all the benchmark algorithms. Furthermore, the proposed algorithm ranked first in Friedman’s statistical test, confirming its reliability. Moreover, we conducted a spatial distribution and uncertainty analysis for the proposed algorithm. The outcomes of this analysis affirmed that the AutoML-GWL can effectively manage data with spatial variations and demonstrates remarkable stability when faced with small uncertainties in the input parameters. This study holds significant promise in revolutionising groundwater management practices by establishing an automated framework for simulating groundwater levels for sustainable water resource management. |
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0952-1976 |
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THL @ christoph.kuells @ singh_automl-gwl_2024 |
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168 |
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Botha, R.; Lindsay, R.; Newman, R.T.; Maleka, P.P.; Chimba, G. |
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Radon in groundwater baseline study prior to unconventional shale gas development and hydraulic fracturing in the Karoo Basin (South Africa) |
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2019 |
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Applied Radiation and Isotopes |
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147 |
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7-13 |
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The prospect of unconventional shale gas development in the semi-arid Karoo Basin (South Africa) has created the prerequisite to temporally characterise the natural radioactivity in associated groundwater which is solely depended on for drinking and agriculture purposes. Radon (222Rn) was the primary natural radionuclide of interest in this study; however, supplementary radium (226Ra and 228Ra) in-water measurements were also conducted. A total of 53 aquifers spanning three provinces were studied during three separate measurement campaigns from 2014 to 2016. The Karoo Basin’s natural radon-in-water levels can be characterised by a minimum of 1 ± 1 Bq/L (consistent with zero or below LLD), a maximum of 183 ± 18 Bq/L and mean of 41 ± 5 Bq/L. The mean radon-in-water levels for shallow aquifers were systematically higher (55 ± 10 Bq/L) compared to deep (14 ± 3 Bq/L) or mixed aquifers (20 ± 6 Bq/L). Radon-in-water activity concentration fluctuations were predominantly observed from shallow aquifers compared to the generally steady levels of deep aquifers. A collective seasonal mean radon-in-water levels increase from the winter of 2014 (44 ± 8 Bq/L) to winter of 2016 (61 ± 16 Bq/L) was noticed which could be related to the extreme national drought experienced in 2015. Radium-in-water (228Ra and 226Ra) levels ranged from below detection level to a maximum of 0.008 Bq/L (226Ra) and 0.015 Bq/L (228Ra). The 228Ra/226Ra ratio was characterised by a minimum of 0.93, a maximum of 6.5 and a mean value of 3.3 ± 1.3. Developing and improving baseline naturally occurring radionuclide groundwater databases is vital to study potential radiological environmental impacts attributed to industrial processes such as hydraulic fracturing or mining. |
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0969-8043 |
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THL @ christoph.kuells @ botha_radon_2019 |
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169 |
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Boumaiza, L.; Ammar, S.B.; Chesnaux, R.; Stotler, R.L.; Mayer, B.; Huneau, F.; Johannesson, K.H.; Levison, J.; Knöller, K.; Stumpp, C. |
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Nitrate sources and transformation processes in groundwater of a coastal area experiencing various environmental stressors |
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2023 |
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Journal of Environmental Management |
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345 |
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118803 |
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Aquifer, Denitrification, MixSIAR, Nitrate, Nitrification, Stable isotopes |
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In coastal salinized groundwater systems, contamination from various nitrate (NO3) inputs combined with complex hydrogeochemical processes make it difficult to distinguish NO3 sources and identify potential NO3 transformtation processes. Effective field-based NO3 studies in coastal areas are needed to improve the understanding of NO3 contamination dynamics in groundwater of such complex coastal systems. This study focuses on a typical Mediterranean coastal agricultural area, located in Tunisia, experiencing substantial NO3 contamination from multiple anthropogenic sources. Here, multiple isotopic tracers (δ18OH2O, δ2HH2O, δ15NNO3, δ18ONO3, and δ11B) combined with a Bayesian isotope MixSIAR model are used (i) to identify the major NO3 sources and their contributions, and (ii) to describe the potential NO3 transformation processes. The measured NO3 concentrations in groundwater are above the natural baseline threshold, suggesting anthropogenic influence. The measured isotopic composition of NO3 indicates that manure, soil organic matter, and sewage are the potential sources of NO3, while δ11B values constrain the NO3 contamination to manure; a finding that is supported by the results of MixSIAR model revealing that manure-derived NO3 dominates over other likely sources. Nitrate derived from manure in the study area is attributed to organic fertilizers used to promote crop growth, and livestock that deposit manure directly on the ground surface. Evidence for ongoing denitrification in groundwaters of the study area is supported by an enrichment in both 15N and 18O in the remaining NO3, although isotopic mass balances between the measured and the theoretical δ18ONO3 values also suggest the occurrence of nitrification. The simultaneous occurrence of these biogeochemical processes with heterogeneous distribution across the study area reflect the complexity of interactions within the investigated coastal aquifer. The multiple isotopic tracer approach used here can identify the effect of multiple NO3 anthropogenic activities in coastal environments, which is fundamental for sustainable groundwater resources management. |
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0301-4797 |
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THL @ christoph.kuells @ boumaiza_nitrate_2023 |
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170 |
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Gardiner, J.; Thomas, R.B.; Phan, T.T.; Stuckman, M.; Wang, J.; Small, M.; Lopano, C.; Hakala, J.A. |
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Utilization of produced water baseline as a groundwater monitoring tool at a CO2-EOR site in the Permian Basin, Texas, USA |
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2020 |
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Applied Geochemistry |
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121 |
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104688 |
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CO storage, Enhanced oil recovery, Geochemical baseline, Groundwater monitoring, Produced water, Solubility trapping |
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Carbon dioxide (CO2) enhanced oil recovery (EOR) provides a pathway for economic reuse and storage of CO2, a greenhouse gas. One challenge with this practice is ensuring CO2 injection does not result in target reservoir fluids migrating into overlying shallow (\textless1000 m) groundwater formations. Effective monitoring for leakage from storage formations could involve measuring sensitive chemical indicators in overlying groundwater units and within the producing formation itself for evidence of deviation from an initial state. In this study, produced waters and overlying groundwaters were monitored over a five-year period to evaluate which geochemical signals may be useful to ensure that oilfield produced waters did not impact overlying groundwaters. During this five-year period, a mature carbonate oil reservoir in the Permian Basin transitioned from a waterflooding operation to a water-alternating-gas injection (WAG), in which the formation was flooded with CO2 and various mixtures of produced water. Significant increases in dissolved inorganic constituents [alkalinity, TDS, Na+, Cl−, SO42−] were observed in produced waters following CO2 injection; however, carbonate reservoir dissolution-precipitation reactions appear to be minimal and injected CO2 appears to be stored via solubility trapping. Although there are statistically significant geochemical variations following CO2 injection, applying isometric log-ratios to certain parameters establishes a narrow range for post-CO2 injection produced waters. This narrow range can be considered a baseline for post-CO2 injection produced waters; this baseline can be utilized to monitor overlying local groundwaters for produced water intrusion. Additionally, certain parameters [Na+, Ca2+, K+, Cl−, alkalinity, and TDS] display large concentration disparities between produced water and overlying groundwaters; these parameters would be sensitive indicators of produced water intrusion into overlying groundwaters. |
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0883-2927 |
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THL @ christoph.kuells @ gardiner_utilization_2020 |
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171 |
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