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Keywords = groundwater quality

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15 pages, 7611 KiB  
Article
The Hydrochemical Characteristics and Formation Mechanism of Highly Mineralized Coal Mine Water in Semi-Arid Regions in Northwest China
by Jian Yang, Wei Zhao, Xiangyang Liang and Feng Xu
Water 2024, 16(16), 2244; https://doi.org/10.3390/w16162244 - 8 Aug 2024
Viewed by 381
Abstract
The over-exploitation of groundwater and the deterioration of its quality have heightened the importance of non-traditional water resources, such as mine water. The study of the water’s chemical characteristics and the formation mechanism of high-salinity mine water in semi-arid regions holds significant importance [...] Read more.
The over-exploitation of groundwater and the deterioration of its quality have heightened the importance of non-traditional water resources, such as mine water. The study of the water’s chemical characteristics and the formation mechanism of high-salinity mine water in semi-arid regions holds significant importance for zero discharge and the resource utilization of mine water in Northwest China. In this study, a total of 38 groundwater and mine water samples were collected to examine the hydrogeochemical characteristics of high-salinity mine water using Piper diagrams and Gibbs diagrams, as well as isotope analyses and ion ratio coefficients. Additionally, the corresponding mine water treatment recommendations were put forward. The results show that the TDS content of groundwater increases with hydrographic depth. The average TDS concentration of Quaternary, Luohe, and Anding groundwater is 336.87, 308.67, and 556.29 mg/L, respectively. However, the TDS concentration of Zhiluo groundwater and mine water is 2768.57 and 3826.40 mg/L, respectively, which belong to high-salinity water. The Quaternary, Luohe, and Anding groundwater hydrochemical type is predominantly HCO3-Ca type, and the Zhiluo groundwater and mine water hydrochemical type is predominantly the SO4-Na type. Furthermore, there is minimal difference observed in δD and δ18O values among these waters. It can be inferred that the Zhiluo Formation in groundwater serves as the primary source of mine water supply, primarily influenced by the processes of concentration caused by evaporation. The high salinity of mine water is closely related to the high salinity of Zhiluo groundwater. The high salinity of groundwater has evolved gradually under the control of the concentration caused by evaporation and rock-weathering processes. The dissolution of salt rock, gypsum, along with other minerals, serves as the material basis for high-salinity groundwater formation. In addition, the evolution of major ions is also affected by cation exchange. The TDS concentration of mine water ranges from 3435.4 mg/L to 4414.3 mg/L, and the combined treatment process of nanofiltration and reverse osmosis can be selected to remove the salt. After treatment, mine water can be used for productive, domestic, and ecological demands. Full article
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<p>Location, water sampling, and hydrogeological histogram of study area.</p>
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<p>Piper diagram.</p>
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<p>Schoeller diagram and variable coefficient diagram of ions. (<b>a</b>) Schoeller diagram; (<b>b</b>) Variable coefficient diagram of ions.</p>
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<p>Correlation analysis chart of high-salinity water samples.</p>
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<p>δD-δ<sup>18</sup>O diagram illustrating water samples collected from the study area.</p>
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<p>Gibbs diagram of water samples from study area.</p>
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<p>Ion ratio diagram. (<b>a</b>) Na<sup>+</sup> and Cl<sup>−</sup> relational graph; (<b>b</b>) Ca<sup>2+</sup> and <math display="inline"><semantics> <mrow> <msubsup> <mi>SO</mi> <mn>4</mn> <mrow> <mn>2</mn> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math> relational graph; (<b>c</b>) (Ca<sup>2+</sup> +Mg<sup>2+</sup>) and (<math display="inline"><semantics> <mrow> <msubsup> <mi>SO</mi> <mn>4</mn> <mrow> <mn>2</mn> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math>+HCO<sub>3</sub><sup>−</sup>) relational graph.</p>
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<p>(<b>a</b>) (Ca<sup>2+</sup> + Mg<sup>2+</sup> − HCO<sub>3</sub><sup>−</sup> − <math display="inline"><semantics> <mrow> <msubsup> <mi>SO</mi> <mn>4</mn> <mrow> <mn>2</mn> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math>)/(Na<sup>+</sup> + K<sup>+</sup> − Cl<sup>−</sup>) and (<b>b</b>) water Chlor-Alkali Index diagram.</p>
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18 pages, 2556 KiB  
Article
Simulation of Groundwater Dissolved Organic Carbon in Yufu River Basin during Artificial Recharge: Improving through the SWAT-MODFLOW-RT3D Reaction Module
by Xiaotao Hong, Xuequn Chen, Kezheng Xia, Wenqing Zhang, Zezheng Wang, Dan Liu, Shuxin Li and Wenjing Zhang
Sustainability 2024, 16(15), 6692; https://doi.org/10.3390/su16156692 - 5 Aug 2024
Viewed by 341
Abstract
To keep groundwater levels stable, Jinan’s government has implemented several water management measures. However, considerable volumes of dissolved organic carbon (DOC) can enter groundwater via water exchange, impacting groundwater stability. In this study, a SWAT-MODFLOW-RT3D model designed specifically for the Yufu River Basin [...] Read more.
To keep groundwater levels stable, Jinan’s government has implemented several water management measures. However, considerable volumes of dissolved organic carbon (DOC) can enter groundwater via water exchange, impacting groundwater stability. In this study, a SWAT-MODFLOW-RT3D model designed specifically for the Yufu River Basin is developed, and part of the code of the RT3D module is modified to simulate changes in DOC concentrations in groundwater under different artificial recharge scenarios. The ultimate objective is to offer valuable insights into the effective management of water resources in the designated study region. The modified SWAT-MODFLOW-RT3D model simulates the variations of DOC concentration in groundwater under three artificial recharge scenarios, which are (a) recharged by Yellow River water; (b) recharged by Yangtze River water; and (c) recharged by Yangtze River and Yellow River water. The study shows that the main source of groundwater DOC in the basin is exogenous water. The distribution of DOC concentration in groundwater in the basin shows obvious spatial variations due to the influence of infiltration of surface water. The area near the upstream riverbank is the earliest to be affected. With the prolongation of the artificial recharge period, the DOC concentration in groundwater gradually rises from upstream to downstream, and from both sides of the riverbank to the surrounding area. By 2030, the maximum level of DOC in the basin will exceed 6.20 mg/l. The Yellow River water recharge scenario provides more groundwater recharge and less DOC input than the other two scenarios. The findings of this study indicate that particularly when recharge water supplies are enhanced with organic carbon, DOC concentrations in groundwater may alter dramatically during artificial recharge. This coupled modeling analysis is critical for assessing the impact of recharge water on groundwater quality to guide subsequent recharge programs. Full article
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<p>Location of the Yufu River basin and its delineation in SWAT, including subbasin division, digital elevation model (DEM), river network, and artificial recharge points.</p>
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<p>Hydrogeologic profile of the strong seepage zone of the Yufu River [<a href="#B26-sustainability-16-06692" class="html-bibr">26</a>].</p>
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<p>Geographical inputs for HRU definition in SWAT: (<b>a</b>) soil types (<b>b</b>) land use and (<b>c</b>) slope classes.</p>
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<p>Data transfer process for coupled model.</p>
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<p>Differences in average daily groundwater recharge (<b>a</b>) and DOC concentrations (<b>b</b>) due to leeching in the basin.</p>
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<p>Changes in groundwater DOC concentrations (<b>a</b>) and water levels (<b>b</b>) at different locations in the basin during artificial recharge (the observation point is close to the river if the lines are solid, and it is distant from the river if the lines are dashed). (<b>c</b>) Distribution of groundwater DOC concentration in the basin at the end of artificial recharge period.</p>
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<p>Distribution of DOC concentration in groundwater in 2030 under different water recharge scenarios: (<b>a</b>) recharged by Yellow River water; (<b>b</b>) recharged by Yangtze River water; and (<b>c</b>) recharged by Yangtze River and Yellow River water.</p>
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<p>Distribution of nitrate (<b>a</b>) and bicarbonate (<b>b</b>) concentration in groundwater.</p>
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16 pages, 4938 KiB  
Article
Research on Real-Time Groundwater Quality Monitoring System Using Sensors around Livestock Burial Sites
by Jonghyun Yoon, Sunhwa Park and Kyungjin Han
Agriculture 2024, 14(8), 1278; https://doi.org/10.3390/agriculture14081278 - 2 Aug 2024
Viewed by 387
Abstract
This study aimed to establish an economical and rapid response system for carcass leachate leakage using a real-time groundwater monitoring system with sensors. In this work, four parameters, namely electrical conductivity (EC), chloride (Cl), nitrate nitrogen (NO3-N), and ammonia nitrogen (NH [...] Read more.
This study aimed to establish an economical and rapid response system for carcass leachate leakage using a real-time groundwater monitoring system with sensors. In this work, four parameters, namely electrical conductivity (EC), chloride (Cl), nitrate nitrogen (NO3-N), and ammonia nitrogen (NH4-N), were monitored. Three actual livestock burial sites were selected as pilot areas and monitored for three years, from 2019 to 2021, using these four parameters. As a result of sensor quality control, the accuracy and precision range of the four parameters were found to be acceptable, within 75~125% and ±25%, respectively. When compared to the laboratory measurement value, the field measurement value recorded by the sensors was 1.1 times higher for EC, 1.6 times higher for Cl, and 2.5 times higher for NO3-N. The correlation analysis between the lab measurement and sensor measurement results showed that the EC had the highest correlation coefficient of 0.3837. Additionally, the factor extraction results showed that the EC showed a relatively significant correlation compared to the other parameters. In summary, based on the results of this study, EC may be considered a key sensor parameter for evaluating leachate leakage from groundwater near disposal sites. Full article
(This article belongs to the Section Agricultural Water Management)
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<p>Real-time monitoring system mimetic diagram.</p>
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<p>Study area (3) characteristics.</p>
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<p>Study area (3) characteristics.</p>
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<p>An example of an anion electrode calibration curve (chloride, nitrate nitrogen, ammonium nitrogen).</p>
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<p>Site 1 monitoring by sensors.</p>
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<p>Site 2 monitoring by sensors.</p>
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<p>Site 3 monitoring by sensors.</p>
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<p>Rotated component matrix of the three principal components (PCs) extracted using a principal component analysis (PCA).</p>
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19 pages, 49838 KiB  
Article
Common-Reflection-Surface Stack with Global Simultaneous Multi-Parameter Velocity Analysis—A Fit for Shallow Seismics
by Zeno Heilmann and Gian Piero Deidda
Appl. Sci. 2024, 14(15), 6748; https://doi.org/10.3390/app14156748 - 2 Aug 2024
Viewed by 306
Abstract
In many regions, particularly coastal areas, population growth, overuse of water, and climate change have put quality and availability of water under threat. While accurate, predictive groundwater flow models are essential for effective water resource management, the precision of these models relies on [...] Read more.
In many regions, particularly coastal areas, population growth, overuse of water, and climate change have put quality and availability of water under threat. While accurate, predictive groundwater flow models are essential for effective water resource management, the precision of these models relies on the ability to determine the geometries of geological structures and hydrogeologic systems accurately. In complex geological settings or with deep aquifers, the drilling of observation wells becomes too costly and shallow seismic surveys become the method of choice. Common-Reflection-Surface stacking is being used by major oil companies for hydrocarbon exploration but can serve also as an advanced imaging method for near-surface seismic data. Its spatial stacking aperture covers a whole group of neighboring common midpoint gathers and, as such, a multitude of traces contribute to every single stacking process. Since the level of noise suppression is proportional to the number of contributing traces, Common-Reflection-Surface stacking generates a large increase in signal-to-noise ratio. In addition, the data-driven velocity analysis is a statistical process and is, as such, the more stable the more input traces it has. At the beginning, however, the spatial operator was only used for stacking, not for velocity analysis, since limiting computational demand was mandatory to obtain results within a reasonable time frame. Today’s computing facilities are thousands of times faster and even large efficiency gains do not justify the loss of effectiveness anymore that comes with a truncated velocity analysis. We show that this is particularly true for near-surface data with low signal-to-noise ratio and modest common midpoint fold. For the spatial velocity analysis, we present two options: (1) as reference, a global search of all three parameters of the Common-Reflection-Surface operator, and (2) as a quicker solution, a strategy that uses the two-parameter Common-Diffraction-Surface operator to obtain initial values for a local three-parameter optimization. For shallow P-wave data from a hydrogeological survey, we show that the computational cost of option (2) is one order of magnitude smaller than the cost of option (1), while the stack and corresponding normal-moveout velocities are very similar. Comparing the results of the spatial velocity analysis to those of preceding, computationally lighter, strategies, we find a significant improvement, both in stack section resolution and stacking parameter accuracy. Full article
(This article belongs to the Special Issue Recent Advances in Exploration Geophysics)
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<p>(<b>a</b>) A CMP gather with six different reflections and their hyperbolic approximations (red lines); (<b>b</b>) the semblance spectrum, where the coherence of the data along hyperbolas associated with a variety of NMO velocities is depicted over time and best fitting velocities were picked for the six reflections and extrapolated and interpolated to a continuous velocity model (black line); (<b>c</b>) the CMP gather after NMO correction using this model; (<b>d</b>) the stacked trace. Figure taken from [<a href="#B6-applsci-14-06748" class="html-bibr">6</a>].</p>
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<p>(<b>a</b>) CRS stacking surface in the midpoint-offset domain, displayed above the corresponding 2D velocity medium composed of two constant velocity layers, separated by a dome shaped reflector. The gray curves are the forward modeled common-offset traveltimes for this interface. The stacking surface is depicted in red and spans over an entire collection of CMP gathers, a so-called CRS super-gather. All amplitudes summed along the red surface are assigned to the point <span class="html-italic">P</span><sub>0</sub> = (<span class="html-italic">x</span><sub>0</sub>,<span class="html-italic">t</span><sub>0</sub>), where <span class="html-italic">x</span><sub>0</sub> is the coincident source and receiver coordinate and <span class="html-italic">t</span><sub>0</sub> is the traveltime of the central ZO ray, depicted as a straight blue line (Figure modified from [<a href="#B14-applsci-14-06748" class="html-bibr">14</a>]). (<b>b</b>) A collection of neighboring CMP gathers from near-surface seismic data, showing the continuation of reflection events in both midpoint and offset direction.</p>
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<p>Two eigenwaves described by the radii of curvature <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>N</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>N</mi> <mi>I</mi> <mi>P</mi> </mrow> </msub> </mrow> </semantics></math>. On the left, the NIP wave, related to a point source at the NIP, and, on the right, the N wave, related to an exploding reflector experiment around the NIP. Both wavefronts emerging at ZO location <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> are depicted as arc segments with perpendicular rays (Figure modified from [<a href="#B16-applsci-14-06748" class="html-bibr">16</a>]).</p>
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<p>Sensitivity analysis of the CRS traveltime with respect to reflector dip, reflector curvature and stacking velocity. Figure taken from [<a href="#B45-applsci-14-06748" class="html-bibr">45</a>].</p>
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<p>Raw field records from three locations along the seismic line (see arrows in Figure 13a), according to [<a href="#B46-applsci-14-06748" class="html-bibr">46</a>]. Automatic Gain Correction (AGC) with a 100 ms time window was applied to enhance the different seismic events for the display.</p>
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<p>Annotated stack section obtained by conventional NMO/DMO-Stack processing, published by [<a href="#B46-applsci-14-06748" class="html-bibr">46</a>].</p>
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<p>Automatic CMP stack result obtained in step one of the cascaded search strategy.</p>
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<p>CRS stack result obtained after the cascaded search strategy.</p>
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<p>CRS stack result after cascaded search strategy and local three-parameter optimization.</p>
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<p>CRS stack result after cascaded search plus three iterations of event-consistent smoothing, each followed by a local three-parameter optimization.</p>
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<p>CRS stack result after spatial hybrid diffraction/reflection parameter optimization.</p>
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<p>CRS stack result after global simultaneous three-parameter optimization.</p>
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<p>CMP stack after NMO/DMO processing (<b>a</b>) and the respective stacking velocities after DMO (<b>b</b>). Figure modified from [<a href="#B46-applsci-14-06748" class="html-bibr">46</a>].</p>
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<p>CRS NMO velocities obtained from cascaded search followed by local optimization.</p>
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<p>CRS NMO velocities obtained from cascaded search plus three iterations of event-consistent smoothing, each followed by a local three-parameter optimization.</p>
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<p>CRS NMO velocities obtained from hybrid diffraction/reflection parameter optimization.</p>
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<p>CRS NMO velocities obtained from full simultaneous three-parameter search.</p>
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15 pages, 1900 KiB  
Article
Heavy Metal Distribution and Health Risk Assessment in Groundwater and Surface Water of Karst Lead–Zinc Mine
by Jinmei Zhou, Zhongcheng Jiang, Xiaoqun Qin and Liankai Zhang
Water 2024, 16(15), 2179; https://doi.org/10.3390/w16152179 - 1 Aug 2024
Viewed by 537
Abstract
Heavy metal pollution seriously threatens the drinking water safety and ecological environment in karst lead–zinc mines. Fifteen groundwater and surface water samples were collected in a karst lead–zinc mine in Daxin, Chongzuo. Ten heavy metal (Mn, Zn, As, Pb, Cr, Cd, Ni, Co, [...] Read more.
Heavy metal pollution seriously threatens the drinking water safety and ecological environment in karst lead–zinc mines. Fifteen groundwater and surface water samples were collected in a karst lead–zinc mine in Daxin, Chongzuo. Ten heavy metal (Mn, Zn, As, Pb, Cr, Cd, Ni, Co, Cu, and Fe) concentrations were detected. Correlation and cluster analysis were utilized to explore the distribution characteristics and sources. The health risks were appraised using the health risk assessment model. The groundwater had more heavy metal types than the surface water, of which the concentrations and average concentrations exceeded the class III water quality standard. The mine drainage contributed most (65.10%) to the heavy metal concentrations. Pb, Zn, Cd, Mn, Co, Ni, Cu, and Fe primarily originated from the mining of the lead–zinc mine, Cr primarily came from the fuel combustion and wear of metals, and As was primarily connected with the regional geological background. The groundwater had a higher total health risk (5.12 × 10−4 a−1) than the surface water (2.17 × 10−4 a−1). In comparison with the non-carcinogenic risk, the carcinogenic risk increased by three to five orders of magnitude. The carcinogenic risk distribution of Cr and Cd represented the health risk pattern. The drinking pathway posed two to three orders of magnitude the amount of health risks that the dermal contact pathway posed. Children suffered greater health risks. Water security for children should be more strictly controlled. Zn, Cd, Pb, Mn, and Cr must be paid more attention in terms of water quality protection and management. Full article
(This article belongs to the Section Water Quality and Contamination)
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<p>The study area location and sampling site distribution.</p>
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<p>Total concentrations of heavy metals at the sampling sites.</p>
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<p>Clustering tree of the heavy metals.</p>
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19 pages, 4167 KiB  
Article
Effects of Restricted Irrigation and Straw Mulching on Corn Quality, Soil Enzyme Activity, and Water Use Efficiency in West Ordos
by Ying Zhang, Guoshuai Wang, Yanwei Liu, Bing Xu, Hexiang Zheng, Delong Tian, Jinjin Guo, Jianzhong Su, Zhiwei Ma, Feixing Zhou and Xueyi Jiang
Agronomy 2024, 14(8), 1691; https://doi.org/10.3390/agronomy14081691 - 31 Jul 2024
Viewed by 329
Abstract
Groundwater overexploitation in West Ordos necessitates sustainable irrigation practices. This study evaluated three irrigation levels—W1 (3300 m3 · ha−1), W2 (2850 m3 · ha−1), and W3 (2400 m3 · ha−1)—by modifying the wide-width planting [...] Read more.
Groundwater overexploitation in West Ordos necessitates sustainable irrigation practices. This study evaluated three irrigation levels—W1 (3300 m3 · ha−1), W2 (2850 m3 · ha−1), and W3 (2400 m3 · ha−1)—by modifying the wide-width planting pattern of maize. Additionally, two levels of straw mulch were analyzed: F1 (9000 kg · ha−1) and F2 (no mulch). The study aimed to investigate the effects of these treatments on corn growth dynamics, soil water temperature, soil enzyme activity, yield, grain quality, and water use efficiency. The results indicated a decline in growth indices, enzyme activities, grain quality, and yield under the limited irrigation levels W2 and W3 compared to W1. The highest corn yields were observed with W1F1 (6642.54 kg · ha−1) and W2F1 (6602.38 kg · ha−1), with the latter showing only a 0.6% decrease. Notably, water use efficiency in the W2F1 treatment improved by 4.69%, 12.08%, 10.27%, 12.59%, and 12.96% compared to W1F1, W3F1, W1F2, W2F2, and W3F2, respectively. Straw mulch (F1) significantly elevated the soil temperature, increasing the effective accumulated temperature during the growth period by 10.11~85.79 °C, and boosted the soil enzyme activity by 10–25%. Under limited irrigation, the W2 (2850 m3 · ha−1) and F1 (9000 kg · ha−1 straw) treatments achieved the highest water productivity of 2.48 kg·m−3, maintaining a high yield of 6602.38 kg · ha−1 while preserving nutrients essential to the corn’s quality. This approach presents a viable strategy for wide-width corn planting in groundwater-depleted regions, offering a scientifically grounded and sustainable water management solution for efficient corn production in West Ordos. Full article
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<p>Location of the trial.</p>
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<p>Experimental layout.</p>
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<p>Average temperature and precipitation during the growth period in 2023.</p>
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<p>Plant height (<b>A</b>), blades number (<b>B</b>), and stem diameter (<b>C</b>) of maize under different treatments during the whole growth period. Note: Different colors represent significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Canopy coverage (<b>A</b>) and leaf area index (<b>B</b>) of maize during the whole growth period under different treatments.</p>
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<p>Enzyme activity analysis of sucrose (<b>A</b>), protease (<b>B</b>), cellulase (<b>C</b>), urease (<b>D</b>), catalase (<b>E</b>), and phosphatase (<b>F</b>) in different treatment groups at the filling stage and mature stage. Other letters in the exact figure represent significant differences at the 0.05 level.</p>
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<p>Effects of reduced irrigation and straw mulching on soil temperature (<b>A</b>), soil water content (<b>B</b>), water use efficiency (<b>C</b>), and maize effective accumulated temperature (<b>D</b>). Soil temperature and humidity measurements are based on soil conditions of soil layers 0–10 cm. Different letters in the exact figure represent significant differences at the 0.05 level.</p>
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<p>The nutritional components of maize in six treatment groups, including crude protein (<b>A</b>), fat (<b>B</b>), fiber (<b>C</b>), ash (<b>D</b>), and nitrogen-free extract (<b>E</b>). Different letters in the figure represent significant differences at the 0.05 level.</p>
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33 pages, 7112 KiB  
Article
Assessment of the Impact of Coal Mining on Water Resources in Middelburg, Mpumalanga Province, South Africa: Using Different Water Quality Indices
by Mndeni Magagula, Ernestine Atangana and Paul Oberholster
Hydrology 2024, 11(8), 113; https://doi.org/10.3390/hydrology11080113 - 31 Jul 2024
Viewed by 494
Abstract
The objective of this study was to assess the water quality status of the surface water and groundwater resources in the Middelburg area, South Africa. The assessment was addressed using combined water quality indices, investigating selected chemical parameters over four different seasons for [...] Read more.
The objective of this study was to assess the water quality status of the surface water and groundwater resources in the Middelburg area, South Africa. The assessment was addressed using combined water quality indices, investigating selected chemical parameters over four different seasons for a period of five years from 2017 to 2021. A combination of the Canadian Council of Ministers of the Environment water quality index and the comprehensive pollution index was used to analyze the water quality status of surface water and groundwater of the town of Middelburg, situated near coal mining activities in Mpumalanga, South Africa. The combination of the indices indicated that some surface water monitoring sites ranged between poor to fair water quality. Groundwater monitoring points also showed a poor to fair ranking. The comprehensive pollution index confirmed that some sites showed very poor water quality in the summer seasons, exceeding expected limits for the period 2017 to 2021. The principal component analysis further showed that both surface water and groundwater sites had high levels of contamination with increased chemical parameters. The results were compared against the different water quality guidelines. In an extensive monitoring program, water management systems must be properly implemented to mitigate impacts on water resources. Full article
(This article belongs to the Special Issue Novel Approaches in Contaminant Hydrology and Groundwater Remediation)
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<p>Regional and drainage regions of the study area, South Africa.</p>
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<p>Surface water monitoring sites sampled from 2017 to 2021 (A–H sampling sites) [<a href="#B41-hydrology-11-00113" class="html-bibr">41</a>].</p>
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<p>Groundwater sampling sites sampled from 2017 to 2021 (BH = borehole) [<a href="#B41-hydrology-11-00113" class="html-bibr">41</a>].</p>
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<p>Average total dissolved solids (mg/L) from 2017 to 2021.</p>
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<p>Average sulfates (mg/L) from 2017 to 2021.</p>
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<p>Average electrical conductivity (mS/m) from 2017 to 2021.</p>
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<p>Average calcium (mg/L) from 2017 to 2021.</p>
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<p>Average magnesium (mg/L) from 2017 to 2021.</p>
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<p>Average pH Level from 2017 to 2021.</p>
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<p>Average total dissolved solids (mg/L) from 2017 to 2021.</p>
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<p>Average sulfates (mg/L) from 2017 to 2021.</p>
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<p>Average electrical conductivity (mS/m) from 2017 to 2021.</p>
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<p>Average calcium (mg/L) from 2017 to 2021.</p>
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<p>Principal component analysis—component plot for surface water monitoring at site A from 2017 to 2021.</p>
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<p>Principal component analysis—component plot for surface water monitoring at site B from 2017 to 2021.</p>
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<p>Principal component analysis—component plot for surface water monitoring at site D from 2017 to 2021.</p>
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<p>Principal component analysis—component plot for surface water monitoring at site G from 2017 to 2021.</p>
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<p>Principal component analysis—component plot for surface water monitoring at site H from 2017 to 2021.</p>
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<p>Principal component analysis—component plot for groundwater monitoring at site BH 1 from 2017 to 2021.</p>
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<p>Principal component analysis—component plot for groundwater monitoring site BH 2 from 2017 to 2021.</p>
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<p>Principal component analysis—component plot for groundwater monitoring at site BH 6 from 2017 to 2021.</p>
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<p>Principal component analysis—component plot for groundwater monitoring at site BH 7 from 2017 to 2021.</p>
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<p>Principal component analysis—component plot for groundwater monitoring at site BH 8 from 2017 to 2021.</p>
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<p>Predicted sulfate values in model 1 for both surface water and groundwater monitoring sites from 2017 to 2021.</p>
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<p>Predicted sulfate values in model 3 for both surface water and groundwater monitoring sites from 2017 to 2021.</p>
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<p>Predicted sulfate values in model 4 for both surface water and groundwater monitoring sites from 2017 to 2021.</p>
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16 pages, 5320 KiB  
Article
Strontium Isotopic Composition as Tracers for Identifying Groundwater Recharge Sources in the Choushui River Alluvial Plain, Western Taiwan
by Hao-Wei Huang, Shiuh-Tsuen Huang, Ruo-Mei Wang, Wen-Fu Chen, Chuan-Hsiung Chung and Chen-Feng You
Water 2024, 16(15), 2151; https://doi.org/10.3390/w16152151 - 30 Jul 2024
Viewed by 394
Abstract
Groundwater is a vital resource in the Chuoshui River alluvial plain (CSAP), a key agricultural area in Taiwan. Understanding groundwater recharge is crucial for sustainable water management amidst changing climatic conditions and increasing water demand. This study investigates the major ion composition, solute [...] Read more.
Groundwater is a vital resource in the Chuoshui River alluvial plain (CSAP), a key agricultural area in Taiwan. Understanding groundwater recharge is crucial for sustainable water management amidst changing climatic conditions and increasing water demand. This study investigates the major ion composition, solute Sr concentrations, and 87Sr/86Sr ratios in groundwater and stream water from the Choushui River (CSR) to trace groundwater recharge sources. The Piper diagram reveals that most groundwater samples are of the freshwater Ca–HCO3 type, aligning with the total dissolved solids (TDS) classification. TDS and major ion compositions indicate that groundwater near Baguashan Terrace (BGT) and Douliu Hill (DLH) primarily derives from stream water and rainwater. Na+ and Cl enrichment in some aquifers of BGT and DLH is attributed to the dissolution of paleo-sea salt and mixing with paleo-seawater from sedimentary porewater. Elevated dissolved Sr concentrations and lower 87Sr/86Sr ratios in these aquifers further support the intrusion of paleo-seawater. Groundwater in the proximal fan shows high TDS due to intensive weathering, complicating the use of TDS as a tracer. Sr isotopic compositions and solute Sr2+ concentrations effectively distinguish recharge sources, revealing that the CSR mainstream primarily recharges the proximal fan and BGT region, while CSR tributaries and rainwater mainly recharge the DLH region. This study concludes that Sr isotopic compositions and solute Sr2+ concentrations are more reliable than TDS and major ion compositions in identifying groundwater recharge sources, enhancing our understanding of groundwater origins and the processes affecting water quality. Full article
(This article belongs to the Special Issue New Application of Isotopes in Hydrology and Hydrogeology)
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<p>The sample sites and geological map of Choshui River Alluvial Plain (M—mainstream of CSR; T—tributaries of CSR; E—estuary of CSR; YL—Yuanlin well; HT—Huatan well; DF—Dungfang well; SL—Shiliu well; DH—Donghe well; WTS—Wentsu well; DZ—Dongzong well; SH—Sanhe well; GY—Ganyuan well; ES—Ershui well; TZ—Tianzhong well; JT—Jington well; LH—Liuhe well; WT—Wutu well).</p>
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<p>The (<b>a</b>) A–A’, (<b>b</b>) B–B’, and (<b>c</b>) C–C’ profiles illustrate the hydrogeological characteristics of the CSAP (adapted from [<a href="#B17-water-16-02151" class="html-bibr">17</a>,<a href="#B19-water-16-02151" class="html-bibr">19</a>]).</p>
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<p>The (<b>a</b>) A–A’, (<b>b</b>) B–B’, and (<b>c</b>) C–C’ profiles illustrate the hydrogeological characteristics of the CSAP (adapted from [<a href="#B17-water-16-02151" class="html-bibr">17</a>,<a href="#B19-water-16-02151" class="html-bibr">19</a>]).</p>
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<p>Piper diagram of water samples in this study.</p>
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<p>(<b>a</b>) TDS vs. Cl content and (<b>b</b>) Na<sup>+</sup> vs. Cl<sup>−</sup> content for the groundwater collected from near the BGT region and stream water. The mean rainwater value quotes from Li et al. [<a href="#B41-water-16-02151" class="html-bibr">41</a>]. The seawater line shown in the dashed line reflects the Na/Cl ratio of seawater.</p>
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<p>(<b>a</b>) TDS vs. Cl content and (<b>b</b>) Na<sup>+</sup> vs. Cl<sup>−</sup> content for the groundwater collected from near-DLH region and stream water. The mean rainwater value quotes from Li et al. [<a href="#B41-water-16-02151" class="html-bibr">41</a>]. The seawater line shown in the dashed line reflects the Na/Cl ratio of seawater.</p>
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<p>(<b>a</b>) TDS vs. Cl content and (<b>b</b>) Na<sup>+</sup> vs. Cl<sup>−</sup> content for the groundwater collected from proximal fan and stream water. The mean rainwater value quotes from Li et al. [<a href="#B41-water-16-02151" class="html-bibr">41</a>]. The seawater line shown in the dashed line reflects the Na/Cl ratio of seawater.</p>
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<p>The 1/Sr vs. <sup>87</sup>Sr/<sup>86</sup>Sr plot of proximal fan groundwater, CSR’s stream water, and rainwater from Cheng et al. [<a href="#B42-water-16-02151" class="html-bibr">42</a>].</p>
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<p>The 1/Sr vs. <sup>87</sup>Sr/<sup>86</sup>Sr plot of near-BGT groundwater, CSR’s stream water, and rainwater from Cheng et al. [<a href="#B42-water-16-02151" class="html-bibr">42</a>].</p>
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<p>The 1/Sr vs. <sup>87</sup>Sr/<sup>86</sup>Sr plot of near-DLH groundwater, CSR’s stream water, and rainwater from Cheng et al. [<a href="#B42-water-16-02151" class="html-bibr">42</a>].</p>
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<p>Ca<sup>2+</sup> and Mg<sup>2+</sup> vs. HCO<sub>3</sub><sup>−</sup> content for the groundwater collected from proximal fan area.</p>
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17 pages, 4920 KiB  
Article
Comprehensive Assessment of the Relationship between Metal Contamination Distribution and Human Health Risk: Case Study of Groundwater in Marituba Landfill, Pará, Brazil
by Roberta C. de O. Soares, Ricardo Jorge A. de Deus, Monia M. C. Silva, Kleber Raimundo F. Faial, Adaelson C. Medeiros and Rosivaldo de A. Mendes
Water 2024, 16(15), 2146; https://doi.org/10.3390/w16152146 - 29 Jul 2024
Viewed by 529
Abstract
Effective management of urban solid waste in the Metropolitan Region of Belém, State of Pará, Brazil is essential for conserving ecosystems and public health in eight cities, emphasizing the municipality of Marituba. Considering the vulnerability of underground water resources in Marituba to pollution [...] Read more.
Effective management of urban solid waste in the Metropolitan Region of Belém, State of Pará, Brazil is essential for conserving ecosystems and public health in eight cities, emphasizing the municipality of Marituba. Considering the vulnerability of underground water resources in Marituba to pollution due to the possible impact of leachate percolation from the landfill, this study evaluates the quality of groundwater captured in tubular wells from different adjacent locations potentially used for human consumption. For this purpose, the systematic methodologies of the groundwater quality index and human health risk assessment analysis: non-carcinogenic and carcinogenic risk to human health were used based on chronic daily intake of heavy metals by consumption and dermal adsorption of groundwater, measured through risk quotients, risk index, and incremental lifetime cancer risk. To evaluate the interrelationships of pollutants, analysis of variance, hierarchical cluster analysis, and principal component analysis were used based on the spatio-temporal quantification of pH, temperature, electrical conductivity, As, Al, Ba, Co, Cd, Cu, Cr, Fe, Hg, Ni, Pb, Sb, Se, U, and Zn. Residents of the study area are not at potential risk, as the results demonstrate that groundwater is within the potability standards of Brazilian legislation, except for aluminum concentrations, which ranged from 53.12 to 378.01 μg L−1 and 3.82 to 339.5 μg L−1 in the dry and rainy seasons, respectively, exceeding the established limit of 200.0 μg L−1. The quality index for groundwater and the heavy metal pollution index demonstrated that groundwater has good drinking quality with low metal contamination. The risk was considered low at all sampling sites in the non-carcinogenic risk assessment. Principal component analysis indicated that the sources of metal pollution are natural origins and anthropogeny. In this sense, they become worried because aluminum is a recognized neurotoxicant that can interfere with the central nervous system’s critical physiological and biochemical processes. Furthermore, despite complying with potability standards, trace concentrations of highly toxic metals such as As, Pb, Cd, and Ni may indicate initial contamination by landfill leachate. Full article
(This article belongs to the Special Issue Groundwater Quality and Human Health Risk)
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<p>Municipal network, hydrography, road system, and polygonal representation of the landfill and the wildlife refuge in the municipality of Marituba [<a href="#B22-water-16-02146" class="html-bibr">22</a>,<a href="#B23-water-16-02146" class="html-bibr">23</a>,<a href="#B24-water-16-02146" class="html-bibr">24</a>].</p>
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<p>Map of collection sites [<a href="#B25-water-16-02146" class="html-bibr">25</a>,<a href="#B26-water-16-02146" class="html-bibr">26</a>].</p>
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<p>Graphic of risk index (HI) based on non-carcinogen results.</p>
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<p>(<b>a</b>) Correlation of metals in the rainiest period; (<b>b</b>) correlation of metals in dry period.</p>
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<p>Scatter matrix with the correlation of Al and Ba.</p>
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13 pages, 2957 KiB  
Article
Microbial Metagenomics Revealed the Diversity and Distribution Characteristics of Groundwater Microorganisms in the Middle and Lower Reaches of the Yangtze River Basin
by Yue Wang, Ming-Yu Weng, Ji-Wen Zhong, Liang He, De-Jun Guo, Dong Luo and Jia-Yu Xue
Microorganisms 2024, 12(8), 1551; https://doi.org/10.3390/microorganisms12081551 - 29 Jul 2024
Viewed by 423
Abstract
Groundwater is one of the important freshwater resources on Earth and is closely related to human activities. As a good biological vector, a more diverse repertory of antibiotic resistance genes in the water environment would have a profound impact on human medical health. [...] Read more.
Groundwater is one of the important freshwater resources on Earth and is closely related to human activities. As a good biological vector, a more diverse repertory of antibiotic resistance genes in the water environment would have a profound impact on human medical health. Therefore, this study conducted a metagenomic sequencing analysis of water samples from groundwater monitoring points in the middle and lower reaches of the Yangtze River to characterize microbial community composition and antibiotic resistance in the groundwater environment. Our results show that different microbial communities and community composition were the driving factors in the groundwater environment, and a diversity of antibiotic resistance genes in the groundwater environment was detected. The main source of antibiotic resistance gene host was determined by correlation tests and analyses. In this study, metagenomics was used for the first time to comprehensively analyze microbial communities in groundwater systems in the middle and lower reaches of the Yangtze River basin. The data obtained from this study serve as an invaluable resource and represent the basic metagenomic characteristics of groundwater microbial communities in the middle and lower reaches of the Yangtze River basin. These findings will be useful tools and provide a basis for future research on water microbial community and quality, greatly expanding the depth and breadth of our understanding of groundwater. Full article
(This article belongs to the Section Environmental Microbiology)
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<p>Geographic distribution of metagenomic samples from groundwater sampling points in Jiangxi and Jiangsu.</p>
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<p>Composition of groundwater microbial communities. (<b>a</b>) Overview of microbial community structure at phylum level; (<b>b</b>) overview of microbial community structure at class level.</p>
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<p>(<b>a</b>) Heatmap displaying cluster analysis based on distance matrix of species abundance, The difference in color represents the degree of similarity in species abundance between sample sites; (<b>b</b>) NMDS plot of groundwater microbial communities.</p>
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<p>The response of groundwater microbial communities to environmental factors. (<b>a</b>) NCM (Neutral Community Model) fit of the groundwater microbial communities in Jiangxi and Jiangsu. The solid blue line represents the best fit with the NCM, and the dashed blue lines represent the 95% confidence interval predicted by the model. Microbial communities that occur more or less frequently than predicted by the NCM are shown in different colors. Nm represents the number of species in the groundwater microbial community, and BCTC (bacterial community total count) indicates the degree of model fit. (<b>b</b>) An ecological network diagram (MENs, microbial ecology networks) of groundwater microbial communities in Jiangxi and Jiangsu, constructed using Spearman correlation coefficients of relative abundance at the class level using kraken2 (version 2.1.2) (<b>c</b>) Redundancy analysis (RDA) of microbial communities at the phylum level and chemical factors in groundwater samples, with red dots representing Bray–Curtis distances between samples. The arrows indicate chemical factors influencing the composition of groundwater microbial communities. (<b>d</b>) A heatmap showing the correlation between physicochemical factors and microbial communities in groundwater.</p>
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<p>The occurrence of antibiotic resistance mechanisms. (<b>a</b>) The distribution of ARGs (antibiotic resistance genes) in groundwater samples from Jiangxi and Jiangsu. The lines and their thicknesses represent the types and quantities of ARGs detected at the sampling sites. (<b>b</b>) A box plot showing the number of different types of ARGs detected in groundwater samples; (<b>c</b>) species abundance of various resistance genes in groundwater samples; (<b>d</b>) the mechanisms of antibiotic resistance genes.</p>
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<p>(<b>a</b>) Network analysis revealing co-occurrence of groundwater microbes and ARG subtypes. Node colors distinguish between microbial and ARG types, with line thickness and color indicating strength and nature of correlations. (<b>b</b>) Distribution and abundance of ARGs in plasmids and chromosomes within groundwater environment. (<b>c</b>) Correlation analysis between antibiotic resistance genes and their host microbial communities at phylum level in groundwater.</p>
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48 pages, 1411 KiB  
Review
Fertilizers Based on Nanoparticles as Sources of Macro- and Microelements for Plant Crop Growth: A Review
by Natalia A. Semenova, Dmitriy E. Burmistrov, Sergey A. Shumeyko and Sergey V. Gudkov
Agronomy 2024, 14(8), 1646; https://doi.org/10.3390/agronomy14081646 - 27 Jul 2024
Viewed by 493
Abstract
The necessity for efficacious, sophisticated methodologies to facilitate agricultural intensification in the context of global population growth is widely accepted. One of the principal methods for enhancing the yield of plant agricultural products is the application of fertilizers. In light of the rapid [...] Read more.
The necessity for efficacious, sophisticated methodologies to facilitate agricultural intensification in the context of global population growth is widely accepted. One of the principal methods for enhancing the yield of plant agricultural products is the application of fertilizers. In light of the rapid advancement of nanotechnology over recent decades, the potential of utilizing fertilizing systems based on nanoparticles and nanomaterials—termed “nanofertilizers”—as an alternative to classical mineral fertilizers is increasingly being explored. Due to their unique properties, nanofertilizers demonstrate a number of qualities useful for agriculture. These include high activity, more accurate dosing, targeted delivery of fertilizers to plants, reduced accumulation in soils and groundwater, high durability, and so forth. This review presents a synthesis of data on the efficacy of nanofertilizers over the last decade, focusing on macro-based (N, P, K, Ca, Mg, S) and micro-based (Fe, Zn, Mn, B, Cu, Mo) nanoformulations for agricultural crops. We analyzed over 200 publications, published mainly over the last decade, on the topic of “nanofertilizers”. An analysis of published data on the effectiveness of using nanoparticles as applied fertilizers was carried out, and the effectiveness of using nanofertilizers was compared with traditional chemical fertilizers for a number of elements. Full article
(This article belongs to the Special Issue Advances in Application Effects and Mechanisms of Fertilizer Products)
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<p>Advantages of NPs allowing them to penetrate plants more easily. The figure was created using BioRender web application <a href="https://app.biorender.com/" target="_blank">https://app.biorender.com/</a> (accessed on 5 February 2024).</p>
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<p>The dynamics of the number of publications containing the keywords “Nitrogen/phosphorus/potassium/calcium/magnesium/sulfur” and “nanoparticles plant fertilizers”. Data taken from PubMed database <a href="https://pubmed.ncbi.nlm.nih.gov/" target="_blank">https://pubmed.ncbi.nlm.nih.gov/</a> (accessed on 29 April 2024).</p>
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<p>The dynamics of the number of publications containing the keywords “Iron/manganese/zinc/copper/molybdenum” and “nanoparticles plant fertilizers”. Data taken from PubMed database <a href="https://pubmed.ncbi.nlm.nih.gov/" target="_blank">https://pubmed.ncbi.nlm.nih.gov/</a> (accessed on 29 April 2024).</p>
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<p>Comparative assessment of the effectiveness of using nanofertilizers containing nitrogen (<b>a</b>), phosphorus (<b>b</b>), potassium (<b>c</b>), iron (<b>d</b>), zinc (<b>e</b>), and magnesium (<b>f</b>) in comparison with their bulk counterparts. Data taken from PubMed database <a href="https://pubmed.ncbi.nlm.nih.gov/" target="_blank">https://pubmed.ncbi.nlm.nih.gov/</a> from 2012 to 2024. Black dots represent studies that compared the effects of using both nanofertilizers containing a specific element and bulk fertilizers.</p>
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17 pages, 2052 KiB  
Article
Distribution of Natural Trace Elements in the Drinking Water Sources of Hungary
by Bálint Izsák, Katalin Hegedűs-Csondor, Petra Baják, Anita Erőss, Norbert Erdélyi and Márta Vargha
Water 2024, 16(15), 2122; https://doi.org/10.3390/w16152122 - 26 Jul 2024
Viewed by 410
Abstract
Source water quality is a key determinant of drinking water quality. The recast European Union 2020/2184 Drinking Water Directive (DWD) introduced the obligation for comprehensive risk assessment in drinking water supplies, including hazard assessment of the water source. The DWD also requires further [...] Read more.
Source water quality is a key determinant of drinking water quality. The recast European Union 2020/2184 Drinking Water Directive (DWD) introduced the obligation for comprehensive risk assessment in drinking water supplies, including hazard assessment of the water source. The DWD also requires further elements of natural origin to be monitored, including U, Ca, Mg and K. The current study is the first comprehensive assessment of 15 natural elements (B, Ba, Be, Ca, Co, K, Li, Mg, Mo, Na, Se, Sr, Ti, U and V) in 1155 (82%) Hungarian drinking water sources, including surface water, bank filtered and groundwater sources. Parameters posing a risk to health (Se, V and U) were typically below the limit of quantification (LOQ), but higher concentrations (max. 7.0, 17 and 41 µg/L, respectively) may occur in confined locations. U exceeded the DWD parametric value in one water supply. Mg and Ca in the majority of the water supplies and Li in a small geographic area reached the concentration range assumed to be protective to health. Water sources were grouped in six clusters based on their elemental distribution, some of them also showing clear geographical patterns. Surface and groundwater sources were not differentiated by composition, with the exception of karstic waters (dominated by Ca and Mg). None of the investigated parameters are expected to be a source of public health concern on a national level, but local occurrences of U and Se should be investigated and managed on a case-by-case basis. Full article
(This article belongs to the Section Water Quality and Contamination)
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<p>Geographical distribution of the sampled drinking water sources classified by water type (n = 1155).</p>
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<p>(<b>a</b>–<b>d</b>) Comparison of the six clusters by elements: (<b>a</b>) lithium, (<b>b</b>) potassium, magnesium, calcium and sodium, (<b>c</b>) barium, boron and strontium and (<b>d</b>) uranium, molybdenum and titanium. The boxplots show the median (☐), lower and upper quartile (box), 2.5 and 97.5 percentiles (whiskers), outliers (○) and extremes (*), and the statistically significant difference between clusters (letters).</p>
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<p>Spatial distribution the sampled drinking water supplies of the six HCA clusters. n = 1155.</p>
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<p>(<b>a</b>,<b>b</b>) Separation of water resource samples along the first two principal components (PC1 and PC2) on a 2D-score PCA plot depicted as (<b>a</b>) clusters and (<b>b</b>) water types.</p>
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19 pages, 6790 KiB  
Article
Feasibility of Groundwater Extraction in Nitrate-Impacted Groundwater Source in Serbia: Hydrodynamic Modeling and Nitrate Tracing
by Marija Perović, Vesna Zuber-Radenković and Miloš Zorić
Water 2024, 16(15), 2105; https://doi.org/10.3390/w16152105 - 25 Jul 2024
Viewed by 360
Abstract
Groundwater, essential for supplying drinking water to half of the global population and supporting nearly half of all irrigation needs, faces significant contamination risks. These risks pose serious threats to human health and ecosystem integrity, driven by increasing pressures from both concentrated and [...] Read more.
Groundwater, essential for supplying drinking water to half of the global population and supporting nearly half of all irrigation needs, faces significant contamination risks. These risks pose serious threats to human health and ecosystem integrity, driven by increasing pressures from both concentrated and diffuse pollution sources, as well as from growing exploitation. The presented research was conducted with the dual objectives of identifying sources of nitrate contamination (up to 128.1 mg/L) in an oxic groundwater source (Perkićevo, Serbia) and proposing an optimal extraction regimen to ensure a sufficient supply of potable water. Correlations between chemical elements’ concentrations and principal component analysis (PCA) indicated a significant relationship between anthropogenic impact indicators (NO3, Na+, B, Cl, SO42−, KMnO4 consumption, and electroconductivity), unambiguously showing that groundwater quality was primarily impacted by untreated sewage inflow and confirming nitrate’s tracer behavior in oxic environments. The spatial distribution of selected parameter concentration gradients highlighted the expansion and distribution of the contamination front. A numerical groundwater flow model (Vistas 4 and Modflow) was applied to determine the groundwater flow direction and the quantity of groundwater originating from different parts of the investigated area. Through four simulated groundwater extraction scenarios, Scenario 2, with an average extraction rate of 80 L/s from 12 wells, and Scenario 3, with an average extraction rate of 75 L/s and 4 additional wells, were identified as the most optimal, providing a sufficient quantity of adequately sanitary water. Full article
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<p>The location of the examined area, groundwater flow lines, marked sampling sites, and designated zones.</p>
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<p>Hydrogeological profile of examined alluvial plain.</p>
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<p>Diagram of the observed vs. calculated head values in the calibrated model.</p>
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<p>Four examined scenarios of groundwater extraction: (<b>a</b>) Scenario 1—existing state, average annual exploitation rate of Q = 55 L/s, with 12 existing wells (in Zone 1); (<b>b</b>) Scenario 2—existing state with maximum exploitation rate of Q = 80 L/s for a duration of three months during the dry period, with 12 existing wells (in Zone 1); (<b>c</b>) Scenario 3—average annual exploitation rate of Q = 75 L/s, with 12 existing wells and 4 new wells in Zone 2 (recommended source expansion—future state); and (<b>d</b>) Scenario 4—expansion of the water source to Zones 2 and 3, average annual exploitation rate of Q = 105 L/s, with 12 existing wells, 4 new wells in Zone 2, and 6 new wells in Zone 3 (potential source expansion—future state).</p>
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<p>Spatial gradients of parameter concentrations associated with PC1.</p>
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<p>Spatial gradients of parameter concentrations associated with PC2.</p>
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<p>Correlation matrix of selected variables (*—consumption of KMnO<sub>4</sub>).</p>
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32 pages, 15433 KiB  
Article
Screening the Performance of a Reverse Osmosis Pilot-Scale Process That Treats Blended Feedwater Containing a Nanofiltration Concentrate and Brackish Groundwater
by Christopher R. Hagglund and Steven J. Duranceau
Membranes 2024, 14(8), 164; https://doi.org/10.3390/membranes14080164 - 24 Jul 2024
Viewed by 421
Abstract
A two-stage pilot plant study has been completed that evaluated the performance of a reverse osmosis (RO) membrane process for the treatment of feedwater that consisted of a blend of a nanofiltration (NF) concentrate and brackish groundwater. Membrane performance was assessed by monitoring [...] Read more.
A two-stage pilot plant study has been completed that evaluated the performance of a reverse osmosis (RO) membrane process for the treatment of feedwater that consisted of a blend of a nanofiltration (NF) concentrate and brackish groundwater. Membrane performance was assessed by monitoring the process operation, collecting water quality data, and documenting the blended feedwater’s impact on fouling due to microbiological or organic means, plugging, and scaling, or their combination. Fluorescence and biological activity reaction tests were used to identify the types of organics and microorganisms present in the blended feedwater. Additionally, scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) were used to analyze suspended matter that collected on the surfaces of cartridge filters used in the pilot’s pretreatment system. SEM and EDS were also used to evaluate solids collected on the surfaces of 0.45 µm silver filter pads after filtering known volumes of NF concentrate and RO feedwater blends. Water quality analyses confirmed that the blended feedwater contained little to no dissolved oxygen, and a significant amount of particulate matter was absent from the blended feedwater as defined by silt density index and turbidity measurements. However, water quality results suggested that the presence of sulfate, sulfide, iron, anaerobic bacteria, and humic acid organics likely contributed to the formation of pyrite observed on some of the membrane surfaces autopsied at the conclusion of pilot operations. It was determined that first-stage membrane productivity was impacted by the location of cartridge filter pretreatment; however, second-stage productivity was maintained with no observed flux decline during the entire pilot operation’s timeline. Study results indicated that the operation of an RO process treating a blend of an NF concentrate and brackish groundwater could maintain a sustainable and productive operation that provided a practical minimum liquid discharge process operation for the NF concentrate, while the dilution of RO feedwater salinity would lower overall production costs. Full article
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<p>Aerial photograph of Jupiter Water Utility’s campus.</p>
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<p>Jupiter Water Utility’s (<b>a</b>) RO pilot unit (<b>b</b>) and its pretreatment processes.</p>
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<p>NF concentrate transfer pipeline between NF and RO process rooms.</p>
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<p>Pretreatment configurations for (<b>a</b>) Phase 1 and (<b>b</b>) Phase 2.</p>
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<p>Pretreatment configurations for (<b>a</b>) Phase 1 and (<b>b</b>) Phase 2.</p>
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<p>X saturation for the blended feedwater quality.</p>
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<p>FRI region legend.</p>
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<p>Silver SDI filter pad collected deposits corresponding with the (<b>a</b>) NF concentrate stream and (<b>b</b>) blended feedwater stream.</p>
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<p>Operational performance including (<b>a</b>) NPF and FP, (<b>b</b>) ΔP, (<b>c</b>) NDP, and (<b>d</b>) K<sub>w</sub> over approximately 2100 runtime hours.</p>
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<p>APD of operational performance parameters analyzed for the pilot’s (<b>a</b>) first stage and (<b>b</b>) second stage.</p>
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<p>Percent passage of key water quality parameters in Phases 1 and 2.</p>
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<p>Iron concentration of the RO blended feedwater, permeate, and concentrate streams.</p>
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<p>Pyrite formation pathways. Recurring parameters are shaded. SO<sub>4</sub><sup>2−</sup> and Fe are shaded gray, Fe(OH)<sub>3</sub> and HS<sup>−</sup> are shaded green, and H<sub>2</sub>S is shaded blue.</p>
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<p>Sulfate concentration over 2100 runtime hours.</p>
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<p>Brackish groundwater, NF concentrate, and blended feedwater ORP in Phases 1 and 2.</p>
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<p>Turbidity for brackish groundwater, NF concentrate, and blended feedwater in Phases 1 and 2.</p>
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<p>NF concentrate and blended feedwater SDIs from Phase 2.</p>
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<p>DOC results for the RO pilot process during Phases 1 and 2.</p>
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<p>UV–VIS results of two sampling dates in Phase 2.</p>
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<p>EEM results for the (<b>a</b>) NF concentrate, (<b>b</b>) brackish groundwater, and (<b>c</b>) blended feedwater.</p>
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<p>BART results for the NF concentrate and brackish groundwater streams in terms of (<b>a</b>) population (cfu/mL) and (<b>b</b>) reaction time (days).</p>
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<p>PED results for phosphorous (P), magnesium (Mg), potassium (K), aluminum (Al), silicon (Si), calcium (Ca), carbon (C), oxygen (O), iron (Fe), and sulfur (S).</p>
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<p>Composite pie chart findings for the CF (<b>a</b>) with carbon and oxygen and (<b>b</b>) without carbon and oxygen.</p>
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<p>PED at 110× and 2500× magnification of the blended feedwater.</p>
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<p>PED at 110× magnification of the NF concentrate for carbon (C), oxygen (O), sulfur (S), and silicon (Si).</p>
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<p>Composite pie chart findings of the (<b>a</b>) blended feedwater silver filter pad and the (<b>b</b>) NF concentrate silver filter pad.</p>
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18 pages, 5851 KiB  
Article
Traceability of Phreatic Groundwater Contaminants and the Threat to Human Health: A Case Study in the Tabu River Basin, North China
by Jing Zhang, Zilong Liao, Jing Jin, Yanyan Ni, Jian Xu, Mingxin Wang, Zihe Wang, Yiping Zhao and Yuanzheng Zhang
Sustainability 2024, 16(15), 6328; https://doi.org/10.3390/su16156328 - 24 Jul 2024
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Abstract
Groundwater is the main clean water resource in northern China, and its quality is critical for both human health and social sustainable development. Due to complex anthropogenic and/or geogenic processes, the sources of groundwater contaminants are not easy to determine. The Tabu River [...] Read more.
Groundwater is the main clean water resource in northern China, and its quality is critical for both human health and social sustainable development. Due to complex anthropogenic and/or geogenic processes, the sources of groundwater contaminants are not easy to determine. The Tabu River Basin, located in northern China, is an agriculture and pasture interlaced area in which phreatic groundwater is the predominant water resource for domestic and agricultural purposes. Groundwater with abnormally high levels of NO3, F, and TDS was observed here based on 87 groundwater samples collected from the phreatic aquifer in 2022. In this study, hydrogeochemical and isotopic methods were used to trace groundwater contaminants in the phreatic aquifer, and a risk assessment was conducted to analyze their threat to human health. The results indicated that NO3 in the phreatic groundwater primarily originated from manure, the high concentration of TDS was highly associated with irrigation, and the enrichment of F was mainly controlled by geogenic factors, including alkaline condition, competitive adsorption, the dissolution of fluorine-bearing minerals, and cation exchange. A principal component analysis (PCA) showed that both anthropogenic (PC1, 50.7%) and geogenic (PC2, 19.9%) factors determined the quality of the phreatic groundwater in the study area. The human health risk assessment demonstrated that 98.9%, 92.0%, and 80.5% of the groundwater samples exceeded the permissible limit of the total noncarcinogenic risk for children, adult females, and adult males, respectively. The monitoring results from 2022 to 2023 suggested that phreatic groundwater contamination could not be mitigated through natural attenuation under the existing external pressures. Measures need to be taken to decrease the contamination of phreatic groundwater and enhance the groundwater sustainability in the Tabu River Basin. The findings of this study can provide a reference for sustainable groundwater development in the Tabu River Basin and other arid and semi-arid regions worldwide. Full article
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Graphical abstract
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<p>(<b>a</b>) Location of the Tabu River Basin in the Inner Mongolia Autonomous Region. (<b>b</b>) Land-use map of the Tabu River Basin and the location of the sampling sites.</p>
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<p>Spatial distributions of (<b>a</b>) NO<sub>3</sub><sup>−</sup>, (<b>b</b>) F<sup>−</sup>, and (<b>c</b>) TDS in the groundwater of the Tabu River Basin.</p>
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<p>Spearman correlation heatmap; the correlation coefficients and significance levels are based on the hydrogeochemical and isotopic parameters. The symbols *, **, and *** represent statistical significance levels of <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.01, and <span class="html-italic">p</span> &lt; 0.001, respectively. Hypothesis testing was employed to evaluate the significance of the correlations among various hydrogeochemical and isotopic parameters.</p>
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<p>Relationships between (<b>a</b>) δ<sup>15</sup>N-NO<sub>3</sub><sup>−</sup> vs. δ<sup>18</sup>O-NO<sub>3</sub><sup>−</sup> and (<b>b</b>) Cl<sup>−</sup>/Na<sup>+</sup> vs. NO<sub>3</sub><sup>−</sup>/Na<sup>+</sup>. The typical ranges of NO<sub>3</sub><sup>−</sup> end-members, including atmospheric NO<sub>3</sub><sup>−</sup>, chemical fertilizer, NH<sub>4</sub><sup>+</sup> in fertilizer and rain, soil N, manure, and sewage, were derived from Xue et al. [<a href="#B45-sustainability-16-06328" class="html-bibr">45</a>] and Kendall et al. [<a href="#B46-sustainability-16-06328" class="html-bibr">46</a>].</p>
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<p>Relationships between (<b>a</b>) (Na<sup>+</sup> + K<sup>+</sup> − Cl<sup>−</sup>) vs. (Ca<sup>2+</sup> + Mg<sup>2+</sup>) − (HCO<sub>3</sub><sup>−</sup> + SO<sub>4</sub><sup>2−</sup>), (<b>b</b>) F<sup>−</sup> vs. SI<sub>fluorite</sub>, and (<b>c</b>) SI<sub>calcite</sub> vs. SI<sub>fluorite</sub>.</p>
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<p>F<sup>−</sup> and NO<sub>3</sub><sup>−</sup> concentrations in groundwater according to the four clusters.</p>
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<p>(<b>a</b>) Piper trilinear diagram. (<b>b</b>) Gibbs diagram for groundwater samples according to the four clusters.</p>
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<p>Relationship between CAI-I and CAI-II (chloro-alkaline indices). CAI-I = [Cl<sup>−</sup> − (Na<sup>+</sup> + K<sup>+</sup>)]/Cl<sup>−</sup>. CAI-II = [Cl<sup>−</sup> − (Na<sup>+</sup> + K<sup>+</sup>)]/(SO<sub>4</sub><sup>2−</sup> + HCO<sub>3</sub><sup>−</sup> + CO<sub>3</sub><sup>2−</sup>). The concentrations are represented in equivalent per mille.</p>
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<p>Principal component analysis diagram based on the hydrogeochemical and isotopic parameters.</p>
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<p>Distributions of HI<sub>total</sub> for (<b>a</b>) children, (<b>b</b>) adult females, and (<b>c</b>) adult males.</p>
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<p>Comparison of (<b>a</b>) NO<sub>3</sub><sup>−</sup> and (<b>b</b>) F<sup>−</sup> in groundwater between 2022 and 2023. The classification was based on the results of the HCA of groundwater samples in 2022.</p>
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