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Search Results (2,704)

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23 pages, 12829 KiB  
Article
Analysis of the Response of Shallow Groundwater Levels to Precipitation Based on Different Wavelet Scales—A Case Study of the Datong Basin, Shanxi
by Hongyue Zhang, Xiaoping Rui, Ye Zhou, Wen Sun, Weiyi Xie, Chaojie Gao and Yingchao Ren
Water 2024, 16(20), 2920; https://doi.org/10.3390/w16202920 - 14 Oct 2024
Abstract
The rise in shallow groundwater levels is typically triggered by precipitation recharge, exhibiting a certain lag relative to precipitation changes. Therefore, identifying the response mechanism of shallow groundwater levels to precipitation is crucial for clarifying the interaction between precipitation and groundwater. However, the [...] Read more.
The rise in shallow groundwater levels is typically triggered by precipitation recharge, exhibiting a certain lag relative to precipitation changes. Therefore, identifying the response mechanism of shallow groundwater levels to precipitation is crucial for clarifying the interaction between precipitation and groundwater. However, the response mechanism of groundwater levels to precipitation is complex and variable, influenced by various hydrogeological and geographical conditions, and often exhibits significant nonlinear characteristics. To address this issue, this study employs methods such as continuous wavelet transform, cross wavelet transform, and wavelet coherence to analyze the response patterns of groundwater levels to precipitation at different wavelet scales in the Datong Basin from 2013 to 2022: (i) At short wavelet scales (10.33~61.96 d), the groundwater level dynamics respond almost instantaneously to extreme rainfall; (ii) At medium wavelet scales(61.96~247.83 d), the precipitation-groundwater recharge process shows characteristics of either rapid recovery or significant delay; (iii) At long wavelet scales (247.83~495.67 d), three potential groundwater processes were identified in the Datong Basin, exhibiting long-term lag responses throughout this study period, with lag times of 11.18 days, 148.75 days, and 151.49 days, respectively. Furthermore, the results indicate that the lag response time of shallow groundwater levels to precipitation is not only related to the wavelet scale but also to the identified depth conditions of different groundwater regions, groundwater extraction intensity, precipitation intensity, and aquifer lithology. This study distinguishes the temporal and spatial response mechanisms of shallow groundwater to precipitation at different wavelet scales, and this information may further aid in understanding the interaction between precipitation and groundwater levels. Full article
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Figure 1
<p>Overview of this study area.</p>
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<p>Flowchart of this research process.</p>
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<p>Annual precipitation statistics for meteorological stations.</p>
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<p>Monthly precipitation statistics for meteorological stations.</p>
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<p>Precipitation and groundwater level time series: (<b>a</b>) P1-W1; (<b>b</b>) P2-W2; (<b>c</b>) P3-W3.</p>
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<p>Precipitation and groundwater level time series: (<b>a</b>) P1-W1; (<b>b</b>) P2-W2; (<b>c</b>) P3-W3.</p>
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<p>Continuous wavelet transform spectra of precipitation and groundwater levels, with the wavelet variance plot right side of each subplot.</p>
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<p>Cross wavelet transform spectra of precipitation and groundwater levels.</p>
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<p>Wavelet coherence spectrum of precipitation and groundwater levels.</p>
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<p>Variation of average wavelet coherence values between precipitation and groundwater levels with wavelet scale.</p>
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<p>Original signals of groundwater levels and precipitation, their respective reconstructed signals, and the phase angles between them: (<b>a</b>) Wavelet scale of 11.89 d (period of 12.29 days); (<b>b</b>) Wavelet scale of 134.56 d (period of 138.99 days); (<b>c</b>) Wavelet scale of 359.22 d (period of 371.06 days).</p>
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<p>Original signals of groundwater levels and precipitation, their respective reconstructed signals, and the phase angles between them: (<b>a</b>) Wavelet scale of 11.89 d (period of 12.29 days); (<b>b</b>) Wavelet scale of 134.56 d (period of 138.99 days); (<b>c</b>) Wavelet scale of 359.22 d (period of 371.06 days).</p>
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<p>Cross wavelet transform spectra of precipitation and groundwater levels at different spatial locations.</p>
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<p>Total groundwater extraction in the Datong Basin from 2001 to 2019.</p>
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<p>(<b>a</b>) Characteristics of the groundwater level time series at monitoring well locations; (<b>b</b>) Characteristics of the precipitation time series; (<b>c</b>) Continuous wavelet transform spectrum of the groundwater level time series; (<b>d</b>) Continuous wavelet transform spectrum of the precipitation time series.</p>
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<p>Cross wavelet transform spectra of precipitation and groundwater levels at well locations Wd and We.</p>
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21 pages, 3364 KiB  
Article
Integrated Geospatial and Analytical Hierarchy Process Approach for Assessing Sustainable Management of Groundwater Recharge Potential in Barind Tract
by Md. Zahed Hossain, Sajal Kumar Adhikary, Hrithik Nath, Abdulla Al Kafy, Hamad Ahmed Altuwaijri and Muhammad Tauhidur Rahman
Water 2024, 16(20), 2918; https://doi.org/10.3390/w16202918 - 14 Oct 2024
Abstract
Groundwater depletion in Bangladesh’s Barind tract poses significant challenges for sustainable water management. This study aims to delineate groundwater recharge potential zones in this region using an integrated geospatial and Analytical Hierarchy Process (AHP) approach. The methodology combines remote-sensing data with GIS analysis, [...] Read more.
Groundwater depletion in Bangladesh’s Barind tract poses significant challenges for sustainable water management. This study aims to delineate groundwater recharge potential zones in this region using an integrated geospatial and Analytical Hierarchy Process (AHP) approach. The methodology combines remote-sensing data with GIS analysis, considering seven factors influencing groundwater recharge: rainfall, soil type, geology, slope, lineament density, land use/land cover, and drainage density. The AHP method was employed to assess the variability of groundwater recharge potential within the 7586 km2 study area. Thematic maps of relevant factors were processed using ArcGIS software. Results indicate that 9.23% (700.22 km2), 47.68% (3617.13 km2), 37.12% (2816.13 km2), and 5.97% (452.70 km2) of the study area exhibit poor, moderate, good, and very good recharge potential, respectively. The annual recharge volume is estimated at 2554 × 106 m3/year, constituting 22.7% of the total precipitation volume (11,227 × 106 m3/year). Analysis of individual factors revealed that geology has the highest influence (33.57%) on recharge potential, followed by land use/land cover (17.74%), soil type (17.25%), and rainfall (12.25%). The consistency ratio of the pairwise comparison matrix was 0.0904, indicating acceptable reliability of the AHP results. The spatial distribution of recharge zones shows a concentration of poor recharge potential in areas with low rainfall (1200–1400 mm/year) and high slope (6–40%). Conversely, very good recharge potential is associated with high rainfall zones (1800–2200 mm/year) and areas with favorable geology (sedimentary deposits). This study provides a quantitative framework for assessing groundwater recharge potential in the Barind tract. The resulting maps and data offer valuable insights for policymakers and water resource managers to develop targeted groundwater management strategies. These findings have significant implications for sustainable water resource management in the region, particularly in addressing challenges related to agricultural water demand and climate change adaptation. Full article
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<p>Description of study area.</p>
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<p>Methodological flowchart for GW RP derivation.</p>
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<p>Schematic of overlay operation.</p>
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<p>Derived (<b>A</b>) rainfall distribution, (<b>B</b>) slope, (<b>C</b>) geology, (<b>D</b>) drainage density, (<b>E</b>) LULC, (<b>F</b>) lineament density, (<b>G</b>) soil type map of the study area.</p>
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<p>GW potential based on the AHP map.</p>
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23 pages, 5488 KiB  
Article
Groundwater Recharge Response to Reduced Irrigation Pumping: Checkbook Irrigation and the Water Savings Payment Plan
by Justin Gibson, Trenton E. Franz, Troy Gilmore, Derek Heeren, John Gates, Steve Thomas and Christopher M. U. Neale
Water 2024, 16(20), 2910; https://doi.org/10.3390/w16202910 - 13 Oct 2024
Viewed by 393
Abstract
Ongoing investments in irrigation technologies highlight the need to accurately estimate the longevity and magnitude of water savings at the watershed level to avoid the paradox of irrigation efficiency. This paradox arises when irrigation pumping exceeds crop water demand, leading to excess water [...] Read more.
Ongoing investments in irrigation technologies highlight the need to accurately estimate the longevity and magnitude of water savings at the watershed level to avoid the paradox of irrigation efficiency. This paradox arises when irrigation pumping exceeds crop water demand, leading to excess water that is not recovered by the watershed. Comprehensive water accounting from farm to watershed scales is challenging due to spatial variability and inadequate socio-hydrological data. We hypothesize that water savings are short term, as prior studies show rapid recharge responses to surface changes. Precise estimation of these time scales and water savings can aid water managers making decisions. In this study, we examined water savings at three 65-hectare sites in Nebraska with diverse soil textures, management practices, and groundwater depths. Surface geophysics effectively identified in-field variability in soil water content and water flux. A one-dimensional model showed an average 80% agreement with chloride mass balance estimates of deep drainage. Our findings indicate that groundwater response times are short and water savings are modest (1–3 years; 50–900 mm over 10 years) following a 120 mm/year reduction in pumping. However, sandy soils with shallow groundwater show minimal potential for water savings, suggesting limited effectiveness of irrigation efficiency programs in such regions. Full article
(This article belongs to the Section Hydrology)
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Figure 1
<p>Conceptual diagram of water savings and hypothetical case study. The lag time is defined by the amount of time that elapses following a reduction in pumping but before recharge rates begin to decrease. Lag times are a function of the depth to groundwater, soil water states and fluxes, and soil hydraulic parameters. Also note that the water savings are flat after 3 years, meaning no additional benefit, and that future management decisions can reduce water savings if pumping rates return to their initial rates or if field experiences prolonged periods of dry conditions.</p>
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<p>Location of the three study sites near Brule, NE (red dot on USA). Each site is ~65 ha in area and primarily under irrigated maize production. White outlines are SSURGO soil boundaries. Field sites are S1, S3 and S4 from west to east.</p>
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<p>Results of time-repeat ECa mapping from the Dualem 21S instrument (deep signal ~0–3.2 m) and the corresponding 1st EOF reprojected spatially for each of the three 65 ha study sites (see <a href="#water-16-02910-t001" class="html-table">Table 1</a> for sample dates). Warm EOF colors indicate drier zones/coarser soil texture and cooler colors indicate wetter zones/finer soil texture compared to the field average. White lines are SSURGO soil boundaries. White dots are locations of core extraction (20 November 2017). Red dots are the location of the groundwater observation well (closest well to S1 was ~0.4 km away and not pictured here). Geophysical data layers can be found in <a href="#app1-water-16-02910" class="html-app">Files SI1–SI3</a>.</p>
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<p>Volumetric water content (VWC) and chloride (Cl<sup>−</sup>) concentration profiles of soil cores extracted from the three field sites. Line colors correspond to EOF values determined at the core location (e.g., warm colors correspond to negative EOF values, green colors correspond to near-zero EOF values, and cool colors correspond to positive EOF values; see <a href="#water-16-02910-f003" class="html-fig">Figure 3</a>). Sawtooth patterns observed in VWC and Cl- profiles align with changes in soil textures. Data from this analysis can be found in SI4.</p>
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<p>Numerical modeling results of annual deep drainage; 2012 was an exceptionally dry year with 36% of average precipitation falling for that year. Bar colors correspond to EOF values determined at the core location (e.g., warm colors correspond to negative EOF values, green colors correspond to near-zero EOF values, and cool colors correspond to positive EOF values).</p>
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<p>Volumetric water content profiles from the core analysis overlain onto numerical modeling outputs. Bands are the minimum and maximum of ranges of the simulated VWC profiles and dashed lines are the corresponding simulated mean over the 10-year simulation period. Lines with circles are from the extracted volumetric analysis from core. Line and band colors correspond to the EOF values determined at the core location (e.g., warm colors correspond to negative EOF values, green colors correspond to near-zero EOF values, and cool colors correspond to positive EOF values).</p>
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<p>Correlation between root zone depth integrated VWC for extracted cores and the corresponding simulated root zone depth-integrated VWC (10-year average). EOF values at each core location from the repeat geophysical analysis separate the relative ranges of depth integrated VWC for both the extracted cores and simulated soil profiles. Solid line is 1:1 and dashed line is best fit to data.</p>
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<p>Time series of model output determined at one core (S4C) from two paired simulations that vary only in irrigation scheduling routines. In this case, the lag time is approximately 2.5 years long (determined visually when recharge reductions begin to increase). Water savings are calculated as a cumulative reduction in pumping minus the sum of the cumulative reduction in recharge and ET.</p>
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<p>Time series of simulated water savings calculated from the paired simulations for each core. Cores with coarser soil textures (S1A, S3E, and S4A) had the largest water savings as a result of a reduction in ET.</p>
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<p>Sensitivity analysis of weather year on estimated lag times and water savings. In both panels, simulations were carried out where a continuously repeated dry year is in red, a continuously repeated wet year is in blue, and the 10-year observed weather is in green. The 10th and 90th percentile weather years were selected for this analysis.</p>
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27 pages, 1446 KiB  
Article
A Graph-Refinement Algorithm to Minimize Squared Delivery Delays Using Parcel Robots
by Fabian Gnegel, Stefan Schaudt, Uwe Clausen and Armin Fügenschuh
Mathematics 2024, 12(20), 3201; https://doi.org/10.3390/math12203201 - 12 Oct 2024
Viewed by 307
Abstract
In recent years, parcel volumes have reached record highs, prompting the logistics industry to explore innovative solutions to meet growing demand. In densely populated areas, delivery robots offer a promising alternative to traditional truck-based delivery systems. These autonomous electric robots operate on sidewalks [...] Read more.
In recent years, parcel volumes have reached record highs, prompting the logistics industry to explore innovative solutions to meet growing demand. In densely populated areas, delivery robots offer a promising alternative to traditional truck-based delivery systems. These autonomous electric robots operate on sidewalks and deliver time-sensitive goods, such as express parcels, medicine and meals. However, their limited cargo capacity and battery life require a return to a depot after each delivery. This challenge can be modeled as an electric vehicle-routing problem with soft time windows and single-unit capacity constraints. The objective is to serve all customers while minimizing the quadratic sum of delivery delays and ensuring each vehicle operates within its battery limitations. To address this problem, we propose a mixed-integer quadratic programming model and introduce an enhanced formulation using a layered graph structure. For this layered graph, we present two solution approaches based on relaxations that reduce the number of nodes and arcs compared to the expanded formulation. The first approach, Iterative Refinement, solves the current relaxation to optimality and refines the graph when the solution is infeasible for the expanded formulation. This process continues until a proven optimal solution is obtained. The second approach, Branch and Refine, integrates graph refinement into a branch-and-bound framework, eliminating the need for restarts. Computational experiments on modified Solomon instances demonstrate the effectiveness of our solution approaches, with Branch and Refine consistently outperforming Iterative Refinement across all tested parameter configurations. Full article
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<p>A minimalistic example.</p>
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<p>Arrival states at some customer <span class="html-italic">j</span>.</p>
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<p>Illustrations of two TBEGs.</p>
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<p>Illustrations of two TBEGs.</p>
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<p>Computation times for different customer numbers and 3 vehicles.</p>
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<p>Computation times for different customer numbers and 10 vehicles.</p>
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<p>Computation times for different recharging rates.</p>
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<p>Computation times for different time window widths.</p>
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10 pages, 4750 KiB  
Article
Formulating Electrolytes for 4.6 V Anode-Free Lithium Metal Batteries
by Jiaojiao Deng, Hai Lin, Liang Hu, Changzhen Zhan, Qingsong Weng, Xiaoliang Yu, Xiaoqi Sun, Qianlin Zhang, Jinhan Mo and Baohua Li
Molecules 2024, 29(20), 4831; https://doi.org/10.3390/molecules29204831 - 12 Oct 2024
Viewed by 326
Abstract
High-voltage initial anode-free lithium metal batteries (AFLMBs) promise the maximized energy densities of rechargeable lithium batteries. However, the reversibility of the high-voltage cathode and lithium metal anode is unsatisfactory in sustaining their long lifespan. In this research, a concentrated electrolyte comprising dual salts [...] Read more.
High-voltage initial anode-free lithium metal batteries (AFLMBs) promise the maximized energy densities of rechargeable lithium batteries. However, the reversibility of the high-voltage cathode and lithium metal anode is unsatisfactory in sustaining their long lifespan. In this research, a concentrated electrolyte comprising dual salts of LiTFSI and LiDFOB dissolved in mixing solvents of dimethyl carbonate (DMC) and fluoroethylene carbonate (FEC) with a LiNO3 additive was formulated to address this challenge. FEC and LiNO3 regulate the anion-rich solvation structure and help form a LiF, Li3N-rich solid electrolyte interphase (SEI) with a high lithium plating/stripping Coulombic efficiency of 98.3%. LiDFOB preferentially decomposes to effectively suppress the side reaction at the high-voltage operation of the Li-rich Li1.2Mn0.54Ni0.13Co0.13O2 cathode. Moreover, the large irreversible capacity during the initial charge/discharge cycle of the cathode provides supplementary lithium sources for cycle life extension. Owing to these merits, the as-fabricated AFLMBs can operate stably for 80 cycles even at an ultrahigh voltage of 4.6 V. This study sheds new insights on the formulation of advanced electrolytes for highly reversible high-voltage cathodes and lithium metal anodes and could facilitate the practical application of AFLMBs. Full article
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<p>Schematic illustration of electrolyte design for AFLMBs with the Li-rich Li<sub>1.2</sub>Mn<sub>0.54</sub>Ni<sub>0.13</sub>Co<sub>0.13</sub>O<sub>2</sub> cathode. The blue curve represents the charge profile of AFLMBs with a Li-rich Li<sub>1.2</sub>Mn<sub>0.54</sub>Ni<sub>0.13</sub>Co<sub>0.13</sub>O<sub>2</sub> cathode, where the LiDFOB additive undergoes preferential decomposition during the charging process. The red curve illustrates the discharge profile, during which the LiNO<sub>3</sub> additive preferentially decomposes.</p>
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<p>Galvanostatic cyclability (<b>a</b>) and charge/discharge curves (<b>b</b>) of lithium metal half cells with Li<sub>1.2</sub>Mn<sub>0.54</sub>Ni<sub>0.13</sub>Co<sub>0.13</sub>O<sub>2</sub> cathode.</p>
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<p>Raman spectra of E-LiNO<sub>3</sub>-LiDFOB, E-LiDFOB, E-LiNO<sub>3</sub> electrolytes, and FEC/DMC solvents.</p>
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<p><sup>13</sup>C (<b>a</b>) and <sup>1</sup>H (<b>b</b>) NMR spectra of E-LiNO<sub>3</sub>-LiDFOB, E-LiDFOB, and E-LiNO<sub>3</sub> electrolytes, along with FEC/DMC solvent.</p>
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<p>Cycling stability (<b>a</b>) of Li plating/stripping cycles and the corresponding charge/discharge profiles with E-LiNO<sub>3</sub>-LiDFOB (<b>b</b>), E-LiDFOB (<b>c</b>), and E-LiNO<sub>3</sub> (<b>d</b>) electrolytes.</p>
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<p>SEM images of the deposited Li metal on Cu foil in Li||Cu half-cells with E-LiNO<sub>3</sub>-LiDFOB (<b>a</b>), E-LiDFOB (<b>b</b>), and E-LiNO<sub>3</sub> (<b>c</b>) electrolytes after Li plating/stripping cycles at a current density of 0.5 mA cm<sup>−2</sup> and a capacity of 1 mAh cm<sup>−2</sup>.</p>
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<p>Galvanostatic cyclability (<b>a</b>) of high-voltage AFLMB at 0.5 C, and the corresponding charge/discharge profiles with E-LiNO<sub>3</sub>-LiDFOB (<b>b</b>), E-LiDFOB (<b>c</b>), and E-LiNO<sub>3</sub> (<b>d</b>) electrolytes.</p>
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<p>F1s (<b>a</b>) and B1s (<b>b</b>) spectra of CEI formed on Li-rich Li<sub>1.2</sub>Mn<sub>0.54</sub>Ni<sub>0.13</sub>Co<sub>0.13</sub>O<sub>2</sub> cathode with E-LiNO<sub>3</sub>-LiDFOB electrolyte.</p>
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22 pages, 4265 KiB  
Article
Groundwater Potential Zone Delineation through Analytical Hierarchy Process: Diyala River Basin, Iraq
by Ruqayah Mohammed and Miklas Scholz
Water 2024, 16(20), 2891; https://doi.org/10.3390/w16202891 - 11 Oct 2024
Viewed by 283
Abstract
Groundwater recharge zone identification is vital for managing water resources, particularly in semi-arid and dry climates. Accurate and quantifiable assessment is necessary for the sustainable management of groundwater resources, and it is possible to carry this method out using modern techniques and technical [...] Read more.
Groundwater recharge zone identification is vital for managing water resources, particularly in semi-arid and dry climates. Accurate and quantifiable assessment is necessary for the sustainable management of groundwater resources, and it is possible to carry this method out using modern techniques and technical standards. To identify likely groundwater locations in the Diyala River Catchment, Iraq, which serves as an example study basin, the current research examines a new methodology that employs a geographic information system, and an Analytical Hierarchy Process connected with remote sensing data. The technique of ArcGIS was employed to generate spatially distributed thematic layers of rainfall, lithology, slope, drainage density, land use/land cover, relief and soil. The raster data from these layers were then converted and categorized. The weights assigned to thematic strata depended on their significance relative to groundwater occurrence. A pairwise judgement matrix for the Analytical Hierarchy Process was used, with the categorized ranking, to assess the standardized weights of the layers under consideration. The layers for the formation of groundwater zones have then been placed using the overlay-weighted summation approach. Three regions, which are classed as excellent, good and moderate, have been identified on the resulting groundwater potential zones map, representing roughly 29, 69 and 2% of the basin’s total area, respectively. The study’s conclusions indicate that, in such a climate, the adopted strategy would produce favourable results to promote the organizing of opinions and the sustainable use of groundwater resources. Full article
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<p>The hydrographical system of the Diyala River Basin, which is shown on the map.</p>
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<p>The study methodology flowchart describing the incorporation between the multi-influencing aspects regarding the groundwater potential zone identifications. ASTER DEM, Advanced Spaceborne and Thermal Emission Reflection Radiometer Digital Elevation Model; LULC, Land use and land cover; UTM, Universal Transverse Mercator; and AHP, Analytical Hierarchy Process.</p>
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<p>Spatial distribution of the long-term rainfall over the Diyala River Basin.</p>
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<p>Lithology map of the Diyala River Basin. VR, basic volcanic rocks; PB, plutonic rocks; SC, carbonate sedimentary rocks; PY, pyroclastic; SU, unconsolidated sediments.</p>
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<p>The slope map of the Diyala River Basin.</p>
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<p>The drainage density map of the Diyala River Basin.</p>
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<p>The land use and land cover (LULC) map of the Diyala River Basin.</p>
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<p>The elevation map of the Diyala River Basin.</p>
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<p>The soil map of the Diyala River Basin.</p>
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<p>The groundwater potential zones map of the Diyala River Basin.</p>
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36 pages, 24832 KiB  
Article
Intelligent Swarm: Concept, Design and Validation of Self-Organized UAVs Based on Leader–Followers Paradigm for Autonomous Mission Planning
by Wilfried Yves Hamilton Adoni, Junaidh Shaik Fareedh, Sandra Lorenz, Richard Gloaguen, Yuleika Madriz, Aastha Singh and Thomas D. Kühne
Drones 2024, 8(10), 575; https://doi.org/10.3390/drones8100575 - 11 Oct 2024
Viewed by 570
Abstract
Unmanned Aerial Vehicles (UAVs), commonly known as drones, are omnipresent and have grown in popularity due to their wide potential use in many civilian sectors. Equipped with sophisticated sensors and communication devices, drones can potentially form a multi-UAV system, also called an autonomous [...] Read more.
Unmanned Aerial Vehicles (UAVs), commonly known as drones, are omnipresent and have grown in popularity due to their wide potential use in many civilian sectors. Equipped with sophisticated sensors and communication devices, drones can potentially form a multi-UAV system, also called an autonomous swarm, in which UAVs work together with little or no operator control. According to the complexity of the mission and coverage area, swarm operations require important considerations regarding the intelligence and self-organization of the UAVs. Factors including the types of drones, the communication protocol and architecture, task planning, consensus control, and many other swarm mobility considerations must be investigated. While several papers highlight the use cases for UAV swarms, there is a lack of research that addresses in depth the challenges posed by deploying an intelligent UAV swarm. Against this backdrop, we propose a computation framework of a self-organized swarm for autonomous and collaborative missions. The proposed approach is based on the Leader–Followers paradigm, which involves the distribution of ROS nodes among follower UAVs, while leaders perform supervision. Additionally, we have integrated background services that autonomously manage the complexities relating to task coordination, control policy, and failure management. In comparison with several research efforts, the proposed multi-UAV system is more autonomous and resilient since it can recover swiftly from system failure. It is also reliable and has been deployed on real UAVs for outdoor survey missions. This validates the applicability of the theoretical underpinnings of the proposed swarming concept. Experimental tests carried out as part of an area coverage mission with 6 quadcopters (2 leaders and 4 followers) reveal that the proposed swarming concept is very promising and inspiring for aerial vehicle technology. Compared with the conventional planning approach, the results are highly satisfactory, highlighting a significant gain in terms of flight time, and enabling missions to be achieved rapidly while optimizing energy consumption. This gives the advantage of exploring large areas without having to make frequent downtime to recharge and/or charge the batteries. This manuscript has the potential to be extremely useful for future research into the application of unmanned swarms for autonomous missions. Full article
(This article belongs to the Special Issue Distributed Control, Optimization, and Game of UAV Swarm Systems)
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<p>Proposed swarming workflow. It consists of three main stages: (1) space partitioning stage, (2) mission planning stage, and (3) communication and consensus control stage.</p>
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<p>Autonomous UAV-based model-reflex agent.</p>
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<p>Illustration of the shape-based partition of <span class="html-italic">A</span> for <span class="html-italic">k</span>-drone swarm [<a href="#B11-drones-08-00575" class="html-bibr">11</a>].</p>
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<p>Swarm configuration for high-level mission parallelism.</p>
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<p>Communication topology: (<b>a</b>) Synchronous communication. (<b>b</b>) Asynchronous communication.</p>
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<p>Architecture design of our multi-UAV system.</p>
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<p>Service thread pool management of the swarm. Two threads are allocated for each service. Follower-to-follower services are performed in a synchronous manner. While leader-to-leader services are asynchronous.</p>
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<p>Communication model of the swarm based on single-group architecture.</p>
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<p>Message passing interface for swarm communication via MAVLink.</p>
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<p>Execution workflow of the swarm services based on leader–followers hierarchy.</p>
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<p>State transition diagram of the standby leader UAV. The standby UAV passes to active mode when one of the three events is detected.</p>
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<p>Cooperative execution workflow of leader UAVs for fault-tolerance policy management.</p>
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<p>Illustration of task failure management for the followers <math display="inline"><semantics> <msub> <mi>UAV</mi> <mn>2</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>UAV</mi> <mn>3</mn> </msub> </semantics></math>. The SwarmManager reschedules the failed jobs on <math display="inline"><semantics> <msub> <mi>UAV</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>UAV</mi> <mn>4</mn> </msub> </semantics></math> according to the FIFO and priority order queue.</p>
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<p>UAV Swarm used for the experimental tests: (<b>a</b>) Real swarm used for reliability and deployment testing. It consists of two homogeneous quadrotors and based on ardupilot architecture. (<b>b</b>) virtual swarm used for the simulation. It consists of two leaders and four follower UAVs.</p>
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<p>LMAT coverage operation with the 2-UAV swarm in outdoor environment. The green UAV operates on the left side while the black one on the right.</p>
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<p>Swarming version of the coverage mission with a 4-UAV swarm. The mission is performed simultaneously across the four follower UAVs.</p>
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<p>Time complexity <math display="inline"><semantics> <mrow> <mi>O</mi> <mo>(</mo> <mi>M</mi> <mo>)</mo> </mrow> </semantics></math> of the LMAT coverage algorithm with varying number of follower UAVs <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mrow> <mo>⟦</mo> <mn>1</mn> <mo>.</mo> <mo>.</mo> <mn>4</mn> <mo>⟧</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>Swarm energy consumption from small to large areas (<math display="inline"><semantics> <mrow> <mo>|</mo> <mi>A</mi> <mo>|</mo> <mo>∈</mo> <mo>{</mo> <mn>0.4</mn> <mo>;</mo> <mn>0.8</mn> <mo>;</mo> <mn>1.2</mn> <mo>}</mo> </mrow> </semantics></math>). Small area is covered by (<b>a</b>), while (<b>b</b>,<b>c</b>) show the results for large areas.</p>
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<p>Network bandwidth (pkts) of the swarm based on the number of UAV <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mrow> <mo>⟦</mo> <mn>1</mn> <mo>.</mo> <mo>.</mo> <mn>4</mn> <mo>⟧</mo> </mrow> </mrow> </semantics></math>.</p>
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<p>ROS communication graph of the six-UAV swarm. Each block represents a communication subgraph of each UAV. The nodes and arcs within each block represent the services/topics and their interactions.</p>
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18 pages, 8713 KiB  
Article
Hydrogeochemical Characteristics and Sulfate Source of Groundwater in Sangu Spring Basin, China
by Zhanxue Bai, Xinwei Hou, Xiangquan Li, Zhenxing Wang, Chunchao Zhang, Chunlei Gui and Xuefeng Zuo
Water 2024, 16(20), 2884; https://doi.org/10.3390/w16202884 - 11 Oct 2024
Viewed by 287
Abstract
The Sangu Spring Basin is located in an important economic area, and groundwater is the main source of water for local life and industry. Understanding the sources of chemical components in groundwater is important for the development and utilization of groundwater. In this [...] Read more.
The Sangu Spring Basin is located in an important economic area, and groundwater is the main source of water for local life and industry. Understanding the sources of chemical components in groundwater is important for the development and utilization of groundwater. In this paper, we analyzed the origin of the chemical components of groundwater and their evolution in the Sangu Spring Basin using statistical analysis, Piper diagrams, Gibbs diagrams, ion ratios, and combined hydrochemistry–isotope analyses. The results show that the groundwater in the Sangu Spring Basin is mainly derived from atmospheric precipitation, that the groundwater in stagnant and confined environment zones was formed under colder climatic conditions, and that the surface water (SW) has a close hydraulic relation with the groundwater. Water–rock interaction is the main factor controlling the composition of groundwater. The compositions of groundwater are mainly derived from carbonate weathering, silicate weathering, and dissolution of gypsum. Na+ and K+ in groundwater mainly come from the dissolution of albite and potassium feldspar, rather than rock salt. Ion exchange occurs in karst groundwater (KGW) and fissure groundwater (FGW), and ion exchange is dominated by the exchange of Mg2+ and Ca2+ in the groundwater with Na+ and K+ in the rock or soil. Sulfate in groundwater is derived from dissolution of gypsum, infiltration of atmospheric precipitation, and leakage of SW. Groundwaters with the highest sulfate content are located in the vicinity of SW, as a result of receiving recharge from SW seepage. Groundwaters with higher sulfate contents are located in the stagnant and deeply buried zones, where sulfate is mainly derived from the dissolution of gypsum. SW seepage recharges groundwater, resulting in increased levels of Cl, NO3 and SO42− in groundwater. These insights can provide assistance in the protection and effective management of groundwater. Full article
(This article belongs to the Section Hydrogeology)
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<p>Exposed strata and distribution of sampling points in the Sangu Spring Basin. Note: In the legend, Karst groundwater, Surface water, Pore groundwater and Fissure groundwater all represent sampling locations.</p>
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<p>Hydrogeological cross-section (A,B in <a href="#water-16-02884-f001" class="html-fig">Figure 1</a>) of the study area.</p>
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<p>Piper diagram of groundwater in the Sangu Spring Basin.</p>
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<p>δD −H<sub>2</sub>O versus δ<sup>18</sup>O − H<sub>2</sub>O plot of groundwater in the Sangu Spring Basin.</p>
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<p>Gibbs diagrams representing controlling factors of groundwater quality, expressed in mg⋅L<sup>−1</sup> (<b>a</b>) TDS vs. (Na/(Na + Ca)) and (<b>b</b>) TDS vs. (Cl/(Cl + HCO<sub>3</sub>)).</p>
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<p>Normalized bivariate diagrams for (<b>a</b>) Mg<sup>2+</sup>/Na<sup>+</sup> vs. Ca<sup>2+</sup>/Na<sup>+</sup> and (<b>b</b>) HCO<sub>3</sub><sup>−</sup>/Na<sup>+</sup> vs. Ca<sup>2+</sup>/Na<sup>+</sup>.</p>
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<p>Plots of (Ca<sup>2+</sup> + Mg<sup>2+</sup>) vs. (HCO<sub>3</sub><sup>−</sup> + SO<sub>4</sub><sup>2−</sup> + CO<sub>3</sub><sup>2−</sup> + NO<sub>3</sub><sup>−</sup>) (<b>a</b>), (Ca<sup>2+</sup> + Mg<sup>2+</sup> + Na<sup>+</sup> + K<sup>+</sup> − Cl<sup>−</sup>) vs. (HCO<sub>3</sub><sup>−</sup> + SO<sub>4</sub><sup>2−</sup> + CO<sub>3</sub><sup>2−</sup> + NO<sub>3</sub><sup>−</sup>) (<b>b</b>), Ca<sup>2+</sup> vs. SO<sub>4</sub><sup>2−</sup> (<b>c</b>), (Na<sup>+</sup> + K<sup>+</sup>) vs. Cl<sup>−</sup> (<b>d</b>), Mg<sup>2+</sup> vs. SO<sub>4</sub><sup>2−</sup> (<b>e</b>) and Ca<sup>2+</sup> vs. Mg<sup>2+</sup> (<b>f</b>).</p>
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<p>Relationship plots of CAI1 vs. CAI2 (<b>a</b>) and ((Ca<sup>2+</sup>+Mg<sup>2+</sup>) − (HCO<sub>3</sub><sup>−</sup> + SO<sub>4</sub><sup>2−</sup> + CO<sub>3</sub><sup>2−</sup> + NO<sub>3</sub><sup>−</sup>)) vs. (K<sup>+</sup> + Na<sup>+</sup> − Cl) (<b>b</b>), expressed in meq L<sup>−1</sup>.</p>
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<p>Plots of δ<sup>18</sup>O − SO<sub>4</sub><sup>2−</sup> (‰) vs. δ<sup>18</sup>O − H<sub>2</sub>O (‰) (<b>a</b>), δ<sup>34</sup>S − SO<sub>4</sub><sup>2−</sup> (‰) vs. SO<sub>4</sub><sup>2−</sup> (mg/L) (<b>b</b>), δ<sup>34</sup>S<sub>SO4</sub> (‰) vs. <sup>14</sup>C (PMC) (<b>c</b>), and δ<sup>34</sup>S − SO<sub>4</sub><sup>2−</sup> (‰) vs. δ<sup>18</sup>O − SO<sub>4</sub><sup>2−</sup> (‰) (<b>d</b>).</p>
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27 pages, 4249 KiB  
Article
A Management Framework and Optimization Scheduling for Electric Vehicles Participating in a Regional Power Grid Demand Response under Battery Swapping Mode
by Xiaolong Yang, Ruoyun Du, Zhengsen Ji, Qian Wang, Meiyu Qu and Weiyao Gao
Electronics 2024, 13(20), 3987; https://doi.org/10.3390/electronics13203987 - 10 Oct 2024
Viewed by 415
Abstract
With the rapid development of new energy vehicle industry and battery technology, in addition to charging mode to supplement energy mode for electric vehicles, battery swapping mode is also about to become an important way for electric vehicles to recharge power. Therefore, in [...] Read more.
With the rapid development of new energy vehicle industry and battery technology, in addition to charging mode to supplement energy mode for electric vehicles, battery swapping mode is also about to become an important way for electric vehicles to recharge power. Therefore, in this context, this paper plans the demand response management framework of electric vehicles participating in the regional power grid under the battery swapping mode from the first time. On this basis, the time distribution of battery-swapping demand was proposed by the time series analysis model of different vehicle types of electric vehicles. Then, in order to reduce the peak-valley load difference in the regional power grid as the optimization management goal, the charging schedule optimization scheduling model of electric vehicles participating in the demand response of the regional power grid under the battery swapping mode was constructed. The case analysis shows that under the battery swapping mode, by participating in the demand response through the optimal management and scheduling of the charging load of the power battery, can help the grid balance the contradiction between supply and demand in the peak and valley and promote the full consumption of new energy. Full article
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<p>The number of new energy vehicles and pure electric vehicles in China increased from 2016 to 2020. (Data source: China Automobile Industry Association, <a href="http://www.caam.org.cn/tjsj" target="_blank">http://www.caam.org.cn/tjsj</a>, accessed on 3 September 2024).</p>
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<p>Schematic diagram of electric vehicle participation in demand response management in power-changing mode.</p>
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<p>Analysis diagram of the electric bus status conversion scenario.</p>
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<p>Timing analysis diagram of bus state conversion and power-changing demand.</p>
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<p>Schematic diagram of power-changing mode of point-to-point distribution of mobile electric changing energy vehicle.</p>
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<p>Flow chart of charging load optimization solution in power-changing mode.</p>
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<p>Disorderly charging load of the bus power battery.</p>
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<p>Electric taxi power battery disorderly charging load situation.</p>
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<p>Disorderly charging load of electric private car power battery.</p>
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<p>Comparison of power battery charging load and regional power grid load in disordered charging mode.</p>
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<p>Optimize the comparison of power battery charging load and regional power grid load.</p>
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<p>Sensitivity analysis of battery configuration to demand response ability.</p>
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19 pages, 5109 KiB  
Article
Urban Air Logistics with Unmanned Aerial Vehicles (UAVs): Double-Chromosome Genetic Task Scheduling with Safe Route Planning
by Marco Rinaldi, Stefano Primatesta, Martin Bugaj, Ján Rostáš and Giorgio Guglieri
Smart Cities 2024, 7(5), 2842-2860; https://doi.org/10.3390/smartcities7050110 - 6 Oct 2024
Viewed by 849
Abstract
In an efficient aerial package delivery scenario carried out by multiple Unmanned Aerial Vehicles (UAVs), a task allocation problem has to be formulated and solved in order to select the most suitable assignment for each delivery task. This paper presents the development methodology [...] Read more.
In an efficient aerial package delivery scenario carried out by multiple Unmanned Aerial Vehicles (UAVs), a task allocation problem has to be formulated and solved in order to select the most suitable assignment for each delivery task. This paper presents the development methodology of an evolutionary-based optimization framework designed to tackle a specific formulation of a Drone Delivery Problem (DDP) with charging hubs. The proposed evolutionary-based optimization framework is based on a double-chromosome task encoding logic. The goal of the algorithm is to find optimal (and feasible) UAV task assignments such that (i) the tasks’ due dates are met, (ii) an energy consumption model is minimized, (iii) re-charge tasks are allocated to ensure service persistency, (iv) risk-aware flyable paths are included in the paradigm. Hard and soft constraints are defined such that the optimizer can also tackle very demanding instances of the DDP, such as tens of package delivery tasks with random temporal deadlines. Simulation results show how the algorithm’s development methodology influences the capability of the UAVs to be assigned to different tasks with different temporal constraints. Monte Carlo simulations corroborate the results for two different realistic scenarios in the city of Turin, Italy. Full article
(This article belongs to the Special Issue Smart Urban Air Mobility)
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<p>Snapshot of a simplified example in a portion of the city of Turin (Italy), with three delivery tasks, one charge hub, and a fleet of four UAVs.</p>
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<p>Example of PMC operator for creation of offspring <math display="inline"><semantics> <mrow> <mi>I</mi> </mrow> </semantics></math> from <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>I</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>I</mi> <mi>I</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Example of slide mutation.</p>
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<p>Example of flip mutation.</p>
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<p>Example of swap mutation.</p>
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<p>(<b>a</b>) Graph-based representation of the final schedule related to the solution of Algorithm 1 with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>m</mi> </mrow> <mrow> <mi>d</mi> <mi>w</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> with a simple instance of the DDP. (<b>b</b>) Evolution of the fitness function <math display="inline"><semantics> <mrow> <mi>J</mi> </mrow> </semantics></math> at each iteration of Algorithm 1 with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>m</mi> </mrow> <mrow> <mi>d</mi> <mi>w</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
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<p>Risk maps of the operational area of <a href="#smartcities-07-00110-f001" class="html-fig">Figure 1</a> computed after taking into account UAV A and UAV C, the latter both without and with a payload. The black line is the minimum risk path computed with the risk-aware path planning. The dashed black line is the minimum distance path.</p>
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21 pages, 5954 KiB  
Article
Evaluation of Groundwater Resources in the Middle and Lower Reaches of Songhua River Based on SWAT Model
by Xiao Yang, Changlei Dai, Gengwei Liu, Xiang Meng and Chunyue Li
Water 2024, 16(19), 2839; https://doi.org/10.3390/w16192839 - 6 Oct 2024
Viewed by 713
Abstract
The SWAT model primarily investigates sources of water pollution and conducts ecological assessments of surface water in contemporary hydrology and water resources research. To date, there have been limited accomplishments in the study of groundwater resources in China. The MODFLOW model currently primarily [...] Read more.
The SWAT model primarily investigates sources of water pollution and conducts ecological assessments of surface water in contemporary hydrology and water resources research. To date, there have been limited accomplishments in the study of groundwater resources in China. The MODFLOW model currently primarily simulates groundwater levels and the migration of water quality, depending on the hydrological surface water data in the relevant area. This study aims to investigate the groundwater distribution characteristics of the middle and lower reaches of the Songhua River, a significant agricultural and grain production region in China. The research focuses on the middle and lower reaches of the Songhua River basin in Northeast China and employed the SWAT distributed hydrological model to simulate runoff. The monthly recorded runoff at Tongjiang Station in Jiamusi City was utilized to calibrate the model parameters. Consequently, the MODFLOW model was introduced to compare and assess the simulation outcomes of the SWAT model, ultimately ascertaining the distribution characteristics of shallow groundwater, groundwater recharge, recoverable volume, and groundwater levels in the Songhua River Basin. The findings indicate that: (1) The SWAT model demonstrates efficacy in the study region, achieving R2 and NS values of 0.81 and 0.76, respectively, thereby fulfilling the fundamental criteria for scientific research. The MODFLOW model exhibits strong performance in the study region, achieving a periodic R2 of 0.98 and a verification R2 of 0.97, with the discrepancy between simulated and actual groundwater levels confined to 0.6 m, thereby satisfying the criteria for scientific research. (2) In 2011, 2014, and 2016, the groundwater recharge in the middle and lower sections of the Songhua River was 24.33 × 108 m3, 30.79 × 108 m3, and 32.25 × 108 m3, respectively, aligning closely with the SWAT simulation results, while the average annual groundwater level depth was 8.17 m. (3) In the research area, groundwater recharging occurs primarily by atmospheric precipitation, while drainage predominantly transpires via groundwater as base flow, constituting 81.46%. Secondly, the recharge of shallow groundwater to deep aquifers is around 7.14%, with a minimal share attributed to vadose zone loss, constituting merely 2.1%. (4) From 2010 to 2016, the average groundwater runoff modulus of the middle and lower reaches of the Songhua River basin was 1.005 L/(s·km²), with a total recharge of 216.58 × 108 m3 and a total recoverable amount of 105.11 × 108 m3. The mean yearly supply was 25.11 × 108 m3. The total groundwater recharge was 26.54 × 108 m3 in the driest year (2011) and 33.25 × 108 m3 in the year of most ample water (2016). Full article
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<p>A comprehensive diagram of the study region. (<b>a</b>) represents the subwatershed zoning map created using the SWAT model (ArcSWAT2012). (<b>b</b>) displays a geographic elevation map of the study region. (<b>c</b>,<b>d</b>) illustrate the distribution of soil types and land use, respectively, in the study area. The details of (<b>c</b>) can be found in <a href="#water-16-02839-t001" class="html-table">Table 1</a>, while the details of (<b>d</b>) can be found in Table 5).</p>
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<p>Schematic diagram of SWAT model.</p>
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<p>Study area parameter partition map. (The Roman numerals in the figure are the partition number of the permeability coefficient).</p>
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<p>Schematic diagram of top and bottom elevation points in the study area.</p>
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<p>Discrete graphic of model space.</p>
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<p>Preliminary water level chart.</p>
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<p>Determination and verification of the runoff rate of the model (the longitudinal coordinate indicates the runoff unit: m<sup>3</sup>).</p>
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<p>Evaluation of all recorded well simulation outcomes during the calibration and validation phases.</p>
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<p>Comparison of simulated and actual water levels in a single well throughout calibration and validation intervals.(The serial number in the picture represents the observation well number assigned to each observation well in order to facilitate the experiment.)</p>
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<p>Annual mean water storage of phreatic beds in the sub-basin from 2010 to 2016 (unit: ×10<sup>8</sup> m<sup>3</sup>).</p>
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<p>Variation map of water storage in a shallow aquifer in a sub-watershed. (<b>a</b>) shows the geographical distribution characteristics of the average annual water storage in the shallow aquifer, while (<b>b</b>) illustrates a schematic diagram of the changes in average annual water storage in the shallow aquifer. Unit: 10<sup>8</sup> m<sup>3</sup>).</p>
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<p>Distribution trend of precipitation in the middle and lower reaches of Songhua River from 2008 to 2016 (Unit: mm).</p>
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<p>Trend chart depicting groundwater recharge from 2008 to 2016.</p>
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<p>Diagram of the characteristic annual mean water table.</p>
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17 pages, 10349 KiB  
Article
Experimental Study on Water and Salt Migration and the Aggregate Insulating Effect in Coarse-Grained Saline Soil Subgrade under Freeze–Thaw Cycles
by Haoyuan Yang, Bingbing Lei, Liangfu Xie, Changtao Hu and Jie Liu
Appl. Sci. 2024, 14(19), 8970; https://doi.org/10.3390/app14198970 - 5 Oct 2024
Viewed by 448
Abstract
Understanding multiphase transformations and the migration of heat, water, vapor, and salt in coarse-grained saline soil under groundwater recharge and environmental freeze—thaw cycles is crucial for ensuring the stability of highway infrastructures. To clarify the water, heat, vapor, and salt migration patterns in [...] Read more.
Understanding multiphase transformations and the migration of heat, water, vapor, and salt in coarse-grained saline soil under groundwater recharge and environmental freeze—thaw cycles is crucial for ensuring the stability of highway infrastructures. To clarify the water, heat, vapor, and salt migration patterns in coarse-grained saline soil, as well as the salt-insulating effect of the aggregate insulating layer, an experimental study was conducted in a soil column model under pressureless water replenishment with fluorescein-labeled liquid water under freeze—thaw cycles. The results showed that the temperature in the saline soil columns periodically changed and that hysteresis effects occurred during temperature transfer. External water replenishment and the content of liquid water inside the soil exhibited nonlinear changes with environmental temperatures. After multiple freeze—thaw cycles, two water and salt accumulation zones formed within the coarse-grained saline soil subgrade. The migration of liquid water resulted in a water and salt accumulation zone in the nonfrozen zone, whereas the migration of water vapor yielded a water and salt accumulation zone in the frozen zone. To prevent water and salt migration, a 20 cm thick gravel insulating layer could be laid at a distance of 10 cm from the bottom of the roadbed, which could provide a satisfactory salt-insulating effect. The research results provide a theoretical basis and guidance for regulating the stability of subgrades in saline soil areas. Full article
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<p>Multiphase compositions of frozen saline soil.</p>
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<p>Particle size accumulation curve of the experimental saline soil.</p>
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<p>Diagram of the experimental liquid and vapor coupling migration device.</p>
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<p>Tracer-labeled liquid water migration and visualization validation.</p>
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<p>CS655 sensor water content calibration function.</p>
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<p>CS655 sensor salt content calibration function.</p>
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<p>Temperature variation curves of saline soil at the different heights under different numbers of freeze–thaw cycles.</p>
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<p>Isothermal distribution in saline soil under freeze—thaw cycles.</p>
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<p>Variation curve of external water replenishment with freeze—thaw cycle duration.</p>
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<p>Fluorescein liquid level height change.</p>
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<p>5 Variation curves of the water and salt contents along the height of the saline soil column under freeze—thaw cycles.</p>
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<p>Migration height of fluorescein-labeled liquid water.</p>
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<p>Variation curves of the liquid water content and temperature with respect to the freeze—thaw time.</p>
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<p>Location and thickness of the aggregate insulating layer.</p>
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<p>Actual liquid level height of water.</p>
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<p>Variation curves of the water and salt contents along the height of the saline soil column after the installation of an aggregate insulating layer.</p>
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<p>Schematic diagram of road structure layer.</p>
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20 pages, 4810 KiB  
Article
Understanding Spatio-Temporal Hydrological Dynamics Using SWAT: A Case Study in the Pativilca Basin
by Yenica Pachac-Huerta, Waldo Lavado-Casimiro, Melania Zapana and Robinson Peña
Hydrology 2024, 11(10), 165; https://doi.org/10.3390/hydrology11100165 - 4 Oct 2024
Viewed by 580
Abstract
This study investigates the hydrological dynamics of the Pativilca Basin in the Southern Hemisphere using the SWAT (Soil and Water Assessment Tool) model. Seventy-seven watersheds across a mountainous region were analyzed using elevation data, land cover, soil type, and gridded meteorological products (RAIN4PE [...] Read more.
This study investigates the hydrological dynamics of the Pativilca Basin in the Southern Hemisphere using the SWAT (Soil and Water Assessment Tool) model. Seventy-seven watersheds across a mountainous region were analyzed using elevation data, land cover, soil type, and gridded meteorological products (RAIN4PE and PISCO) for hydrological simulations. Watershed delineation, aided by a Digital Elevation Model, enabled the identification of critical drainage points and the definition of Hydrological Response Units (HRUs). The model calibration and validation, performed using the SWAT-CUP with the SUFI-2 algorithm, achieved Nash–Sutcliffe Efficiency (NSE) values of 0.69 and 0.72, respectively. Cluster analysis categorized the watersheds into six distinct groups with unique hydrological and climatic characteristics. The results showed significant spatial variability in the precipitation and temperature, with pronounced seasonality influencing the daily flow patterns. The higher-altitude watersheds exhibited greater soil water storage and more effective aquifer recharge, whereas the lower-altitude watersheds, despite receiving less precipitation, displayed higher flows due to runoff from the upstream areas. These findings emphasize the importance of incorporating seasonality and spatial variability into water resource planning in mountainous regions and demonstrate the SWAT model’s effectiveness in predicting hydrological responses in the Pativilca Basin, laying the groundwork for future research in mountain hydrology. Full article
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<p>Geographical map of the Pativilca River Basin (<b>a</b>) study area in Peru; (<b>b</b>) study area in Ancash and Lima regions; (<b>c</b>) study area with elevation and rivers in the basin.</p>
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<p>Spatial distribution of slope, land cover, and type soil in the Pativilca Basin. (<b>a</b>) Shows how the slope changes, with steeper areas mostly up in the upper part of the basin; (<b>b</b>) maps out the land cover, including vegetation, farms, and urban spots; and (<b>c</b>) highlights the soil types, showing how they affect water retention and erosion throughout the basin.</p>
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<p>Methodological flowchart.</p>
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<p>Cluster dendrogram for the regionalization of catchments in the Pativilca Basin. The dendrogram delineates six distinct catchment groups (A–F), represented by color-coded branches. Each group’s representative catchment is highlighted in pink. The vertical axis reflects the degree of dissimilarity between the catchments, with greater heights indicating higher dissimilarity. This regionalization was achieved using hierarchical clustering based on Euclidean distances, facilitating the identification of hydrologically similar catchment groups for further analysis.</p>
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<p>Regionalization of watersheds in the Pativilca Basin and selection of representative watersheds.</p>
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<p>Seasonal variations in precipitation, maximum, and minimum temperatures in the Pativilca Basin regions. The first column (blue bars) represents monthly precipitation, while red bars indicate maximum temperatures and orange bars depict minimum temperatures. The groups are arranged vertically from top to bottom, starting with Group A at the uppermost position and concluding with Group F at the lowest. These graphs highlight the temporal distribution and variability in key climatic variables across different seasons, enabling the assessment of seasonal trends and their impact on hydrological processes in the basin.</p>
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<p>Calibration and validation at the Cahua hydrometric station.</p>
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<p>Spatial distribution of hydrological components in the Pativilca Basin. The hydrological components include (<b>a</b>) flow out daily mean (<span class="html-italic">Q</span>) and annual precipitation (<span class="html-italic">R<sub>d</sub></span>), (<b>b</b>) evapotranspiration (ET), (<b>c</b>) percolation (<span class="html-italic">W<sub>seep</sub></span>), (<b>d</b>) groundwater contribution to streamflow (<span class="html-italic">Q<sub>gw</sub></span>), (<b>e</b>) average daily soil water storage (SW), and (<b>f</b>) water yield (<span class="html-italic">W<sub>YLD</sub></span>). Each map illustrates the spatial variability across the basin, highlighting the hydrological dynamics. The representative watersheds are bordered in red, indicating their respective groups at the center. Group boundaries are depicted with black dotted lines, enhancing the differentiation between zones. These visual elements allow for a detailed analysis of the distribution and influence of key hydrological processes across the basin’s distinct regions.</p>
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<p>Temporal distribution of streamflow in the Pativilca Basin. Daily streamflows from 1981 to 2015 show a clear seasonal pattern, with peak flows during the wet season (January to March) and lows in the dry season (June to September). Flow variation is driven by altitude, storage capacity, and watershed connectivity, with lower watersheds redistributing water from upstream areas.</p>
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<p>Temporal distribution of streamflow in the Pativilca Basin. Daily streamflows from 1981 to 2015 show a clear seasonal pattern, with peak flows during the wet season (January to March) and lows in the dry season (June to September). Flow variation is driven by altitude, storage capacity, and watershed connectivity, with lower watersheds redistributing water from upstream areas.</p>
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22 pages, 3043 KiB  
Article
Investigating the Future of Freight Transport Low Carbon Technologies Market Acceptance across Different Regions
by Mohamed Ali Saafi, Victor Gordillo, Omar Alharbi and Madeleine Mitschler
Energies 2024, 17(19), 4925; https://doi.org/10.3390/en17194925 - 1 Oct 2024
Viewed by 685
Abstract
Fighting climate change has become a major task worldwide. One of the key energy sectors to emit greenhouse gases is transportation. Therefore, long term strategies all over the world have been set up to reduce on-road combustion emissions. In this context, the road [...] Read more.
Fighting climate change has become a major task worldwide. One of the key energy sectors to emit greenhouse gases is transportation. Therefore, long term strategies all over the world have been set up to reduce on-road combustion emissions. In this context, the road freight sector faces significant challenges in decarbonization, driven by its limited availability of low-emission fuels and commercialized zero-emission vehicles compared with its high energy demand. In this work, we develop the Mobility and Energy Transportation Analysis (META) Model, a python-based optimization model to quantify the impact of transportation projected policies on freight transport by projecting conventional and alternative fuel technologies market acceptance as well as greenhouse gas (GHG) emissions. Along with introducing e-fuels as an alternative refueling option for conventional vehicles, META investigates the market opportunities of Mobile Carbon Capture (MCC) until 2050. To accurately assess this technology, a techno-economic analysis is essential to compare MCC abatement cost to alternative decarbonization technologies such as electric trucks. The novelty of this work comes from the detailed cost categories taken into consideration in the analysis, including intangible costs associated with heavy-duty technologies, such as recharging/refueling time, cargo capacity limitations, and consumer acceptance towards emerging technologies across different regions. Based on the study results, the competitive total cost of ownership (TCO) and marginal abatement cost (MAC) values of MCC make it an economically promising alternative option to decarbonize the freight transport sector. Both in the KSA and EU, MCC options could reach greater than 50% market shares of all ICE vehicle sales, equivalent to a combined 35% of all new sales shares by 2035. Full article
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<p>Incremental capex for MCC options.</p>
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<p>Total cost of ownership over 10 years for heavy-duty vehicles, 2030–2050, in the EU.</p>
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<p>Total cost of ownership over 10 years for heavy-duty vehicles, 2030–2050, in the KSA.</p>
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<p>Marginal abatement cost for alternative heavy-duty vehicle technologies.</p>
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<p>Marginal abatement cost sensitivity analysis for 50% and 90% capture rates.</p>
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<p>Projected market shares in the KSA, 2024–2050.</p>
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<p>Projected market shares in the EU, 2024–2050.</p>
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<p>Carbon capture market shares in the KSA and EU, 2025–2050.</p>
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<p>Fleet stocks in the KSA, in thousands, 2024–2050.</p>
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<p>Fleet stocks in the EU, in thousands, 2024–2050.</p>
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<p>Freight transport GHG emissions (MtCO<sub>2</sub>/year) for in the KSA, 2024–2050.</p>
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<p>Freight transport GHG emissions (MtCO<sub>2</sub>/year) for in the EU, 2024–2050.</p>
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<p>Fast charging possibility scenarios, 2024–2050.</p>
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<p>Impact of fast charging possibility on BET market shares in the KSA and EU, 2024–2050.</p>
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<p>E-diesel blending penetration scenarios.</p>
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<p>Impact of e-diesel blending penetration on ICE market shares in the KSA and EU, 2024–2050.</p>
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27 pages, 16826 KiB  
Article
Groundwater Quality and Potential Health Risk Assessment for Potable Use
by Pawan Kumar, Gagan Matta, Amit Kumar and Gaurav Pant
World 2024, 5(4), 805-831; https://doi.org/10.3390/world5040042 - 30 Sep 2024
Viewed by 511
Abstract
The Ramganga River basin, comprising three rivers, the Dhela, Dhandi, and Ramganga, plays a vital role in groundwater recharge, sustaining numerous industries, urban areas, and rural communities reliant on these rivers for daily activities. The study’s primary purpose was to analyze the groundwater [...] Read more.
The Ramganga River basin, comprising three rivers, the Dhela, Dhandi, and Ramganga, plays a vital role in groundwater recharge, sustaining numerous industries, urban areas, and rural communities reliant on these rivers for daily activities. The study’s primary purpose was to analyze the groundwater quality in the context of potability, irrigation, and health risks to the local inhabitants of the Ramganga River basin. In 2021–2022, 52 samples (26 × 2) were collected from 13 locations in two different seasons, i.e., pre-monsoon and post-monsoon, and 20 physico-chemical and heavy metal and metalloids were analyzed using the standard protocols. The result shows that heavy metal and metalloids and metalloid concentrations of Zn (0.309–1.787 and 0.613–1.633); Fe (0.290–0.965 and 0.253–1.720), Cd (0.001–0.002 and 0.001–0.002); As (0.001–0.002 and 0.001–0.002), Cr (0.009–0.027 and 0.011–0.029), and Pb (−0.001–0.010 and 0.00–0.010) values in mg/L are present in both seasons. The groundwater quality index (GWQI), heavy metal pollution Index (HPI), and heavy metal evaluation index (HEI) were used to assess the water quality and metal pollution in the basin area. As per GWQI values, water quality lies from excellent water quality (41.639 and 43.091) to good water quality (56.326 and 53.902); as per HPI values, it shows good (29.51 and 30.03) to poor quality (60.26 and 59.75) and HEI values show the low-level contamination (1.03–2.57 and 1.13–3.37) of heavy metal and metalloids in both seasons. According to the potential health risk assessment, infants show low risk in pre-monsoon and low risk to medium post-monsoon, while children and adults show low risk to high risk in both seasons. From the health risk perspective, it shows that children and adults have more concerns about non-carcinogenic effects, so adequate remedial measures and treatment are required to avoid the groundwater quality of the Ramganga River basin. Full article
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<p>Map showing the selected sampling location in the Ramganga River basin.</p>
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<p>IDW Map showing the pH variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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<p>IDW Map showing the TDS variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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<p>IDW Map showing the SO<sub>4</sub><sup>2−</sup> variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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<p>IDW Map showing the NO<sub>3</sub><sup>−</sup> variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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<p>IDW Map showing the Zn variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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<p>IDW Map showing the Fe variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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<p>IDW Map showing the As variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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<p>IDW Map showing the Cd variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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<p>IDW Map showing the Cr variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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<p>IDW Map showing the Pb variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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<p>IDW Map showing the GWQI variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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<p>IDW Map showing the HPI variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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<p>IDW Map showing the HEI variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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<p>IDW Map showing the SAR variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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<p>IDW Map showing the % Na variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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<p>IDW Map showing the HI for Infant variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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<p>IDW Map showing the HI for Child variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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<p>IDW Map showing the HI for Adult variation in pre-monsoon and post-monsoon in selected sampling locations of the Ramganga River basin.</p>
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