Groundwater for Sustainable Development最新文献

This study assesses the vulnerability of aquifers to increased contamination, influenced by rapid peri-urbanization and deficient sanitary infrastructure. The research focuses on the Taza aquifer in northeastern Morocco, where we applied the DRASTIC and GOD models. We further enhanced the DRASTIC method by the addition of a Fracture Index (F) ‘’DRASTICF‘’ to incorporate the effects of tectonics and geological discontinuities on groundwater behavior. The vulnerability mapping was performed for two distinct dry and wet periods of 2016 and 2021 respectively. Moreover, the use of hydrogeochemical analysis of 22 groundwater samples helped to identify potential sources of pollution and to understand the hydrogeological process. The results indicate a dynamic in vulnerability between these two periods due to changes in the recharge parameter and groundwater depth. The comparison between obtained vulnerability maps and nitrate concentrations reveals vulnerable zones that closely resemble regions affected by nitrate pollution. This area is predominantly classified as of high vulnerability at 27 %, associated with an estimated pollution level of 36%. The findings underscore also the importance of integrating the fracture index into the assessment of aquifer vulnerability alongside the different models. This integration offers an enriched perspective for effectively managing risks associated with groundwater contamination. The risk maps of groundwater contamination were also established by overlapping the vulnerability map with the land use layer. The main hydrogeochemical facies determined in the Piper Diagram allowed classifying the waters as Ca–Mg–HCO3 type. The significant correlations indicated that groundwater mineralization in the study area originates from the dissolution of limestone and evaporated formations. The water quality within the study area shows substantial variability, ranging from excellent to unsuitable, primarily due to the leaching of fertilizers and wastewater.

The different results can be very helpful to the farmers, and local authorities for better groundwater sustainable management.

To achieve sustainable development goals (SDGs), drinking water and/or wastewater treatment must be performed at a minimum cost along with negligible environmental impacts. Traditional approaches, like coagulation, precipitation, ion exchange, and membrane filtration have numerous drawbacks in terms of cost and effectiveness. Recently, the thermochemical conversion of biomasses/lignocellulosic wastes for biochar production and subsequently their application in the remediation of contaminated matrices is gaining attention. Further, the application of machine learning (ML) and artificial intelligence (AI) to optimize the production and application of biochar is a topical topic. Therefore, this review critically explains the optimised production process of biochar and its application in the removal of a diverse range of organic and inorganic contaminants from contaminated water and wastewater. Moreover, the review highlights the progress in organic and inorganic pollutants remediation with biochar, focusing on the significance and benefits of utilizing ML and AI to optimize adsorption variables and biochar feedstock properties. The surface area, porosity, and functional groups of the biochar, the type and quantity of the pollutants and the solution's pH, temperature, and ionic strength, all influence the adsorption capacity of the biochar. Furthermore, the duration of the biochar's interaction with the contaminants and the existence of competing ions are significant factors. Utilizing AI and ML proves to be efficient in terms of cost and time, enabling a multidisciplinary approach to eliminate pollutants using biochar. Finally, this review discusses the challenges associated with the application of ML and AI in the treatment of contaminated water and wastewater using biochar and proposed future prospects to make these technologies economically viable and sustainable.

The present study aims to predict the concentration of uranium (U) in groundwater in India by utilizing water quantity indicators, such as Standardised Precipitation Index (SPI), Standardised Precipitation Evapotranspiration Index (SPEI), and Gravity Recovery and Climate Experiment (GRACE) based groundwater levels. The study adopts a multi-scale approach, ranging from state-level to agroclimatic zones. The findings indicate that the highest levels of U (101–500 μg.L⁻¹), which surpass the World Health Organization (WHO) prescribed limit of 30 μg.L⁻¹ for drinking water, are primarily concentrated in India's plateau and hills regions. The GRACE data map portrays a downward trend in groundwater levels throughout India, with the mid-Gangetic plains experiencing the most significant decline. The meteorological aspect of the study, as indicated by SPI and SPEI, reveals that the plateau and hills region is experiencing a decline in rainfall. The SPEI further underscores the grim picture of decreasing precipitation in northern India. Additionally, the study employs cluster analysis to cluster states according to the division of agro-climatic zones. Lastly, the study employs a random forest algorithm to assess the relative importance of each predictor and predict U concentration under the trinity of precipitation, extraction, and evaporation. The most significant contribution of this work is to identify the hotspots in India that require the most attention in terms of U toxicity owing to groundwater decline. Overall, this study highlights the need for immediate attention to mitigate the adverse impacts of U contamination and aims at sensitizing the stakeholders towards the compelling need to fulfil SDG-3 (health aspects due to U hazard) and SDG-6 (groundwater over-exploitation and deteriorating water quality).

Unregulated mining drives considerable long-term soil and water pollution with increased health risks to humans and other organisms. The main objective of this study is to assess the water quality, physicochemical parameters, heavy metal content, and health risks of borehole water from selected mining communities in Ghana. Quality parameters of 56 borehole water samples collected from 19 mining communities in the Amansie West District, Ghana were carried out. The study is one of the few studies to be carried out in the district because of its breadth of communities, depth of analysis, and scale of health risk assessments performed. Physicochemical parameters including pH, conductivity, turbidity, total dissolved solids, and color were evaluated using standard and appropriate methods. Additionally, arsenic, cadmium, lead, and mercury were evaluated using an atomic absorption spectrometer. Observed mean physicochemical parameters varied from 5.1 to 6.5 pH, 59.3 to 325 μS/cm conductivity, 1.4 to 86.4 NTU turbidity, 32.5–214 mg/L total dissolved solids, and 2.5–250 Hz color. Also, the mean concentration of the heavy metals varied from 1.6 ± 2.24 to 169.14 ± 2.18 μg/L arsenic, 0.6 ± 2.24 to 6.0 ± 0.71 μg/L cadmium, 1.25 ± 2.00 to 15.60 ± 1.11 μg/L lead and 0.025 ± 2.00 to 3.33 ± 0.17 μg/L mercury. There was no statistically significant link between arsenic, cadmium, lead, and mercury at 0.05 concentrations predicting source diversities. Though some samples met WHO quality standards, others were unsafe with increased health risks due to color, turbidity, acidity, and high arsenic issues. In conclusion, this study observed an increased health risk in some selected sampled communities due to exposure to mining-influenced contaminations from heavy metals and particulate matter. This implies that without urgent mining waste disposal regulations, scarcity of quality water and increased risk to health issues could be imminent.

Due to ultra-fast technological improvement and rapid urbanization nowadays, industries have generated a huge amount of wastewater that is discharged directly into the environment. Resulting in harsh damage to ecology and public safety/security by contaminating the ground/surface water sources. Therefore, it is very crucial to purify this wastewater before discharging/reusing. This study will be devoted to the latest enhancements along with the new route of production of the multifunctional Crystalline Nanocellulose-Modified Bituminous Coal (CNC-MC) bionanocomposites. Besides these, the significant implementation of the fabricated CNC-MC bionanosorbents for the simultaneous removal of Ni²⁺ and Congo red (CR) from bulk-scale industrial wastewater has been stated. Conversely, influential parameters like inlet concentration (25–45 ppm), flow rate (3–5 mL/min), and adsorbent bed height (0.2–0.8 cm) were explored. The bionanosorbents were characterized by using some state-of-the-art equipment such as FTIR-ATR, XRD, BET, FESEM, and TGA analysis, while the water samples were analyzed by AAS and UV-NIR techniques. Results suggested that the fabricated CNC-MC bionanocomposites possessed a considerable amount of active binding sites/functional groups, showed high thermal stability up to 700°C, and had a high crystallinity index (around 92.41 ± 0.009%). They revealed a noteworthy honeycomb-like mesoporous microstructure with a significant specific surface area. Due to these outstanding features, this multifunctional bionanosorbents has exhibited sensational adsorption performance. Meanwhile, the maximum removal efficiency and percentage were observed at approximately 328.7 and 478.3 mg/g, as well as around 67.95 and 76.65% for Ni²⁺ and CR. The experimental data was evaluated by three well-known column models, namely the Thomas, Adam-Bohart, and Yoon-Nelson models, and respectable agreement was found. That is similarly appropriate for the justification of the experimental breakthrough curve by supporting the Langmuir isotherm and 2nd-order reversible reaction kinetics. Hence, this novel CNC-MC bionanosorbent could be beneficially used to purify industrial wastewater in an economical and eco-friendly way for sustainable environmental safety.

Groundwater is linked to climate change, land use, and land cover changes around the world. This research aims to enhance understanding of hydrological dynamics and environmental changes in the region for sustainable water resource management and conservation. It employs the Soil and Water Assessment Tool (SWAT) model and the standard Groundwater Level Index to determine the hydrological components, groundwater level, and fluctuations of the catchments over time. The model has been rerun with Global Climate Model (GCM) derived scenarios for predicting seasonal hydrological components. To assess changes in the land use and land cover around Kivu Lake, CA-Markov and regression analysis were also applied. Different calibration and validation schemes, as well as hydrological component patterns, were implemented. Across all catchments, streamflow ranged from 40.89 m³s⁻¹–170.45 m³s⁻¹, indicating a good fit between observed and simulated data. At Sake's northernmost point, a catchment's maximum average annual evapotranspiration was 1480.89 mm, with the same amount of precipitation, and the groundwater percolation rate ranged from 103.8 to 318.3 mmyr⁻¹. According to the seasonal hydrological component trends, Shared Socioeconomic Pathway (SSP)119 predicted discharge ranging from 3.5% to 13.6% and SSP545 predicted discharge ranging from 1.8% to 10.83% for all catchments. Furthermore, under SSP119 and SSP545, average ET will range from 3.3% to 13.69% and from 4.33% to 15.63%, respectively. The 88.36% of the catchment from South Kivu in Kalehe district has shifted from forest to agriculture. This has the potential to dramatically alter percolation in the region. The findings of this research will assist decision-makers and resource managers in Kivu Lake catchments in making informed decisions regarding water allocation, land management practices, and conservation efforts.

Fresh water supply in arid land is facing many challenges that require proper management of water resources, especially in drinking water. Management of water projects depends on the conditions of each country or region. Bajestan is located in Razavi Khorasan province, Iran. The certain resource of drinking water supply is desalinated of high salinity groundwater from Playa. Government has implemented the project “Separation of Drinking and sanitary water networks by groundwater desalination” since of 2014, by creating “Government Drinking Water Withdrawal Smart Stations (GDWWSS)" in this city. This study evaluates the feasibility of different methods of separation drinking water from sanitary water by considering sustainability dimensions these methods. These methods including of Home Water Desalination Machines, Mobile Packaged Water, GDWWSS (with new design), Private Drinking Water Withdrawal Stations (PDWWS) and Dual Drinking Water Distribution Network (DDWDN). Findings showed, that if just the economic dimension is considered in the drinking water separation project, PDWWS would be the best method but since the social dimension is an integral part of a sustainable project, the most compatible method is DDWDN. The environmental evaluation showed that production of salt water and salt (NaCl) is the most common adverse effect of all the proposed methods, but the DDWDN will produce more salt wastewater. On the other hand, Mobile packaged water method will produce 17,000 tons of PET annually, which will cause biological problems if it is not recycled. Therefore, the best way is to use the water resources of the plain and not use the desalination of salt groundwater resources. Also, to increase people's satisfaction and reduce water consumption with good quality water, DDWDN is suggested.

Securing reliable water resources in arid and semi-arid regions poses a significant challenge for decision-makers, especially under recent droughts and projected climate change. This need is particularly acute in the M'zi Wadi basin, located in the north-central part of Algeria, where its surface water projects are challenged by low and erratic rainfall coupled with high water evaporation, adversely impacting its water resources sustainability. Implementing water harvesting techniques in such arid regions, specifically groundwater dams, can be regarded as an effective water scarcity adaptation strategy, and their effectiveness is dependent on the optimal site selection which must consider many complex criteria. The paper aims to identify potential sites for groundwater dams in the M'zi Wadi basin using a multi-criteria decision-making approach in a GIS environment based on seven socio-economic and natural factors such as rainfall, stream order, land use/land cover (LULC), geology, Topographic wetness index (TWI), and distances from faults and villages. The results showed that approximately 1.27% of the M'zi Wadi basin area is highly suitable for groundwater dam siting. Four candidate sites were identified as having favourable conditions for installing new groundwater dams based on field and geophysical survey data conducted along the alluvial aquifer of the M'zi Wadi basin, and the remaining areas were deemed unsuitable due to specific constraints. The suitability of the selected sites was validated through a comparative analysis with existing groundwater dams. The results of the present study could offer valuable insights for decision-makers at all levels, facilitating informed judgments during the initial planning of groundwater dam construction. Furthermore, the methodology can be replicated in other data-scarce regions under similar arid climate conditions.

The widespread use of hazardous chemicals in residential and agricultural activities has given rise to a significant global concern regarding pesticide pollution. Present research highlighted the adsorption of carbaryl (CBRL), a carbamate-type insecticide, using zinc oxide nanoparticles (ZnONPs) synthesized from Helianthus annuus flower petals. The study involved synthesizing ZnONPs through a sol-gel process using flower petal extracts, followed by characterization of the nanoparticles. The ZnONPs were then applied for adsorption of CBRL, with mechanisms explored through isotherms, kinetics, thermodynamics insights into CBRL-ZnONPs interactions. The Box–Behnken Design (BBD) of the Response Surface Methodology (RSM) was conducted to optimize the settings for CBRL removal during the batch method adsorption studies. Additionally, molecular dynamic simulation, and density functional theory (DFT) techniques provided deeper insights into the adsorption mechanism. The adsorption process is most precisely fitted linearly as Freundlich isotherm (R² = 0.9934). By using the Langmuir isotherm, the adsorption demonstrated a noteworthy CBRL adsorption capacity of 158.34 mg/g. The kinetic analysis indicated that the process followed the pseudo-second-order model (R² = 0.9978). The thermodynamic parameters suggested an endothermic (ΔH^o = 36.279 kJ/mol), entropy-driven, and non-spontaneous adsorption process. Nevertheless, the RSM optimization reveals that 99.983% of the CBRL was removed. Furthermore, throughout the process of adsorbent stability via regeneration, ethanol appears to be the most effective eluting agent, exhibiting the 58.33% desorption efficiency. The DFT analyzes combined visual and energetic insight into the molecular dynamics of the CBRL-ZnONPs system, revealed complex interactions and higher binding energies in complex-I. With the support of nanoparticle-based adsorbent, this research presents an in-depth overview of the adsorption of CBRL by ZnONPs, and important insights for creating efficient pollution mitigation strategies.

The efficient and sustainable use of water has become a necessity in regions prone to drought and water scarcity. One such region is the Fars province of Iran, where farmers often face uncertainties in irrigation water supply due to frequent droughts and declining groundwater levels. This study employed a quantitative research methodology, utilizing surveys and questionnaires to collect data. Specifically, the study used the choice experiment (CE) methodology to evaluate policy attributes aimed at guaranteeing agricultural water supply. The research was conducted in Marvdasht County within the Fars province, with a sample size of 170 farmers and 4080 observations collected in 2015. The collected data were analyzed using the conditional logit (CL) model. The sample size was determined using the stratified random sampling method. The results of the study indicate that age has a negative effect on farmers' willingness to pay (WTP) for guaranteed water supply, while education has a positive effect. Additionally, the study found that farmers' WTP for different policies varied, with the highest WTP observed for the use of water-saving technologies (estimated at 254.89 IRR per m3) across all areas. Consequently, the study recommends that policies promoting the adoption of water-saving technologies should be prioritized globally. It is worth noting that water policies can significantly differ between countries and regions due to various factors, including local water challenges, legal frameworks, cultural norms, and socio-economic conditions. Therefore, when formulating water policies, it is crucial to consider the specific context and tailor them to the unique circumstances of each region or country.