Simulation of ground water quality for noyyal river basin of Coimbatore city, Tamilnadu using MODFLOW

Migration characteristics and simulation prediction of high ammonia nitrogen groundwater pollution in landfills in Southwest China

A total of 31 groundwater samples were obtained and analyzed in this research endeavor from a conventional rural landfill situated in the red-layer region of northern Sichuan province, China. The concentrations of NH4-N in groundwater varied from 0.025 to 17.3 mg/L, with 51.61% of samples surpassing the limit of 0.5 mg/L established by the World Health Organization for drinking water. The groundwater chemistry in the studied area was primarily affected by cation exchange, human activities, and the weathering of carbonate rocks, according to the Gibbs plot, ionic ratio analysis, and SI calculations. According to the calculated weighted water quality index (EWQI), the majority of the groundwater quality indicators in the study area were classified as poor or very poor, with NH4-N concentration being the primary determinant. Numerical simulation results showed that the diffusion area of the NH4-N pollution plume in the horizontal plane along the direction of groundwater flow was 5618 m2, 10,142 m2, and 11,695 m2 for 1, 5, and 10 years of waste leachate leakage, respectively. In conclusion, the findings of this research offer a scientific basis for the remediation of groundwater attributable to the landfill situated in the red-layer region of northern Sichuan, China.

Improving groundwater quality predictions in semi-arid regions using ensemble learning models

Groundwater resources constitute one of the primary sources of freshwater in semi-arid and arid climates. Monitoring the groundwater quality is an essential component of environmental management. In this study, a comprehensive comparison was conducted to analyze the performance of nine ensembles and regular machine learning (ML) methods in predicting two water quality parameters including total dissolved solids (TDS) and pH, in an area with semi-arid climate conditions. The study area under consideration is an aquifer located in the Sirjan plain, Kerman, Iran. The developed models include standard multilayer perceptron neural network (MLPNN), classification and regression trees (CART), Chi-square automatic interaction detection (CHAID), and their ensemble versions in bagging (BG) and boosting (BT) ensemble structures. The analysis revealed that standard MLs yield comparable results in predicting TDS. The MLPNN, exhibiting a standard root mean square error (SRMSE) of 0.085, demonstrated superior accuracy in predicting TDS when contrasted with CART and CHAID models. Predicting pH poses a greater challenge for the models. Ensemble techniques significantly enhanced the accuracy of regular models. On average, the bagging and boosting techniques resulted in a 22.68% improvement in the accuracy of regular models, which represents a statistically significant enhancement. The boosting method, with an average SRMSE of 0.0602, is more accurate than bagging. Based on the results, the CHAID-BT with SRMSE of 0.0790 and CHAID-BG with SRMSE of 0.0330 are ranked the most accurate models for predicting TDS and pH, respectively. The performance of ensemble techniques in predicting TDS is more remarkable. In practical implementation, ensemble techniques can be considered an alternative method with high accuracy for sustainable water resources management in semi-arid regions, helping to address water shortages, climate change, water pollution, etc.

Integrated assessment for groundwater quality and flood vulnerability in coal mining regions

Coal mining activities greatly damage water resources, explicitly concerning water quality. The adverse effects of coal mining and potential routes for contaminants to migrate, either through surface water or infiltration, into the groundwater table. Dealing with pollution from coal mining operations is a significant surface water contamination concern. Consequently, surface water resources get contaminated, harming nearby agricultural areas, drinking water sources, and aquatic habitats. Moreover, the percolation process connected with coal mining could alter groundwater quality. Subsurface water sources can get contaminated by toxins generated during mining activities that infiltrate the soil and reach the groundwater table. The aims of this study are the creation of models and the provision of proposals for corrective measures. Twenty-five scenarios were simulated using MODFLOW; according to the percolation percentage and contamination, 35% of the study area, i.e., the middle of the research area, was the most affected. About 38.08% of the area around the mining zones surrounding Margherita is prone to floods. Agricultural areas, known for applying chemical fertilizers, are particularly vulnerable, generating a risk of pollution to surrounding water bodies during flooding. The outputs of this research contribute to identifying and assessing flood-vulnerable regions, enabling focused measures for flood risk reduction, and strengthening water resource management.

Design optimization of groundwater circulation well based on numerical simulation and machine learning

The optimal design of groundwater circulation wells (GCWs) is challenging. The key to purifying groundwater using this technique is its proficiency and productivity. However, traditional numerical simulation methods are limited by long modeling times, random optimization schemes, and optimization results that are not comprehensive. To address these issues, this study introduced an innovative approach for the optimal design of a GCW using machine learning methods. The FloPy package was used to create and implement the MODFLOW and MODPATH models. Subsequently, the formulated models were employed to calculate the characteristic indicators of the effectiveness of the GCW operation, including the radius of influence (R) and the ratio of particle recovery (Pr). A detailed collection of 3000 datasets, including measures of operational efficiency and key elements in machine learning, was meticulously compiled into documents through model execution. The optimization models were trained and evaluated using multiple linear regression (MLR), artificial neural networks (ANN), and support vector machines (SVM). The models produced by the three approaches exhibited notable correlations between anticipated outcomes and datasets. For the optimal design of circulating well parameters, machine learning methods not only improve the optimization speed, but also expand the scope of parameter optimization. Consequently, these models were applied to optimize the configuration of the GCW at a site in Xi'an. The optimal scheme for R (Q = 293.17 m3/d, a = 6.09 m, L = 7.28 m) and optimal scheme for Pr (Q = 300 m3/d, a = 3.64 m, L = 1 m) were obtained. The combination of numerical simulations and machine learning is an effective tool for optimizing and predicting the GCW remediation effect.

Study on groundwater pollution and its human impact analysis using geospatial techniques in semi-urban of south India

Groundwater recharging and thus renewable groundwater supplies will experience considerable changes due to climate change. The present investigation focussed to evaluate the susceptibility of subsurface water in rapid developing textile industrial region of Southern India. To determine the aquifer's susceptibility, the DRASTIC-LU Remote sensing and GIS based vulnerability model was applied and the results were compared with human activity risk (HAR) analysis to ensure the water borne disease on human health. DRASTIC layers like Depth to Water, Recharge, Aquifer Media, Soil Media, Topography, Impact of Vadose Zone, and Conductivity of the Aquifer are overlay along with respective LULC layers (2010 & 2020) to generate the DRASTIC-LU models for 2010 and 2020. The results of the DRASTIC LU groundwater vulnerability pattern between the range of 25-201 and 55 to 212 in 2010 and 2020, and have been classified into five categories from very high to low susceptibility. The effects of various Drastic-LU parameters were 69%, 76%, 81%, 82%, 63%, 79%, and 84%, respectively, based on the output of the sensitivity analysis method. The areas of Northern, Central, Central East and Central Western portions were identified as maximum and very highly vulnerable on the DRASTIC-LU index map and rest of the locations were classed as low and medium in terms of vulnerability. The outcomes of a risk analysis of human activity revealed that areas with high human activity density, such as of Northern, Central, Central East and Central Western portions were shown to be a vulnerable between the years 2010 and 2020, according to the results of human activity risk analysis.

An integrated approach for an impact assessment of the tank water and groundwater quality in Coimbatore region of South India: implication from anthropogenic activities

Groundwater in the Coimbatore region is very much essential for irrigation and human beings' day-to-day life activities. This research focuses on the impact of anthropogenic activities on the surface water quality of four tanks in Coimbatore city and its influences on disturbing the groundwater quality as well. Water quality is degrading as a result of encroachment and other anthropogenic activities. The disposal of municipal garbage and waste from other enterprises into the nearby tank degrades the quality of the water in the tanks. This pollutant-concentrated surface water, together with the leech organism, may percolate via the pore spaces between soil particles, where it will interact with groundwater. The quality of groundwater will be impacted as a result of this interaction. As a result, water quality metrics for both surface and groundwater have been assessed in this study, and spatial interpolation was performed using ArcGIS. The map of the spatial distribution of water quality was produced using ArcGIS. This spatially interpolated water quality map helped researchers understand how the quality of surface and groundwater changed over time. Visual MODFLOW and MT3D were used to simulate the groundwater flow. The MODFLOW program is used to simulate the direction of groundwater flow based on groundwater level and rainfall data. The output of MT3D allowed for accurate calculation of the pollutant movement's amount and direction. In this study, chloride was used as the pollutant transport parameter. A semi-structured interview has been done in the study region to find out more about the availability of drinking water, how waste is disposed of, and how diseases are spread among residents who live close to the tanks.