Characterizing seasonal, environmental and human-induced factors influencing the dynamics of Rispana River's water quality: Implications for sustainable river management

Results in Engineering, Год журнала: 2024, Номер 22, С. 102007 - 102007

Опубликована: Март 19, 2024

This study will comprehensively evaluate the Rispana River's aquatic environment from February to June 2021, integrating physicochemical, biological, and hydrological data. Key objectives include identifying pollution sources, assessing climate change impacts, evaluating health effects on humans life. Analysis of 50 water samples reveals significant pH variations across five sampling sites influenced by natural human factors. Fluctuating temperatures (16.41 °C–20.81 °C) impact various parameters, including dissolved oxygen (DO), total solids (TDS), biochemical demand (BOD). Microbiological assessment detects coliforms, indicating contamination with potential risks. The highlights degradation River due change. Urgent action is needed mitigate impacts safeguard health. Sustained monitoring strategic management approaches are crucial for river's sustainability. Collaborative efforts necessary address sources restore health, emphasizing importance protecting this vital resource current future generations.

Язык: Английский

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Assessment of irrigational suitability of groundwater in Thanjavur district, Southern India using Mamdani fuzzy inference system

Results in Engineering, Год журнала: 2024, Номер 21, С. 101789 - 101789

Опубликована: Янв. 23, 2024

Groundwater is an important source for the agricultural needs in Thanjavur district, Tamil Nadu State, India. This study focused on assessing groundwater suitableness irrigation by employing Mamdani fuzzy inference system (MFIS). The quality data of premonsoon (PRM) and postmonsoon (POM) seasons were used this purpose. Based mean value, dominant cations sequence sodium > magnesium calcium potassium anions order was bicarbonate chloride sulphate nitrate. Gibbs diagram showed that major ionic concentration primarily influenced rock-water interaction evaporation process. To determine suitability purposes, adsorption ratio, electrical conductivity, percentage, residual carbonate, ratio Kelly's estimated. Then a developed with three input one output variable. categorized sample as "good" (PRM-36 %, POM-44 %) "good to permissible" (PRM-7%, POM-2%), "permissible" (PRM-24 POM-23 %), "permissible poor" (PRM-5%, POM-4%) "poor" (PRM-28 POM-27 %). On comparing conventional water classification method, model found be more accurate than method classifying suitability. will assist decision-makers develop strategies long-term management resources area.

Язык: Английский

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Analysing Heavy Metal Contamination in Groundwater in the Vicinity of Mumbai’s Landfill Sites: An In-depth Study

Topics in Catalysis, Год журнала: 2024, Номер 67(15-16), С. 1009 - 1023

Опубликована: Апрель 21, 2024

Язык: Английский

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Unraveling Two-Dimensional Modeling of Multispecies Reactive Transport in Porous Media with Variable Dispersivity

Groundwater for Sustainable Development, Год журнала: 2025, Номер 28, С. 101404 - 101404

Опубликована: Янв. 7, 2025

Язык: Английский

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Application of machine learning techniques to predict groundwater quality in the Nabogo Basin, Northern Ghana

Heliyon, Год журнала: 2024, Номер 10(7), С. e28527 - e28527

Опубликована: Март 30, 2024

The main objective of this study was to map the quality groundwater for domestic use in Nabogo Basin, a sub-catchment White Volta Basin Ghana, by applying machine learning techniques. conducted Random Forest (RF) algorithm predict quality, utilizing factors that influence occurrence and such as Elevation, Topographical Wetness Index (TWI), Slope length (LS), Lithology, Soil type, Normalize Different Vegetation (NDVI), Rainfall, Aspect, Slope, Plan Curvature (PLC), Profile (PRC), Lineament density, Distance faults, Drainage density. area predicted building model based on computed Arithmetic Water Quality Indices (WQI) (as dependent variable) existing boreholes, serve an indicator quality. WQI shows it ranges from 9.51 69.99%. This implied 21.97 %, 74.40 3.63 % had respectively likelihood excellent. models were found perform much better with RMSE 23.03 R2 value 0.82. highlighted essential understanding area, paving way further studies policy development management.

Язык: Английский

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Evaluation of surface water quality in Brahmani River Basin, Odisha (India), for drinking purposes using GIS-based WQIs, multivariate statistical techniques and semi-variogram models

Innovative Infrastructure Solutions, Год журнала: 2024, Номер 9(12)

Опубликована: Ноя. 23, 2024

Язык: Английский

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Comprehensive analysis of multiple classifiers for enhanced river water quality monitoring with explainable AI

Case Studies in Chemical and Environmental Engineering, Год журнала: 2024, Номер 10, С. 100822 - 100822

Опубликована: Июнь 27, 2024

Monitoring river water quality is crucial for safeguarding public health, protecting ecosystems, and ensuring economic sustainability. It helps detect contaminants, ensures drinking safety, facilitates early intervention environmental protection legal compliance. The objective of this study to evaluate multiple machine learning algorithms analyze parameters in computing index (WQI) classification thereof, aiming devise a reliable method forecasting with high accuracy. In study, fourteen classifiers applied include Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Multilayer Perceptron (MLP), K-Nearest Neighbor (KNN), Naïve Bayes, Gradient boosting, AdaBoost, Bagging, Extra Trees, Quadratic Discriminant Analysis (QDA), XGBoost, CATBoost. A total 1096 sample data was used where each consists nineteen analytical parameters. To assess the performance various classifiers, several evaluation techniques were utilized including confusion matrices, reports detailing precision accuracy ratios, Receiver Operating Characteristic (ROC) curves. also utilizes explainable AI (LIME SHAP) provide clear insights into decision-making processes classify quality. results indicated that all ML models demonstrate satisfactory predicting WQI. Among used, Boosting achieves highest Accuracy (99.64 %), Precision (0.95), Recall (0.96), F1-Score indicating its superior ability correctly instances suggesting balanced across different metrics. analysis presented article holds promise providing accurate researchers, thereby enhancing monitoring effectiveness through application techniques.

Язык: Английский

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Traditional Dual Tank Water Management: A Study of Kolakanatham in Perambalur District, Tamil Nadu

Current World Environment, Год журнала: 2025, Номер 19(3), С. 1526 - 1546

Опубликована: Янв. 10, 2025

Societies make rainwater harvesting structures to collect and store rain water cater for their immediate future needs. In a remote village in the Perambalur district, people use dual temple tank, nearly 350 years old, drinking purposes. This study tries understand traditional knowledge embedded pond structures, cascade aerators, an open well connected with ground aquifer inside tank. delves into projected rainfall trends using NASA POWER data 2050, revealing noticeable increase assessment of quality parameters demonstrating adherence acceptable limits set by Bureau Indian Standards (BIS). However, despite meeting these standards, index deems unsuitable direct human consumption, though it remains suitable domestic household use. Moreover, analysis land cover changes Sentinel2A over past 22 underscores significant growth settlement areas, indicative rising demand resources. The proposes restoration utilization tank system as potential solution address this burgeoning demand, offering reliable source preserving practices within local community.

Язык: Английский

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Correction: Patel et al. Application of the Weighted Arithmetic Water Quality Index in Assessing Groundwater Quality: A Case Study of the South Gujarat Region. Water 2023, 15, 3512

Water, Год журнала: 2025, Номер 17(4), С. 583 - 583

Опубликована: Фев. 18, 2025

There was an error in the original publication [...]

Язык: Английский

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Assessment of surface water quality parameters using multivariate analysis—A case study of Kurichi and big lakes in Coimbatore

Water Environment Research, Год журнала: 2025, Номер 97(3)

Опубликована: Март 1, 2025

Abstract Water quality deterioration due to industrialization and urbanization is a growing environmental concern, particularly in developing regions. This study assesses the surface water of Kurichi Big Lakes Ukkadam area, Coimbatore, India, using multivariate statistical techniques identify key pollution sources evaluate contamination levels. Despite prior research on urban lakes, limited studies have systematically analyzed multiple contaminants advanced approaches. A total 12 samples were collected between June 2023–June 2024 for physicochemical, microbiological, anionic parameters. Principal Component Analysis (PCA) Factor (FA) revealed three dominant components explaining 68.42% 42.81% variance Lakes, respectively. The Piper plot classified types, while Cluster (CA) grouped sampling sites based Pearson correlation matrix determined pollutant interdependencies, Quality Index (WQI) categorized severity against WHO BIS standards. results indicate that organic matter, industrial discharge, fertilizer runoff, untreated wastewater are primary contributors pollution. High levels detected near zones, with Lake exhibiting significantly poorer than Lake. findings highlight urgent need improved management control policies safeguard aquatic ecosystems public health. Practitioner Points Multivariate Statistical Analysis: Applied PCA, FA, plot, CA, assess quality. Classification: Identified Findings: Three major explained variation Major Pollution Sources: analysis identified compounds, human activity, fertilizers, chemical waste, discharge as contaminants. Industrial Area Impact: CA WQI highlighted high sensitivity zones

Язык: Английский

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