Assessment of dumpsites leachate, geotechnical properties of the soil, and their impacts on surface and groundwater quality of Sunyani, Ghana DOI Creative Commons
Daniel Gyabaah, Esi Awuah, Richard Amankwah Kuffour

и другие.

Environmental Advances, Год журнала: 2024, Номер 16, С. 100548 - 100548

Опубликована: Май 22, 2024

Leachate from sanitary landfill and dumpsites have potential to cause soil groundwater contamination, disrupt ecosystems. However, there is little information about dumpsite leachates geotechnical properties their impacts on surface in Bono region, Ghana. This study assessed leachate, of the soil, quality Sunyani. Six triplicate leachates, eleven three water samples were taken at (up-stream, mid-stream down-stream) urban dumpsite, eight around peri-urban dumpsite. Water stored (<4°C) polyethylene bottle for laboratory analysis. Physicochemical leachate analyzed using standard methods. Heavy metals (Cd, Hg, Pb, Zn, Cr, Fe, Cu, Ni) determined atomic absorption spectroscopy. index (WQI) pollution loads (LPL) investigated, while plasticity (Ip) liquid (Il) Atterberg limit test. The results showed that all heavily polluted with mean concentrations chlorides ranged between (2830±220 63810±340), biochemical oxygen demand (358±36 820±80), ammonium ions (82.8±2.2 267.6±62), cadmium (0.49±0.02 5.32±1.0); (LPI) LPL exceeding disposal standards (LDS>100). WQI 6.51 289, indicating excellent sources. Surface was 0.84 776.75, majority having WQI>100. located 75-155 m away Moderate significant negative association established (WQI-P WQI-H) distance multiple R2=0.4709, p=0.0191 R2= 0.4482, p=0.02425 respectively. Statistically, strong & Ip (p < 0.05). implies when sources increased, values decreased, improved.

Язык: Английский

Toxicological risk assessment using spring water quality indices in plateaus of Giresun Province/Türkiye: a holistic hydrogeochemical data analysis DOI Creative Commons
S. Karadeniz, Fikret Ustaoğlu, Handan Aydın

и другие.

Environmental Geochemistry and Health, Год журнала: 2024, Номер 46(8)

Опубликована: Июль 5, 2024

Abstract Water scarcity is a growing concern due to rapid urbanization and population growth. This study assesses spring water quality at 20 stations in Giresun province, Türkiye, focusing on potentially toxic elements physicochemical parameters. The Quality Index rated most samples as "excellent" during the rainy season "good" dry season, except 4 (40° 35′ 12″ North/38° 26′ 34″ East) 19 44′ 28″ 06′ 53″ West), indicating "poor" quality. Mean macro-element concentrations (mg/L) were: Ca (34.27), Na (10.36), Mg (8.26), K (1.48). trace element values (μg/L) Al (1093), Zn (110.54), Fe (67.45), Mn (23.03), Cu (9.79), As (3.75), Ni (3.00), Cr (2.84), Pb (2.70), Co (1.93), Cd (0.76). Health risk assessments showed minimal non-carcinogenic risks, while carcinogenic from arsenic slightly exceeded safe limits (CR = 1.75E−04). Higher were increased recharge, arsenic-laden surface runoff, human activities. Statistical analyses (PCA, PCC, HCA) suggested that metals physico-chemical parameters originated lithogenic, anthropogenic, or mixed sources. Regular monitoring of recommended mitigate potential public health risks waterborne contaminants.

Язык: Английский

Процитировано

35

Advancing Water Quality Assessment and Prediction Using Machine Learning Models, Coupled with Explainable Artificial Intelligence (XAI) Techniques Like Shapley Additive Explanations (SHAP) For Interpreting the Black-Box Nature DOI Creative Commons
Randika K. Makumbura, Lakindu Mampitiya, Namal Rathnayake

и другие.

Results in Engineering, Год журнала: 2024, Номер 23, С. 102831 - 102831

Опубликована: Сен. 1, 2024

Water quality assessment and prediction play crucial roles in ensuring the sustainability safety of freshwater resources. This study aims to enhance water by integrating advanced machine learning models with XAI techniques. Traditional methods, such as index, often require extensive data collection laboratory analysis, making them resource-intensive. The weighted arithmetic index is employed alongside models, specifically RF, LightGBM, XGBoost, predict quality. models' performance was evaluated using metrics MAE, RMSE, R2, R. results demonstrated high predictive accuracy, XGBoost showing best (R2 = 0.992, R 0.996, MAE 0.825, RMSE 1.381). Additionally, SHAP were used interpret model's predictions, revealing that COD BOD are most influential factors determining quality, while electrical conductivity, chloride, nitrate had minimal impact. High dissolved oxygen levels associated lower indicative excellent pH consistently influenced predictions. findings suggest proposed approach offers a reliable interpretable method for prediction, which can significantly benefit specialists decision-makers.

Язык: Английский

Процитировано

22

Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review DOI
Johnson C. Agbasi, Johnbosco C. Egbueri

Environmental Science and Pollution Research, Год журнала: 2024, Номер 31(21), С. 30370 - 30398

Опубликована: Апрель 20, 2024

Язык: Английский

Процитировано

21

Ecological Security Pattern based on XGBoost-MCR model: A case study of the Three Gorges Reservoir Region DOI
Deliang Sun, Xiaoqing Wu, Haijia Wen

и другие.

Journal of Cleaner Production, Год журнала: 2024, Номер 470, С. 143252 - 143252

Опубликована: Июль 26, 2024

Язык: Английский

Процитировано

15

Integrating machine learning models for optimizing ecosystem health assessments through prediction of nitrate–N concentrations in the lower stretch of Ganga River, India DOI
Basanta Kumar Das,

Soumitra Paul,

Biswajit Mandal

и другие.

Environmental Science and Pollution Research, Год журнала: 2025, Номер unknown

Опубликована: Янв. 30, 2025

Язык: Английский

Процитировано

1

Nitrate sources and transformation in surface water and groundwater in Huazhou District, Shaanxi, China: integrated research using hydrochemistry, isotopes and MixSIAR model DOI
Lingxi Li, Peiyue Li,

Yan Tian

и другие.

Environmental Research, Год журнала: 2024, Номер 263, С. 120052 - 120052

Опубликована: Сен. 23, 2024

Язык: Английский

Процитировано

6

Assessment of Surface Water Quality Using Chemometric Tools: A Case Study of Jabi Lake, Abuja, Nigeria DOI Creative Commons
E. O. Adejuwon, Toochukwu Chibueze Ogwueleka, Emmanuel Ogungbemi

и другие.

Iranian Journal of Science and Technology Transactions of Civil Engineering, Год журнала: 2025, Номер unknown

Опубликована: Янв. 2, 2025

Abstract Water pollution has become a growing threat to human society and natural ecosystems in recent decades. It increases the need understand surface water quality assessment better using chemometric tools within aquatic systems. This study sampled of 21 parameters at multiple sampling points Jabi Lake during wet dry seasons (August–December 2021) various statistical methods including cluster analysis, principal component analysis/factorial discriminant box plot analysis. These samples were examined for physicochemical employing standard techniques. The revealed significant seasonal variations quality. During season, key measurements included total dissolved solids (100.40 mg/l), oxygen (13.72 electrical conductivity (97.14 µs/cm). season showed higher levels most parameters, with 137.91 mg/l 230.93 µs/cm. Statistical analysis identified strong correlations between notably phosphate hardness ( r = 0.978, α 0.05) pH temperature 0.995, 0.05). four components explaining 98.5–100% variance, representing sources organic waste, domestic sewage, factors. findings indicated that more polluted, some exceeding World Health Organisation standards, suggesting potential health risks. research demonstrated effectiveness multivariate techniques analysing complex data provided valuable insights resource management, particularly regarding variations' impact on

Язык: Английский

Процитировано

0

A long-period assessment of climate change impact on river water quality in the central region of Iran DOI
Malihe Moazeni,

Masoud Sayedipour,

Kun‐Yi Andrew Lin

и другие.

International Journal of Environmental Science and Technology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 8, 2025

Язык: Английский

Процитировано

0

Spatial Dynamics and Ecotoxicological Health Hazards of Toxic Metals in Surface Water Impacted by Agricultural Runoff: Insights from Gis-Based Risk Assessment in the Sebou Basin, Morocco DOI
Hatim Sanad, Rachid Moussadek,

Latifa Mouhir

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Assessment of community perceptions on drinking water quality and its implications for human health in Islamabad, Pakistan: A comprehensive analysis DOI Creative Commons
Asif Sajjad, Muhammad Masood Ahmad,

Tariq Rana

и другие.

Desalination and Water Treatment, Год журнала: 2025, Номер 321, С. 101055 - 101055

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0