A novel predictive framework for water quality assessment based on socio-economic indicators and water leaving reflectance

Groundwater for Sustainable Development, Год журнала: 2025, Номер unknown, С. 101405 - 101405

Опубликована: Янв. 1, 2025

Язык: Английский

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Alleviating Health Risks for Water Safety: A Systematic Review on Artificial Intelligence-Assisted Modelling of Proximity-Dependent Emerging Pollutants in Aquatic Systems

Опубликована: Фев. 21, 2025

Emerging pollutants such as pharmaceuticals, industrial chemicals, heavy metals, and microplastics are a growing ecological risk affecting water soil resources. Another challenge in current wastewater treatments includes tracking treating these pollutants, which can be costly. As concern, emerging do not have lower limit levels detrimental to aquatic resources minuscule amounts. Thus, the assessment of multiple community-based sources surface groundwater is prioritized area study for resource management. It provides basis health management arising diseases cancer dengue caused by unsafe sources. Accordingly, utilizing artificial intelligence, wide-range data-driven insights synthesized assist propose solution pathways without need exhaustive experimentation. This systematic review examines intelligence-assisted modelling notably machine learning deep models, with proximity dependence correlated synergistic effects both humans life. underscores increasing accumulation their toxicological on community how utilized addressing research gaps related treatment methods pollutants.

Язык: Английский

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Predictive modelling of peroxisome proliferator-activated receptor gamma (PPARγ) IC50 inhibition by emerging pollutants using light gradient boosting machine

SAR and QSAR in environmental research, Год журнала: 2025, Номер unknown, С. 1 - 23

Опубликована: Март 24, 2025

Peroxisome proliferator-activated receptor gamma (PPARγ), a critical nuclear receptor, plays pivotal role in regulating metabolic and inflammatory processes. However, various environmental contaminants can disrupt PPARγ function, leading to adverse health effects. This study introduces novel approach predict the inhibitory activity (IC50 values) of 140 chemical compounds across 13 categories, including pesticides, organochlorines, dioxins, detergents, flame retardants, preservatives, on PPARγ. The predictive model, based light-gradient boosting machine (LightGBM) algorithm, was trained dataset 1804 molecules showed r2 values 0.82 0.59, Mean Absolute Error (MAE) 0.38 0.58, Root Square (RMSE) 0.54 0.76 for training test sets, respectively. provides insights into interactions between emerging PPARγ, highlighting potential hazards risks these chemicals may pose public environment. ability inhibition by hazardous demonstrates value this guiding enhanced toxicology research risk assessment.

Язык: Английский

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A review of machine learning and internet-of-things on the water quality assessment: methods, applications and future trends

Results in Engineering, Год журнала: 2025, Номер unknown, С. 105182 - 105182

Опубликована: Май 1, 2025

Язык: Английский

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Statistical analysis of earth observing data for physicochemical water quality parameters estimation for Lake Beseka, Northern main Ethiopian rift, Ethiopia

Geology Ecology and Landscapes, Год журнала: 2024, Номер unknown, С. 1 - 21

Опубликована: Май 27, 2024

Water quality deterioration in the Main Ethiopian Rift Lakes is one of problems affecting health and socioeconomic development area. This study was aimed to develop a method assess water change Lake Beseka using satellite image reflectance data observed data. Multi-temporal Landsat imagery three variables were used physicochemical environment: pH, EC, TDS. Linear regression correlation accomplished between parameters surface different band operations. The best-fitted linear equation for pH found on SWIR1 while EC TDS, blue/green ratio highly fitted. Pearson coefficients TDS − 0.79, 0.86, 0.85, respectively. RMSE p-value validation analysis 0.25/0.005, 548/0.003, 367/0.004 estimated higher central portion lake grouped under brackish lower southwestern, southern, northeastern shores. years 2018 2021 relatively than 2007 2023. spatiotemporal variations due anthropogenic geogenic factors including hot springs groundwater discharge lake, water-level rise depth variation, evaporation. These findings are helpful understanding equations that developed from data, which essential estimating monitoring cost-effective time-saving manner.

Язык: Английский

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Combining multiple numerical and chemometric models for assessing the microbial and pollution level of groundwater resources in a shallow alluvial aquifer, Southeastern Nigeria

Arabian Journal of Geosciences, Год журнала: 2024, Номер 17(7)

Опубликована: Июнь 8, 2024

Язык: Английский

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Modeling the vulnerability of water resources to pollution in a typical mining area, SE Nigeria using speciation, geospatial, and multi-path human health risk modeling approaches

Modeling Earth Systems and Environment, Год журнала: 2024, Номер 10(5), С. 5923 - 5952

Опубликована: Авг. 23, 2024

Язык: Английский

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Performance of artificial intelligence model (LSTM model) for estimating and predicting water quality index for irrigation purposes in order to improve agricultural production

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(11)

Опубликована: Окт. 13, 2024

Язык: Английский

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Exploring the Recent Trends, Progresses, and Challenges in the Application of Artificial Intelligence in Water Quality Assessment and Monitoring in Nigeria: A Systematic Review

Environmental science and engineering, Год журнала: 2024, Номер unknown, С. 339 - 366

Опубликована: Янв. 1, 2024

Язык: Английский

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Human health risk assessment of drinking water using heavy metal pollution index: a GIS-based investigation in mega city

Applied Water Science, Год журнала: 2024, Номер 15(1)

Опубликована: Дек. 28, 2024

Contaminated drinking water poses a significant threat to public health, particularly in urban areas where industrial and environmental pollutants may affect quality. However, there is lack of comprehensive studies that evaluate the specific health risks associated with harmful metal contaminants water. This study seeks address this gap by assessing quality contamination using pollution indices human risk assessments. The findings will help identify potential for residents guide development targeted interventions improved management strategies. groundwater samples were collected from five different zones Kasur rural area. A total 25 random sampling hand pumps during 4 months (March–June, 2021) determining various physiochemical attributes (pH, electric conductivity, turbidity, hardness, chloride, phosphate) potentially toxic elements (arsenic, cadmium, lead) standard protocols. Results revealed almost all physicochemical close World Health Organization (WHO) guidelines. assessment pH levels ranged 7.4 9.0, electrical conductivity (EC) between 150 µS/cm 800 µS/cm, average turbidity 12 ± 3.29 NTU, hardness varied 200 1000 mg/L. Chloride phosphate concentrations averaged 304 1.28 mg/L 4.51 1.99 mg/L, respectively. Cadmium 0.15 0.53 while lead arsenic reached up 7.47 exceeding WHO Heavy index (HPI) values sites less than critical value 100. considering HPI classes, locations had high (> 30) class indicating critically polluted heavy metals. Through exposure water, metals impact on non-carcinogenic (HI > 1), according hazard determined analysis children, infants, adults. As compared carcinogenic values, posed adults children infants as mean CR adults, 1.48E + 00, 1.40E 7.60E-01, It suggested supplies, need installation treatment plants minimize issues.

Язык: Английский

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