Soil contamination and health risk assessment at coastal Upazilas of the Bangladesh: a case study DOI Creative Commons
Sk. Abdul Kader Arafin, Md Musfike Meraz, Hazem Ghassan Abdo

et al.

Environmental Pollutants and Bioavailability, Journal Year: 2023, Volume and Issue: 35(1)

Published: Sept. 4, 2023

The present study analyzed soil samples from flood-prone Unions in two Coastal Upazilas of Bangladesh using Proton Induced X-Ray Emission (PIXE) techniques with Van de Graaff Accelerator for detecting heavy trace elements and Gamma spectrometry techniques. findings indicate that while Potassium (averaging 19,62 μg/g Sutarkhali; 21364.67 Amtoli) Calcium 36,923.92 30404.33 levels were high naturally, the Lead 71.8 171.44 Amtoli), Chromium 6.87 340.22 posing a serious risk to inhabitants. evaluation contamination factor (CF), pollution load index (PLI), potential ecological (ERI) health assessment severe metal both regions, young children being particularly vulnerable lead poisoning. Nonetheless, radiation below safe limit set by International Atomic Energy Agency (IAEA).

Language: Английский

Enhancing groundwater quality assessment in coastal area: A hybrid modeling approach DOI Creative Commons
Md Galal Uddin, M. M. Shah Porun Rana, Mir Talas Mahammad Diganta

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(13), P. e33082 - e33082

Published: June 19, 2024

Monitoring of groundwater resources in coastal areas is vital for human needs, agriculture, ecosystems, securing water supply, biodiversity, and environmental sustainability. Although the utilization quality index (WQI) models has proven effective monitoring resources, it faced substantial criticism due to its inconsistent outcomes, prompting need more reliable assessment methods. Therefore, this study addresses concern by employing data-driven root mean squared (RMS) evaluate Bhola district near Bay Bengal, Bangladesh. To enhance reliability RMS-WQI model, research incorporated extreme gradient boosting (XGBoost) machine learning (ML) algorithm. For GWQ, utilized eleven crucial indicators, including turbidity (TURB), electric conductivity (EC), pH, total dissolved solids (TDS), nitrate (NO3-), ammonium (NH4+), sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), iron (Fe). In terms GW concentration K, Ca Mg exceeded guideline limit collected samples. The computed scores ranged from 54.3 72.1, with an average 65.2, categorizing all sampling sites' GWQ as "fair." model reliability, XGBoost demonstrated exceptional sensitivity (R2 = 0.97) predicting accurately. Furthermore, exhibited minimal uncertainty (<1%) WQI scores. These findings implied efficacy accurately assessing areas, that would ultimately assist regional managers strategic planners sustainable management resources.

Language: Английский

Citations

13

Coastal groundwater quality prediction using objective-weighted WQI and machine learning approach DOI
Chinmoy Ranjan Das, Subhasish Das

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(13), P. 19439 - 19457

Published: Feb. 15, 2024

Language: Английский

Citations

9

Determination of water quality and efficient removal of arsenic and iron from groundwater using mahogany fruit husk and banana peduncle charcoals DOI Creative Commons
Molla Rahman Shaibur,

Yasmin Khatun,

Masum Howlader

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102220 - 102220

Published: May 4, 2024

The groundwater (GW) of Bangladesh is predominantly contaminated with arsenic (As) and iron (Fe) which has a bad impact on human health. We tried to remove these elements easily available mahogany-fruit (Swietenia mahagoni) husk charcoal (MHC) banana (Musa acuminata) peduncle (BPC). trial was implemented 3 replications throughout the research. sampled GW contained 0.06 mg As L-1 4.83 Fe L-1. Firstly, pH 3, 5, 7, 9 250 dose. MHC removed almost 91.05% at 5.0, BPC 86.67% 9. However, in case Fe, 100% 7 9; same quantity Secondly, contact times were 0, 10, 20, 40 minutes dose 7.0. maximum removal 5 minutes. pseudo-first-order kinetic, pseudo-second-order intra-particle diffusion models considered. result showed that rate adsorption followed kinetic model. Lastly, adsorbent doses 50, 150, 250, 350 At highest 79.47% for MHC. Similarly, values 79.29% BPC, indicating are good heavy metals removal.

Language: Английский

Citations

9

Heavy metal (Pb, Cd and Cr) contamination and human health risk assessment of groundwater in Kuakata, southern coastal region of Bangladesh DOI Creative Commons
Abdulla Nasir Chowdhury,

Samsun Naher,

Md. Nur Alam Likhon

et al.

Geosystems and Geoenvironment, Journal Year: 2024, Volume and Issue: unknown, P. 100325 - 100325

Published: Nov. 1, 2024

Language: Английский

Citations

5

A concise review of the impact of groundwater pollution in coastal regions on human gut microbiome composition and its effect on human health DOI

A T Rithi,

Antara Banerjee, Abhijit Mitra

et al.

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 26, P. 101187 - 101187

Published: April 30, 2024

Language: Английский

Citations

3

Identifying spatial patterns and driving factors of anthropogenic impacts on the groundwater environment based on groundwater chemical kinetics DOI
Yuandong Deng, Ying Lü,

Xinqiang Du

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 486, P. 144436 - 144436

Published: Dec. 9, 2024

Language: Английский

Citations

3

Developing a Semi-Automated Technique of Surface Water Quality Analysis Using GEE And Machine Learning: A Case Study for Sundarbans DOI Creative Commons
Sheikh Fahim Faysal Sowrav,

Sujit Kumar Debsarma,

Mohan Kumar Das

et al.

Heliyon, Journal Year: 2025, Volume and Issue: 11(3), P. e42404 - e42404

Published: Feb. 1, 2025

This study presents a semi-automated approach for assessing water quality in the Sundarbans, critical and vulnerable ecosystem, using machine learning (ML) models integrated with field remotely-sensed data. Key parameters-Sea Surface Temperature (SST), Total Suspended Solids (TSS), Turbidity, Salinity, pH-were predicted through ML algorithms interpolated Empirical Bayesian Kriging (EBK) model ArcGIS Pro. The predictive framework leverages Google Earth Engine (GEE) AutoML, utilizing deep libraries to create dynamic, adaptive that enhance prediction accuracy. Comparative analyses showed ML-based effectively captured spatial temporal variations, aligning closely measurements. integration provides more efficient alternative traditional methods, which are resource-intensive less practical large-scale, remote areas. Our findings demonstrate this technique is valuable tool continuous monitoring, particularly ecologically sensitive areas limited accessibility. also offers significant applications climate resilience policy-making, as it enables timely identification of deteriorating trends may impact biodiversity ecosystem health. However, acknowledges limitations, including variability data availability inherent uncertainties predictions dynamic systems. Overall, research contributes advancement monitoring techniques, supporting sustainable environmental management practices Sundarbans against emerging challenges.

Language: Английский

Citations

0

An Improved Method for Estimating Blue Carbon Storage in Coastal Salt Marsh Wetlands: Considering the Heterogeneity of Soil Thickness DOI Creative Commons
Lina Ke,

Chengkai Yin,

Nan Lei

et al.

Land, Journal Year: 2025, Volume and Issue: 14(4), P. 776 - 776

Published: April 4, 2025

Coastal wetlands are vital ecosystems at the land–sea interface. They intercept land-based pollutants, regulate microclimates, and mediate carbon cycles. play a significant role in enhancing sequestration capacity maintaining ecological structure functioning. This study proposes an improved method for estimating blue storage coastal salt marsh wetlands, considering soil thickness, by utilizing enhanced Soil Land Inference Model (SoLIM) to estimate thickness with restricted number of sample points. The wetland index is integrated into Integrated Valuation Ecosystem Services Trade-offs (InVEST) estimation model, ultimately enabling visualization Liaohe Estuary wetland. Results indicate following: (1) studied area’s shows spatial distribution pattern that becomes progressively thinner from north south. more vegetation areas minor tidal flat areas, 52% region having between 40 60 cm. (2) In 2023, stock area estimated 389.85 × 106 t, high-value concentrated northern natural landscapes, low-value southern zone, characterized terrain human influence. coupled thickness–blue model provides methodological support refining wetlands. It also offers technical formulating policies on restoration, compensation, protection, management

Language: Английский

Citations

0

Assessing spatial distribution of heavy metal contamination in groundwater and associated human health risk in the Chittagong industrial area, Bangladesh DOI Creative Commons
Md. Sajib Hossain, Ashfaqur Rahman, Elsai Mati Asefa

et al.

Journal of Hazardous Materials Advances, Journal Year: 2025, Volume and Issue: unknown, P. 100728 - 100728

Published: April 1, 2025

Language: Английский

Citations

0

Sources, consumption patterns and challenges assessment of freshwater in the coastal regions of Bangladesh DOI Creative Commons
Md. Shohel Khan, Shitangsu Kumar Paul

World Development Sustainability, Journal Year: 2025, Volume and Issue: unknown, P. 100227 - 100227

Published: May 1, 2025

Language: Английский

Citations

0