Marine waters assessment using improved water quality model incorporating machine learning approaches

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 344, P. 118368 - 118368

Published: June 24, 2023

In marine ecosystems, both living and non-living organisms depend on "good" water quality. It depends a number of factors, one the most important is quality water. The index (WQI) model widely used to assess quality, but existing models have uncertainty issues. To address this, authors introduced two new WQI models: weight based weighted quadratic mean (WQM) unweighted root squared (RMS) models. These were in Bay Bengal, using seven indicators including salinity (SAL), temperature (TEMP), pH, transparency (TRAN), dissolved oxygen (DOX), total oxidized nitrogen (TON), molybdate reactive phosphorus (MRP). Both ranked between "fair" categories, with no significant difference models' results. showed considerable variation computed scores, ranging from 68 88 an average 75 for WQM 70 76 72 RMS. did not any issues sub-index or aggregation functions, had high level sensitivity (R2 = 1) terms spatio-temporal resolution waterbodies. study demonstrated that approaches effectively assessed waters, reducing improving accuracy score.

Language: Английский

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Developing a novel tool for assessing the groundwater incorporating water quality index and machine learning approach

Groundwater for Sustainable Development, Journal Year: 2023, Volume and Issue: 23, P. 101049 - 101049

Published: Nov. 1, 2023

Groundwater plays a pivotal role as global source of drinking water. To meet sustainable development goals, it is crucial to consistently monitor and manage groundwater quality. Despite its significance, there are currently no specific tools available for assessing trace/heavy metal contamination in groundwater. Addressing this gap, our research introduces an innovative approach: the Quality Index (GWQI) model, developed tested Savar sub-district Bangladesh. The GWQI model integrates ten water quality indicators, including six heavy metals, collected from 38 sampling sites study area. enhance precision assessment, employed established machine learning (ML) techniques, evaluating model's performance based on factors such uncertainty, sensitivity, reliability. A major advancement incorporation metals into framework index model. best authors knowledge, marks first initiative develop encompassing heavy/trace elements. Findings assessment revealed that area ranged 'good' 'fair,' indicating most indicators met standard limits set by Bangladesh government World Health Organization. In predicting scores, artificial neural networks (ANN) outperformed other ML models. Performance metrics, root mean square error (RMSE), (MSE), absolute (MAE) training (RMSE = 0.361; MSE 0.131; MAE 0.262), testing 0.001; 0.00; 0.001), prediction evaluation statistics (PBIAS 0.000), demonstrated superior effectiveness ANN. Moreover, exhibited high sensitivity (R2 1.0) low uncertainty (less than 2%) rating These results affirm reliability novel monitoring management, especially regarding metals.

Language: Английский

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Assessing the impact of COVID-19 lockdown on surface water quality in Ireland using advanced Irish water quality index (IEWQI) model

Environmental Pollution, Journal Year: 2023, Volume and Issue: 336, P. 122456 - 122456

Published: Sept. 4, 2023

The COVID-19 pandemic has significantly impacted various aspects of life, including environmental conditions. Surface water quality (WQ) is one area affected by lockdowns imposed to control the virus's spread. Numerous recent studies have revealed considerable impact on surface WQ. In response, this research aimed assess in Ireland using an advanced WQ model. To achieve goal, six years monitoring data from 2017 2022 were collected for nine indicators Cork Harbour, Ireland, before, during, and after lockdowns. These include pH, temperature (TEMP), salinity (SAL), biological oxygen demand (BOD5), dissolved (DOX), transparency (TRAN), three nutrient enrichment indicators-dissolved inorganic nitrogen (DIN), molybdate reactive phosphorus (MRP), total oxidized (TON). results showed that lockdown had a significant indicators, particularly TEMP, TON, BOD5. Over study period, most within permissible limit except MRP, with exception during COVID-19. During pandemic, TON DIN decreased, while improved. contrast, COVID-19, at 7% sites deteriorated. Overall, Harbour was categorized as "good," "fair," "marginal" classes over period. Compared temporal variation, improved 17% period Harbour. However, no trend observed. Furthermore, analyzed model's performance assessing indicate model could be effective tool evaluating lockdowns' quality. can provide valuable information decision-making planning protect aquatic ecosystems.

Language: Английский

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Assessing water quality of an ecologically critical urban canal incorporating machine learning approaches

Ecological Informatics, Journal Year: 2024, Volume and Issue: 80, P. 102514 - 102514

Published: Feb. 13, 2024

This study assessed water quality (WQ) in Tongi Canal, an ecologically critical and economically important urban canal Bangladesh. The researchers employed the Root Mean Square Water Quality Index (RMS-WQI) model, utilizing seven WQ indicators, including temperature, dissolve oxygen, electrical conductivity, lead, cadmium, iron to calculate index (WQI) score. results showed that most of sampling locations poor WQ, with many indicators violating Bangladesh's environmental conservation regulations. eight machine learning algorithms, where Gaussian process regression (GPR) model demonstrated superior performance (training RMSE = 1.77, testing 0.0006) predicting WQI scores. To validate GPR model's performance, several measures, coefficient determination (R2), Nash-Sutcliffe efficiency (NSE), factor (MEF), Z statistics, Taylor diagram analysis, were employed. exhibited higher sensitivity (R2 1.0) (NSE 1.0, MEF 0.0) WQ. analysis uncertainty (standard 7.08 ± 0.9025; expanded 1.846) indicates RMS-WQI holds potential for assessing inland waterbodies. These findings indicate could be effective approach waters across study's did not meet recommended guidelines, indicating Canal is unsafe unsuitable various purposes. implications extend beyond contribute management initiatives

Language: Английский

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Assessment of human health risk from potentially toxic elements and predicting groundwater contamination using machine learning approaches

Journal of Contaminant Hydrology, Journal Year: 2024, Volume and Issue: 261, P. 104307 - 104307

Published: Jan. 21, 2024

The Rooppur Nuclear Power Plant (RNPP) at Ishwardi, Bangladesh is planning to go into operation within 2024 and therefore, adjacent areas of RNPP gaining adequate attention from the scientific community for environmental monitoring purposes especially water resources management. However, there a substantial lack literature as well datasets earlier years since very little was done beginning RNPP's construction phase. Therefore, this study conducted assess potential toxic elements (PTEs) contamination in groundwater its associated health risk residents part during year 2014–2015. For achieving aim study, samples were collected seasonally (dry wet season) nine sampling sites afterwards analyzed quality indicators such temperature (Temp.), pH, electrical conductivity (EC), total dissolved solid (TDS), hardness (TH) PTEs including Iron (Fe), Manganese (Mn), Copper (Cu), Lead (Pb), Chromium (Cr), Cadmium (Cd) Arsenic (As). This adopted newly developed Root Mean Square index (RMS-WQI) model scenario whereas human assessment utilized quantify toxicity PTEs. In most sites, concentration found higher season than dry Fe, Mn, Cd As exceeded guideline limit drinking water. RMS score mostly classified terms "Fair" condition. non-carcinogenic risks (expressed Hazard Index-HI) revealed that around 44% 89% adults 67% 100% children threshold set by USEPA (HI > 1) possessed through oral pathway season, respectively. Furthermore, calculated cumulative HI throughout period. carcinogenic (CR) PTEs, magnitude decreased following pattern Cr Cd. Although current based on old dataset, findings might serve baseline reduce future hazardous impact power plant.

Language: Английский

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Assessment of hydrogeochemistry in groundwater using water quality index model and indices approaches

Heliyon, Journal Year: 2023, Volume and Issue: 9(9), P. e19668 - e19668

Published: Sept. 1, 2023

Groundwater resources around the world required periodic monitoring in order to ensure safe and sustainable utilization for humans by keeping good status of water quality. However, this could be a daunting task developing countries due insufficient data spatiotemporal resolution. Therefore, research work aimed assess groundwater quality terms drinking irrigation purposes at adjacent part Rooppur Nuclear Power Plant (RNPP) Bangladesh. For achieving aim study, nine samples were collected seasonally (dry wet season) seventeen hydro-geochemical indicators analyzed, including Temperature (Temp.), pH, electrical conductivity (EC), total dissolved solids (TDS), alkalinity (TA), hardness (TH), organic carbon (TOC), bicarbonate (HCO3-), chloride (Cl-), phosphate (PO43-), sulfate (SO42-), nitrite (NO2-), nitrate (NO3-), sodium (Na+), potassium (K+), calcium (Ca2+) magnesium (Mg2+). The present study utilized Canadian Council Ministers Environment index (CCME-WQI) model purposes. In addition, indices EC, TDS, TH, adsorption ratio (SAR), percent (Na%), permeability (PI), Kelley's (KR), hazard (MHR), soluble percentage (SSP), Residual carbonate (RSC) used assessing computed mean CCME-WQI score found higher during dry season (ranges 48 74) than 40 65). Moreover, ranked between "poor" "marginal" categories implying unsuitable human consumption. Like model, majority also demonstrated suitable crop cultivation season. findings indicate that it requires additional care improve programme protecting RNPP area. Insightful information from might useful as baseline national strategic planners protect any emergencies associated with RNPP.

Language: Английский

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Comparison between the WFD approaches and newly developed water quality model for monitoring transitional and coastal water quality in Northern Ireland

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 901, P. 165960 - 165960

Published: Aug. 3, 2023

This study aims to evaluate existing approaches for monitoring and assessing water quality in waterbodies the North of Ireland using newly developed methodologies. The results reveal significant differences between new technique "one-out, all-out" approach rating quality. found status be "good," "fair," "marginal," whereas classified as "moderate," respectively. outperformed different waterbody types, with high R2 = 1, NSE 0.99, MEF 0 values. Furthermore, final assessment methodologies had lowest uncertainty (<1 %), efficiency measures (NSE MEF) indicate that are bias-free assess at any geographic scale. this proposed effective states transitional coastal Ireland. also highlighted limitations importance updating resource management systems better protection these waterbodies. findings have implications planning other similar regions.

Language: Английский

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Data-driven modelling for assessing trophic status in marine ecosystems using machine learning approaches

Environmental Research, Journal Year: 2023, Volume and Issue: 242, P. 117755 - 117755

Published: Nov. 25, 2023

Assessing eutrophication in coastal and transitional waters is of utmost importance, yet existing Trophic Status Index (TSI) models face challenges like multicollinearity, data redundancy, inappropriate aggregation methods, complex classification schemes. To tackle these issues, we developed a novel tool that harnesses machine learning (ML) artificial intelligence (AI), enhancing the reliability accuracy trophic status assessments. Our research introduces an improved data-driven methodology specifically tailored for (TrC) waters, with focus on Cork Harbour, Ireland, as case study. innovative approach, named Assessment (ATSI) model, comprises three main components: selection pertinent water quality indicators, computation ATSI scores, implementation new scheme. optimize input minimize employed ML techniques, including advanced deep methods. Specifically, CHL prediction model utilizing ten algorithms, among which XGBoost demonstrated exceptional performance, showcasing minimal errors during both training (RMSE = 0.0, MSE MAE 0.01) testing phases. Utilizing linear rescaling interpolation function, calculated scores evaluated model's sensitivity efficiency across diverse application domains, employing metrics such R2, Nash-Sutcliffe (NSE), factor (MEF). The results consistently revealed heightened all domains. Additionally, introduced brand scheme ranking waters. assess spatial sensitivity, applied to four distinct waterbodies comparing assessment outcomes Estuaries Bays Ireland (ATSEBI) System. Remarkably, significant disparities between ATSEBI System were evident except Mulroy Bay. Overall, our significantly enhances assessments marine ecosystems. combined cutting-edge techniques scheme, represents promising avenue evaluating monitoring conditions TrC study also effectiveness assessing various waterbodies, lakes, rivers, more. These findings make substantial contributions field ecosystem management conservation.

Language: Английский

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Pollution status and ecological risks of metals in surface water of a coastal estuary and health risk assessment for recreational users

Chemosphere, Journal Year: 2023, Volume and Issue: 348, P. 140768 - 140768

Published: Nov. 22, 2023

Language: Английский

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Enhancing groundwater quality assessment in coastal area: A hybrid modeling approach

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

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