Hydrological distribution of physicochemical parameters and heavy metals in surface water and their ecotoxicological implications in the Bay of Bengal coast of Bangladesh

Islam, Md. Saiful; Idris, Abubakr M.; Islam, Abu Reza Md. Towfiqul; Ali, Mir Mohammad; Rakib, Md. Refat Jahan

doi:10.1007/s11356-021-15353-9

Cited by 60 publications

(16 citation statements)

References 59 publications

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“…In the Kutubdia Channel, Bangladesh, Hossain M. S. et al (2020) found 22.0 mg/kg Pb. About 23.82 mg/kg, 6.47 mg/kg, 4.96 mg/kg, 15.49 mg/kg, 26.7 mg/kg, 26.42 mg/kg, and 3.12 mg/kg were found in the Bhairab River, Feni River Estuary, Karnaphuli River, Matamuhuri River, and Bakkhali River, respectively, by Ali et al (2021), Islam et al (2018), Islam et al (2013), Mamun et al (2013, Siddique and Aktar (2012), Ashraful et al (2009). These results were lower than those found in the current study.…”

Section: Leadmentioning

confidence: 96%

“…The contents were cooked for 5 h at 130 °C, or only 2-3 ml remained. Materials were passed through Whatman 41 filter paper after digestion (colorless or weak color of content), rinsed with 0.1 M HNO 3 solution, and produced up to 100 ml volume for metal analysis (Ali et al, 2021).…”

Section: Sample Digestionmentioning

confidence: 99%

“…In a riverine ecosystem, the concentration of HM in sediment is thought to be an indicator of monitoring the environmental conditions and pollution status (Han et al, 2018;Rahman et al, 2019;Hossain et al, 2021;Ali et al, 2022). As metals tend to accumulate in the bottom layers of riverine water bodies, heavy metals are much higher in sediment than in the water column (Bhuyan et al, 2017;Islam et al, 2021;Jolly et al, 2021). Heavy metals are not permanently attached to sediments, and they can be released into the water column due to the change in environmental circumstances such as temperature and pH, or other physical or biological disturbances (Agarwal et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Pollution level of trace metals (As, Pb, Cr and Cd) in the sediment of Rupsha River, Bangladesh: Assessment of ecological and human health risks

Kubra

Mondol

Ali

et al. 2022

Front. Environ. Sci.

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The study area was selected in the Rupsha river basin and the sediment samples were collected to determine trace metal concentrations of As, Pb, Cd, and Cr along with biological effects, and potential ecological and human health hazards for adults and children. The concentrations of trace metals were detected from sixty composite sediment samples using an Atomic Absorption Spectrophotometer (AAS) following some sequential analytical procedures. The mean concentrations of trace metals were organized in the descending order of chromium (Cr) (43.2 mg/kg) > lead (Pb) (29.21 mg/kg) > arsenic (As) (5.18 mg/kg) > cadmium (Cd) (1.8 mg/kg). The current study highlighted that metals were attributed to the riverine sediments from natural sources and other anthropogenic sources, particularly from various industries. Based on the effect-range classifications of threshold effect concentration (TEC) and probable effect concentration (PEC), the trace metal concentrations can impact on the sediment-dwelling organisms occasionally especially for Cr, Cd, and Pb. That is, the concentrations had negative biological consequences on aquatic creatures. The assessed potential ecological risk of Cd offers a significant risk to the aquatic ecosystem, whereas As, Cr, and Cd were in low-risk. Most of the sites of the study area were within the range of moderate risk, indicated by the risk index (RI) values. Furthermore, the applied sediment quality indices, geo-accumulation index (Igeo) indicated that sediment was contaminated by Cd whereas contamination factor (CF) denoted that the sediment of the study area was moderately polluted by Pb. However, pollution load index (PLI) revealed that the study area was polluted for cumulative sence especially in winter season. The age-group risk index (HI) was much lower than the threshold limit of 1, showing that the pollution had no non-carcinogenic risk effect. Total carcinogenic risk (TCR) was less than one-tenth of a percentile. For the sake of human and environmental health, proper monitoring of metal element attribution and strict regulation are required to lessen trace metal pollution.

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Section: Leadmentioning

confidence: 96%

Section: Sample Digestionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pollution level of trace metals (As, Pb, Cr and Cd) in the sediment of Rupsha River, Bangladesh: Assessment of ecological and human health risks

Kubra

Mondol

Ali

et al. 2022

Front. Environ. Sci.

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“…The people rely on salinity-rich rivers, channels, and fishponds for their water supply 68 . Furthermore, rainwater recharges the shallow aquifer during the dry winter season, which is invaded excessively 69 .…”

Section: Methodsmentioning

confidence: 99%

Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh

Jamei

Karbasi

Malik

et al. 2022

Sci Rep

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The rising salinity trend in the country’s coastal groundwater has reached an alarming rate due to unplanned use of groundwater in agriculture and seawater seeping into the underground due to sea-level rise caused by global warming. Therefore, assessing salinity is crucial for the status of safe groundwater in coastal aquifers. In this research, a rigorous hybrid neurocomputing approach comprised of an Adaptive Neuro-Fuzzy Inference System (ANFIS) hybridized with a new meta-heuristic optimization algorithm, namely Aquila optimization (AO) and the Boruta-Random forest feature selection (FS) was developed for estimating the salinity of multi-aquifers in coastal regions of Bangladesh. In this regard, 539 data samples, including ten water quality indices, were collected to provide the predictive model. Moreover, the individual ANFIS, Slime Mould Algorithm (SMA), and Ant Colony Optimization for Continuous Domains (ACOR) coupled with ANFIS (i.e., ANFIS-SMA and ANFIS-ACOR) and LASSO regression (Lasso-Reg) schemes were examined to compare with the primary model. Several goodness-of-fit indices, such as correlation coefficient (R), the root mean squared error (RMSE), and Kling-Gupta efficiency (KGE) were used to validate the robustness of the predictive models. Here, the Boruta-Random Forest (B-RF), as a new robust tree-based FS, was adopted to identify the most significant candidate inputs and effective input combinations to reduce the computational cost and time of the modeling. The outcomes of four selected input combinations ascertained that the ANFIS-OA regarding the best accuracy in terms of (R = 0.9450, RMSE = 1.1253 ppm, and KGE = 0.9146) outperformed the ANFIS-SMA (R = 0.9406, RMSE = 1.1534 ppm, and KGE = 0.8793), ANFIS-ACOR (R = 0.9402, RMSE = 1.1388 ppm, and KGE = 0.8653), Lasso-Reg (R = 0.9358), and ANFIS (R = 0.9306) models. Besides, the first candidate input combination (C1) by three inputs, including Cl− (mg/l), Mg2+ (mg/l), Na+ (mg/l), yielded the best accuracy among all alternatives, implying the role importance of (B-RF) feature selection. Finally, the spatial salinity distribution assessment in the study area ascertained the high predictability potential of the ANFIS-OA hybrid with B-RF feature selection compared to other paradigms. The most important novelty of this research is using a robust framework comprised of the non-linear data filtering technique and a new hybrid neuro-computing approach, which can be considered as a reliable tool to assess water salinity in coastal aquifers.

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“…Water is one of the most basic needs for life to continue for all life forms on the world (Bytyci et al 2018 ; Islam et al 2022 ; Elbeltagi et al 2022 ) and it is also one of the most significant natural resources on which the socio-economic growth, as well as the sustainable development of a society (Pobi et al 2019 ; Proshad et al 2020 ; Varol et al 2022 ; Kumar et al 2022 ). However, despite its importance to mankind, it is one of the most poorly managed resources in the earth and facing a serious threat because of anthropogenic activities (Islam et al 2014 , 2015 , 2017a , 2021 ; Ustaoğlu and Islam 2020 ; Yüksel et al 2021 ; Ali et al 2021a , b ; Rani et al 2021 ; Varol and Tokatlı 2021 ; 2022 ; Pal et al 2022 ; Proshad et al 2021a , b ). With the increasing use of a wide variety of remedial measures against COVID-19 pandemic situation in Türkiye in way of disposing waste indiscriminately to the environment, there is little awareness of detrimental health effects of post operation measures for COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the effects of COVID-19 lockdown period on groundwater quality of a significant rice land in an urban area of Türkiye

Tokatlı

Titiz

Uğurluoğlu³

et al. 2022

Environ Sci Pollut Res

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In the current research, the impact of the COVID-19 lockdown period on groundwater quality of Lower Meriç Plain (Thrace Region of Türkiye) was evaluated. Some significant nutrient characteristics (NO 3 − , NO 2 − , and PO 4 3− ), salinity characteristics (EC, TDS, and salinity), and physical characteristics (temperature, DO, pH, and turbidity) were investigated in groundwater samples collected from 45 sampling points in pre-lockdown and lockdown periods. Water quality index (WQI) and nutrient pollution index (NPI), Pearson correlation index (PCI), cluster analysis (CA), one-way ANOVA test (OWAT), and factor analysis (FA) were applied to assess ecological risk. Excluding recorded statistical differences in temperature and DO due to climatic conditions ( p < 0.05 ), levels of all the investigated water quality parameters show no statistically significant differences and no significant reduction in pollutants measured in the lockdown period. On the contrary, the WQI and NPI scores have increased between the rates of 4.76–27.10% during the lockdown period. In the lockdown period, although the reduction of industry or limited production of many industrial facilities reduced the inorganic contaminant releases to the environment, ongoing agricultural activities and domestic wastes caused to prevent the reduction of organic pollutants in groundwater of the region during the lockdown period. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s11356-022-20959-8.

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Hydrological distribution of physicochemical parameters and heavy metals in surface water and their ecotoxicological implications in the Bay of Bengal coast of Bangladesh

Cited by 60 publications

References 59 publications

Pollution level of trace metals (As, Pb, Cr and Cd) in the sediment of Rupsha River, Bangladesh: Assessment of ecological and human health risks

Pollution level of trace metals (As, Pb, Cr and Cd) in the sediment of Rupsha River, Bangladesh: Assessment of ecological and human health risks

Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh

Assessment of the effects of COVID-19 lockdown period on groundwater quality of a significant rice land in an urban area of Türkiye

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