Salinity-induced fluorescent dissolved organic matter influence co-contamination, quality and risk to human health of tube well water, southeast coastal Bangladesh

Predicting groundwater phosphate levels in coastal multi-aquifers: A geostatistical and data-driven approach

The groundwater (GW) resource plays a central role in securing water supply in the coastal region of Bangladesh and therefore the future sustainability of this valuable resource is crucial for the area. However, there is limited research on the driving factors and prediction of phosphate concentration in groundwater. In this work, geostatistical modeling, self-organizing maps (SOM) and data-driven algorithms were combined to determine the driving factors and predict GW phosphate content in coastal multi-aquifers in southern Bangladesh. The SOM analysis identified three distinct spatial patterns: K+Na+pH, Ca2+Mg2+NO₃-, and HCO₃-SO₄2-PO43-F-. Four data-driven algorithms, including CatBoost, Gradient Boosting Machine (GBM), Long Short-Term Memory (LSTM), and Support Vector Regression (SVR) were used to predict phosphate concentration in GW using 380 samples and 15 prediction parameters. Forecasting accuracy was evaluated using RMSE, R2, RAE, CC, and MAE. Phosphate dissolution and saltwater intrusion, along with phosphorus fertilizers, increase PO43- content in GW. Using input parameters selected by multicollinearity and SOM, the CatBoost model showed exceptional performance in both training (RMSE = 0.002, MAE = 0.001, R2 = 0.999, RAE = 0.057, CC = 1.00) and testing (RMSE = 0.001, MAE = 0.002, R2 = 0.989, RAE = 0.057, CC = 0.998). Na+, K+, and Mg2+ significantly influenced prediction accuracy. The uncertainty study revealed a low standard error for the CatBoost model, indicating robustness and consistency. Semi-variogram models confirmed that the most influential attributes showed weak dependence, suggesting that agricultural runoff increases the heterogeneity of PO43- distribution in GW. These findings are crucial for developing conservation and strategic plans for sustainable utilization of coastal GW resources.

Hydro-geochemistry and age-dependent health risk assessment of nitrate, nitrite, and fluoride in health facilities water: a multivariate analysis

Groundwater from alluvial fan plains is the prevailing water source, especially for arid/semiarid regions, but its contamination poses substantial risks to water supply and public health. The recent study aims to assess the hydro-geochemistry, distribution, and potential health risks of NO3-, NO2-, and F- concentrations in the groundwater of previously unexplored health facilities in District Vehari, Punjab, Pakistan. In total, 75 groundwater samples were evaluated for NO3-, NO2-, and F- levels as well as pH, EC, TDS, CO32-, HCO3-, Cl-, Na+, Fe, K+, Ca2+, Mg2+, taste, odor, color, and turbidity. The Durav graph shows that the water type is Na-HCO3-Ca, with Na and HCO3 dominant, weak acids > strong acids, and alkaline ions > alkalis. Results revealed that drinking water samples (21.73% and 20%) taken from Tehsil Mailsi, and the Basic Health Unit (BHU) exceeded the WHO standard (1.5 mg/L) for F- concentration, respectively. Moreover, the mean chronic daily intake (CDI) of F- was 0.044, 0.018, and 0.02 mg/kg/day in children, men, and women, respectively. Similarly, the average CDI of NO3- was 0.113, 0.046, and 0.050 in children, men, and women, respectively, and the respective values of NO2- were 0.004, 0.001, and 0.001. The NO2- shows a significant range of hazard quotient (HQ) (0.0-1.172) in children. The range of HQ for F- was 0.0-3.114, 0.0-1.290, and 0.0-1.389 in children, men, and women, respectively. Additionally, the health risks analysis revealed an HQ > 1.0 for children in groundwater, indicating a potential carcinogenic risk from the F-. Pearson correlation and PCA analysis found a significant positive correlation (0.8) between NO3- and NO2- and a negative correlation (0.3) between F- and HCO3-. These findings highlight the need for groundwater treatment in healthcare facilities prior to water consumption. Enforcing international and national drinking water standards in healthcare units is vital to strengthening services and providing equitable access to safe drinking water. Legislative and efficient water management measures must be taken for the protection of public health.

Sewage Vertical Infiltration Introduced Polygenic Multipollutants into Groundwater

With the increasing environmental impacts of human activities, the problem of polygenic multipollutants in groundwater has attracted the attention of researchers. Identifying the hydrobiogeochemical characteristics of the surface sewage that replenishes groundwater is crucial to addressing this problem. The input of polygenic multipollutants into groundwater leads to not only the mechanical superposition of pollutants but also the formation of secondary pollutant types. The evolution of polygenic multipollutants is influenced by aquifer characteristics, carbon sources, microbial abundance, etc. Therefore, this study took a sewage leakage point in Northwest China as the research object, carried out a controlled laboratory experiment on the impact of sewage discharge on groundwater, and, combined with long-term field monitoring results, determined the main hydrobiogeochemical processes of polygenic multipollutants and their secondary pollutants. The results showed that the redox environment and the gradient change in pH were identified as the most critical controlling factors. In oxidative groundwater during the early stage of vertical infiltration, sewage carries a substantial amount of NH4+, which is oxidized to form the secondary pollutant NO3−. As O2 is consumed, the reduction intensifies, and secondary pollutants NO3−, Mn (IV), and Fe(III) minerals are successively reduced. Compared with the natural conditions of rainwater vertical infiltration, the reaction rates and intensities of various reactions significantly increase during sewage vertical infiltration. However, there is a notable difference in the groundwater pH between sewage and rainwater vertical infiltration. In O2 and secondary pollutant NO3− reduction, a large amount of CO2 is rapidly generated. Excessive CO2 dissolves to produce a substantial amount of H+, promoting the acidic dissolution of Mn (II) minerals and generation of Mn2+. Sewage provides a higher carbon load, enhancing Mn (II) acidic dissolution and stimulating the activity of dissimilatory nitrate reduction to ammonium, which exhibits a higher contribution to NO3− reduction. This results in a portion of NO3− converted from NH4+ being reduced back to NH4+ and retained in the groundwater, reducing the denitrification’s capacity to remove secondary NO3−. This has important implications for pollution management and groundwater remediation, particularly monitored natural attenuation.

Assessment of potentially toxic elements in groundwater through interpolation, pollution indices, and chemometric techniques in Dehradun in Uttarakhand State

Providing safe drinking water for the entire world's population is essential for ensuring sustainable development. The presence of harmful compounds in aquifers, majorly toxic elements, is a serious environmental concern around the globe. This research aimed to quantify for the initial period the amounts of toxic elements in freshwater in the Dehradun Industrial Region of Uttrakhand, India, as well as the associated health risks. The PTEs (potentially toxic elements) Fe, Cd, Mn, Cu, Cr and Pb, Zn, Ni is measured by AAS and compared to BIS and WHO requirements for drinking safety. The order of mean trace element values in all groundwater samples were determined as Fe > Zn > Cu > Ni > Co > Cd > Pb. HPI was discovered to be higher than high class during the research period (HPI > 30), but under the severe contamination criterion of 100. Iron's MI and PI values were consistently over the threshold limit during the research period, and certain toxic elements were discovered exceptionally near the threshold limit, indicating a severe future influence on groundwater quality. According to PCA (principal component analysis), CM (correlation matrix), and potential health hazard, maximum levels of toxic elements in groundwater in the Dehradun region are attributed to land use patterns, anthropogenic activity, industrial activity, fertilizer and pesticide leaching, and residential waste into the aquifer system. The findings of this study could aid local planners and policymakers in preventing health risks from contaminated aquifers through the deployment of suitable monitoring and mitigation measures.

Assessing metal(loid)s-Induced long-term spatiotemporal health risks in Coastal Regions, Bay of Bengal: A chemometric study

Despite sporadic and irregular studies on heavy metal(loid)s health risks in water, fish, and soil in the coastal areas of the Bay of Bengal, no chemometric approaches have been applied to assess the human health risks comprehensively. This review aims to employ chemometric analysis to evaluate the long-term spatiotemporal health risks of metal(loid)s e.g., Fe, Mn, Zn, Cd, As, Cr, Pb, Cu, and Ni in coastal water, fish, and soils from 2003 to 2023. Across coastal parts, studies on metal(loid)s were distributed with 40% in the southeast, 28% in the south-central, and 32% in the southwest regions. The southeastern area exhibited the highest contamination levels, primarily due to elevated Zn content (156.8 to 147.2 mg/L for Mn in water, 15.3 to 13.2 mg/kg for Cu in fish, and 50.6 to 46.4 mg/kg for Ni in soil), except for a few sites in the south-central region. Health risks associated with the ingestion of Fe, As, and Cd (water), Ni, Cr, and Pb (fish), and Cd, Cr, and Pb (soil) were identified, with non-carcinogenic risks existing exclusively through this route. Moreover, As, Cr, and Ni pose cancer risks for adults and children via ingestion in the southeastern region. Overall non-carcinogenic risks emphasized a significantly higher risk for children compared to adults, with six, two-, and six-times higher health risks through ingestion of water, fish, and soils along the southeastern coast. The study offers innovative sustainable management strategies and remediation policies aimed at reducing metal(loid)s contamination in various environmental media along coastal Bangladesh.

Analysis of self-organizing maps and explainable artificial intelligence to identify hydrochemical factors that drive drinking water quality in Haor region

Water contamination undermines human survival and economic growth. Water resource protection and management require knowledge of water hydrochemistry and drinking water quality characteristics, mechanisms, and factors. Self-organizing maps (SOM) have been developed using quantization and topographic error approaches to cluster hydrochemistry datasets. The Piper diagram, saturation index (SI), and cation exchange method were used to determine the driving mechanism of hydrochemistry in both surface and groundwater, while the Gibbs diagram was used for surface water. In addition, redundancy analysis (RDA) and a generalized linear model (GLM) were used to determine the key drinking water quality parameters in the study area. Additionally, the study aimed to utilize Explainable Artificial Intelligence (XAI) techniques to gain insights into the relative importance and impact of different parameters on the entropy water quality index (EWQI). The SOM results showed that thirty neurons generated the hydrochemical properties of water and were organized into four clusters. The Piper diagram showed that the primary hydrochemical facies were HCO3--Ca2+ (cluster 4), Cl---Na+ (all clusters), and mixed (clusters 1 and 4). Results from SI and cation exchange show that demineralization and ion exchange are the driving mechanisms of water hydrochemistry. About 45 % of the studied samples are classified as "medium quality"," that could be suitable as drinking water with further refinement. Cl- may pose increased non-carcinogenic risk to adults, with children at double risk. Cluster 4 water is low-risk, supporting EWQI findings. The RDA and GLM observations agree in that Ca2+, Mg2+, Na+, Cl- and HCO3- all have a positive and significant effect on EWQI, with the exception of K+. TDS, EC, Na+, and Ca2+ have been identified as influencing factors based on bagging-based XAI analysis at global and local levels. The analysis also addressed the importance of SO4, HCO3, Cl, Mg2+, K+, and pH at specific locations.

Spatio-temporal characterization of dissolved organic matter in karst rivers disturbed by acid mine drainage and its correlation with metal ions

Dissolved organic matter (DOM) is widely present in surface water environments and plays a critical role in the biogeochemical cycling of metal ions. Metal ions in acid mine drainage (AMD) have seriously polluted karst surface water environments, but few studies have explored interactions between DOM and metal ions in AMD-disturbed karst rivers. Here, the composition and sources of DOM in AMD-disturbed karst rivers were investigated by fluorescence excitation-emission spectroscopy combined with parallel factor analysis. In addition, correlations between metal ions and other factors (DOM components, total dissolved carbon (TDC) and pH) were determined using structural equation modeling (SEM). Results showed that there were evident differences in the seasonal distribution of TDC and metal ion concentrations in AMD-disturbed karst rivers. The concentrations of DOC, dissolved inorganic carbon (DIC), and metal ions were generally higher in the dry season than in the wet season, with Fe and Mn pollution being the most pronounced. The DOM in AMD contained two types of protein-like substances that were mainly from autochthonous inputs, while DOM in AMD-disturbed karst rivers contained two additional types of humic-like substances from both autochthonous and allochthonous inputs. The SEM results showed that the influence of DOM components on the distribution of metal ions was greater than that of TDC and pH. Among the DOM components, the influence of humic-like substances was greater than that of protein-like substances. Additionally, DOM and TDC had direct positive effects on metal ions, while pH had a direct negative effect on these. These results further elucidated the geochemical interactions between DOM and metal ions in AMD-disturbed karst rivers, which will assist in the pollution prevention of metal ions in AMD.

Spatio-temporal assessment of water quality of a tropical decaying river in India for drinking purposes and human health risk characterization

River water pollution and water-related health problems are common issues across the world. The present study aims to examine the Jalangi River's water quality to assess its suitability for drinking purposes and associated human health risks. The 34 water samples were collected from the source to the mouth of Jalangi River in 2022 to depict the spatial dynamics while another 119 water samples (2012-2022) were collected from a secondary source to portray the seasonal dynamics. Results indicate better water quality in the lower reach of the river in the monsoon and post-monsoon seasons. Principal component analysis reveals that K+, NO3-, and total alkalinity (TA) play a dominant role in controlling the water quality of the study region, while, CaCO3, Ca2+, and EC in the pre-monsoon, EC, TDS, Na+, and TA in the monsoon, and EC, TDS and TA in the post-monsoon controlled the water quality. The results of ANOVA reveal that BOD, Ca2+, and CaCO3 concentrations in water have significant spatial dynamics, whereas pH, BOD, DO, Cl-, SO42-, Na+, Mg2+, Ca2+, CaCO3, TDS, TA, and EC have seasonal dynamics (p < 0.05). The water quality index depicts that the Jalangi River's water quality ranged from 6.23 to 140.83, i.e., excellent to unsuitable for drinking purposes. Human health risk analysis shows that 32.35% of water samples have non-carcinogenic health risks for all three groups of people, i.e., adults, children, and infants while only 5.88% of water samples have carcinogenic health risks for adults and children. The gradual decay of the Jalangi River coupled with the disposal of urban and agricultural effluents induces river pollution that calls for substantial attention from the various stakeholders to restore the water quality.

Water-lifting and aeration system improves water quality of drinking water reservoirs: Biological mechanism and field application

Reservoirs have been served as the major source of drinking water for dozens of years. The water quality safety of large and medium reservoirs increasingly becomes the focus of public concern. Field test has proved that water-lifting and aeration system (WLAS) is a piece of effective equipment for in situ control and improvement of water quality. However, its intrinsic bioremediation mechanism, especially for nitrogen removal, still lacks in-depth investigation. Hence, the dynamic changes in water quality parameters, carbon source metabolism, species compositions and co-occurrence patterns of microbial communities were systematically studied in Jinpen Reservoir within a whole WLAS running cycle. The WLAS operation could efficiently reduce organic carbon (19.77%), nitrogen (21.55%) and phosphorus (65.60%), respectively. Biolog analysis revealed that the microbial metabolic capacities were enhanced via WLAS operation, especially in bottom water. High-throughput sequencing demonstrated that WLAS operation altered the diversity and distributions of microbial communities in the source water. The most dominant genus accountable for aerobic denitrification was identified as Dechloromonas. Furthermore, network analysis revealed that microorganisms interacted more closely through WLAS operation. Oxidation-reduction potential (ORP) and total nitrogen (TN) were regarded as the two main physicochemical parameters influencing microbial community structures, as confirmed by redundancy analysis (RDA) and Mantel test. Overall, the results will provide a scientific basis and an effective way for strengthening the in-situ bioremediation of micro-polluted source water.

Brief status of contamination in surface water of rivers of India by heavy metals: a review with pollution indices and health risk assessment

Water is polluted via various means; among these, heavy metal (HM) contamination is of great concern because of the involvement of metal toxicity and its effect on aquatic environment. The significance and novelty of this study is that it focuses on assessment of HMs in the surface water of Indian rivers only from 1991 to 2021. For this, multivariate studies were used to find multiple sources of HMs. The average concentrations of Fe, Cr, Pb, Ni, Cd, Mn, Hg, Co, and As in surface water of rivers were found to far exceed the permitted limits established by both World Health Organisation and Bureau of Indian Standards. The HM indices like HM pollution, degree of contamination, evaluation index, water pollution, and toxicity load data all indicated that the rivers under investigation are heavily polluted by HMs. In this study, health risk assessment indicated non-carcinogenic effects of Fe, Cr, Cu, Pb, Cd, Mn, Hg, Co, and As in children and those of Fe, Cr, Pb, Cd, Hg, Co, and As in adults. Values investigated for Cancer index were higher for Cr, Pb, Ni, Cd, and As indicating a high risk of cancer development in adults and children via the ingestion pathway than the cutaneous pathway. Moreover, children are more prone to be exposed to both non-carcinogenic and carcinogenic effects of HMs than adults. To reduce human dangers, remediation approaches, such as environment-friendly, cost-effective adsorbents, phytoremediation and bio-remediation, as well as tools like bio-sensors, should be included in river management plans.

Distribution, sources, and pollution levels of toxic metal(loid)s in an urban river (Ichamati), Bangladesh using SOM and PMF modeling with GIS tool

Indexical assessment coupled with a self-organizing map (SOM) and positive matrix factorization (PMF) modeling of toxic metal(loid)s in sediment and water of the aquatic environment provides valuable information from the environmental management perspective. However, in northwest Bangladesh, indexical and modeling assessments of toxic metal(loid)s in surface water and sediment are still rare. Toxic metal(loid)s were measured in sediment and surface water from an urban polluted river (Ichamati) in northwest Bangladesh using an atomic absorption spectrophotometer to assess distribution, pollution levels, sources, and potential environmental risks to the aquatic environment. The mean concentrations (mg/kg) of metal(loid)s in water are as follows: Fe (871) > Mn (382) > Cr (72.4) > Zn (34.2) > Co (20.8) > Pb (17.6) > Ni (16.7) > Ag (14.9) > As (9.0) > Cu (5.63) > Cd (2.65), while in sediment, the concentration follows the order, Fe (18,725) > Mn (551) > Zn (213) > Cu (47.6) > Cr (30.2) > Ni (24.2) > Pb (23.8) > Co (9.61) > As (8.23) > Cd (0.80) > Ag (0.60). All metal concentrations were within standard guideline values except for Cr and Pb for water and Cd, Zn, Cu, Pb, and As for sediment. The outcomes of eco-environmental indices, including contamination and enrichment factors and geo-accumulation index, differed spatially, indicating that most of the sediment sites were moderately to highly polluted by Cd, Zn, and As. Cd and Zn content can trigger ecological risks. The positive matrix factorization (PMF) model recognized three probable sources of sediment, i.e., natural source (49.39%), industrial pollution (19.72%), and agricultural source (30.92%), and three possible sources of water, i.e., geogenic source (45.41%), industrial pollution (22.88%), and industrial point source (31.72%), respectively. SOM analysis identified four spatial patterns, e.g., Fe-Mn-Ag, Cd-Cu, Cr-Pb-As-Ni, and Zn-Co in water and three patterns, e.g., Mn-Co-Ni-Cr, Cd-Cu-Pb-Zn, and As-Fe-Ag in sediment. The spatial distribution of entropy water quality index values shows that the southwestern area possesses "poor" quality water. Overall, the levels of metal(loid) pollution in the investigated river surpassed a critical threshold, which might have serious consequences for the river's aquatic biota and human health in the long run.

Irrigation suitability, health risk assessment and source apportionment of heavy metals in surface water used for irrigation near marble industry in Malakand, Pakistan

Water is a vital, finite resource whose quantity and quality are deteriorating as the world population increases. The current study aims to investigate the concentration of heavy metals (HM) in surface water for irrigation purposes with associated human health risks and pollution sources near the marble industry in Malakand, Pakistan. Twenty-seven water samples were randomly collected and analyzed for HM concentration by inductively coupled plasma‒optical emission spectrometry (ICP‒OES). pH, electrical conductivity (EC), total dissolved solids (TDS), biological oxygen demand (BOD), and chemical oxygen demand (COD) were measured using standard methods of American Public Health Association (APHA). Irrigation suitability was assessed using specific water quality parameters. The associated health risks from ingestion and dermal exposure to heavy metals were calculated by USEPA health risk indices. Pollution sources and spatial distribution mapping were studied using compositional data analysis (CoDa) and the application of a geographic information system (GIS) to understand the changing behavior of heavy metals in surface waters. The concentrations of BOD (89%), COD (89%), Al (89%), Ca (89%), Cr (56%), Cu (78%), Fe (56%), K (34%) Mg (23%), Mn (56%), Na (89%), Ni (56%), P (89%), and Zn (11%) exceeded the safety limits of National Environmental Quality standards (NEQs) of Pakistan. The results of Kelly's ratio (KR) classified surface water as unsuitable for irrigation. The average daily doses (ADD, mg/kg/day) for Al, Cu, Cr, Fe, Mn, Ni, and Zn were higher in children than in adults. The hazard index (HI) for children and adults was above the threshold (HI > 1), indicating a significant risk of non-carcinogenic toxicity. The carcinogenic risk values for Cr and Ni were above the USEPA limit (1 × 10-6 to 1 × 10-4), suggesting a potential carcinogenic risk for the target population. Principal component analysis (PCA), biplot (CLR), and the CoDa-dendrogram allowed for the identification of elemental associations, and their potential source was anthropogenic rather than natural in origin. Regular monitoring and phytoremediation strategies are proposed to safeguard crops and human health.

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

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.

Copper nanoclusters-based fluorescent sensor array to identify metal ions and dissolved organic matter

In recent years, the prevention and control of water pollution has received extensive attention. There is a need to develop simple and effective strategies for the rapid detection of metal ions and dissolved organic matter (DOM) in order to improve water quality. To this end, the first copper nanoclusters (CuNCs)-based fluorescent sensor array was done to identify 12 metal ions (Pb²⁺, Fe³⁺, Cu²⁺, Cd²⁺, Cr³⁺, Co²⁺, Ni²⁺, Zn²⁺, Ag⁺, Fe²⁺, Hg²⁺, and Al³⁺) and DOM (humic substances, lipids, fatty acids, amino acids, and lignans). The results revealed that CuNCs that were synthesized with polyethyleneimine (PEI), histidine (His), and glutathione (GSH) exhibited different binding abilities to metal ions and DOM. These unique fluorescence responses were analyzed using principal component analysis (PCA) and linear discriminant analysis (LDA) to identify metal ions and DOM in the buffer. The aforementioned 12 metal ions were classified at a limit concentration of 1.5 μM. Moreover, quantification of metal ions was achieved even at a low concentration of 0.83 μM (Zn²⁺). This array also worked well in the recognition of metal ions in tap water as well as distinguishing riverine and seawater samples of different regions, which was of great significance in environmental monitoring.

Interactions between anthropogenic pollutants (biodegradable organic nitrogen and ammonia) and the primary hydrogeochemical component Mn in groundwater: Evidence from three polluted sites

Anthropogenic pollutants (organic nitrogen and ammonia) can change the dynamic balances of hydrogeochemical components of groundwater, and this can affect the fates of the pollutants and groundwater quality. The aim of this paper is to assess the long-term impact of pollutants on groundwater component concentrations and species in three sites that has been polluted with illegal discharge wastewater containing organic nitrogen and ammonia, in order to reveal the interactions between nitrogen species and Mn. We analyzed semi-monthly groundwater data from three sites in northwestern China over a long period of time (2015-2020) by using statistical analyses, correlation analyses, and a correlation co-occurrence network method. The results showed that wastewater entering groundwater from surface changed the hydrogeochemical component concentrations and species significantly. The main form of inorganic nitrogen species changed from nitrate to ammonia. The Mn concentration increased from undetectable (<0.01 mg/L) to 1.64 mg/L (the maximum), which surpassed the guideline value suggested by China and WHO. The main mechanism for Mn increase is the reductive dissolution of Mn oxide caused by the oxidation of organic nitrogen. Mn‑nitrogen species interaction complicates the transformation of nitrogen components. Chemoautotrophic denitrification and dissimilatory nitrate reduction to ammonium (DNRA) mediated by Mn are the major mechanisms of nitrate attenuation when dissolved oxygen is greater than 2 mg/L. Mn oxides reductive dissolution and reoxidation of Mn by nitrate reduction cause Mn to circulate in groundwater. The results provide field evidence for interactions between nitrogen species transformation and Mn cycle in groundwater. This has important implications for pollution management and groundwater remediation, particularly monitored natural attenuation.

Heavy metals contamination and associated health risks in food webs-a review focuses on food safety and environmental sustainability in Bangladesh

Heavy metals occur naturally in very small amounts in living organisms, but exposure to their higher concentrations is hazardous. Heavy metals at hazardous levels are commonly found in foodstuffs of Bangladesh, mainly due to the lack of safety guidelines and poor management of industrial effluents. Several lines of evidence suggest that the level of heavy metals in foodstuffs of Bangladesh is higher than the acceptable limits set by World Health Organization/Food and Agriculture Organization. Literature survey revealed that the sources and transport pathways of heavy metals in the ecosystem and the abundance of heavy metals in the food products of Bangladesh are potential threats to food safety. However, an extensive assessment of the toxicity of heavy metals in food webs is lacking. Although widespread heavy metal contamination in various foodstuffs and environmental matrices have been summarized in some reports, a critical evaluation regarding multi-trophic transfer and the health risk of heavy metal exposure through food chain toxicity in Bangladesh has not been performed. This systematic review critically discussed heavy metal contamination, exposure toxicity, research gaps, existing legislation, and sustainable remediation strategies to enhance Bangladesh's food safety. In particular, this study for the first time explored the potential multi-trophic transfer of heavy metals via food webs in Bangladesh. Furthermore, we recommended a conceptual policy framework to combat heavy metal contaminations in Bangladesh.

The Optical Characterization and Distribution of Dissolved Organic Matter in Water Regimes of Qilian Mountains Watershed

The constituents and content of dissolved organic matter (DOM) in the Qilian Mountain watershed were characterized with a spectroscopic technique, especially 3-DEEM fluorescence assisted by parallel factor (PARAFAC) analysis. The level of DOM in the surrounding area of Qinghai lake (thereafter the lake in this article specifically refers to Qinghai Lake)was highest at 9.45 mg C·L^-1 and about 3 times less (3.09 mg C·L^-1) in a cropland aquatic regime (the lowest value). In general, DOM was freshly autochthonously generated by plankton and plant debris, microorganisms and diagenetic effects in the aquatic environment (FI > 1.8). Component 1 (humic acid-like) and 3 (fulvic acid-like) determined the humification degree of chromophoric dissolved organic matter (CDOM). The spatial variation of sulfate and nitrate in the surrounding water regime of the lake revealed that organic molecules were mainly influenced by bacterial mediation. Mineral disintegration was an important and necessary process for fluorescent fraction formation in the cropland water regime. Exceptionally, organic moiety in the unused land area was affected by anespecially aridclimate in addition to microbial metabolic experience. Salinity became the critical factor determining the distribution of DOM, and the total normalized fluorescent intensity and CDOM level were lower in low-salinity circumstances (0.2-0.5 g·L^-1) with 32.06 QSU and 1.38 m^-1 in the grassland area, and higher salinity (0.6~0.8 g·L^-1) resulted in abnormally high fluorescence of 150.62 QSU and absorption of 7.83 m^-1 in the cropland water regime. Climatic conditions and microbial reactivity controlled by salinity were found to induce the above results. Our findings demonstrated that autochthonous inputs regulated DOM dynamics in the Qilian Mountains watershed of high altitude.

Application of novel framework approach for prediction of nitrate concentration susceptibility in coastal multi-aquifers, Bangladesh

This study aims to construct a novel framework approach for predicting and mapping nitrate concentration susceptibility in the coastal multi-aquifers of Bangladesh by coupling the K-fold cross-validation method and novel ensemble learning algorithms, including Boosting, Bagging and Random Forest (RF). In total, 286 nitrate sampling sites were employed in the model work. The dataset was demarcated into a 75:25 ratio for model construction (75% 3-fold ≅ 214 sites) and (25% 1-fold ≅ 72 sites) for model validation using the 4-fold cross-validation schemes. A total of 14 groundwater causative factors including salinity, depth, pH, EC, As, HCO₃^-, F^-, Cl^-, SO₄^2-, PO₄^2-, Na⁺, K⁺, Mg²⁺, and Ca²⁺ were adopted for the construction of the proposed models. OneR relative importance model was employed to choose and rank critical factors for spatial nitrate modeling. The results showed that depth, pH and As are the most influential causative factors in the elevated nitrate concentration in groundwater. Based on the model assessment criteria such as receiver operating characteristic (ROC)'s AUC (area under curve), sensitivity, specificity, accuracy, precession, F score, and Kappa coefficient, the Boosting model outperforms others (r = 0.92, AUG ≥ 0.90) in mapping nitrate concentration susceptibility, followed by Bagging and RF models. The results of mapping nitrate concentration also demonstrated that the south-central and western regions had an elevated amount of nitrate content than other regions due to depth variation in the study area. During our sampling campaign, we observed hundreds of fish hatcheries operation, a fish landing center and aquaculture farms which are the reasons for overexploitation and excessive agrochemicals used in the study area. Thus, the dependability of ensemble learning modeling verifies the effectiveness and applicability of the proposed novel approach for decision-makers in groundwater pollution management at the local and regional levels.

Characterization of groundwater hydrogeochemistry, quality, and associated health hazards to the residents of southwestern Bangladesh

In this research, we intended to appraise the hydrogeochemistry and human health risks of groundwater (GW) in southwestern Bangladesh, applying hydrogeochemical techniques, GW quality index (GWQI), several pollution indices, and mathematical health risk models. The trace elements (TEs) and ionic composition of GW samples were analyzed by atomic absorption spectroscopy and ion chromatography (AAS-IC) technique. The evaporate dissolution, silicate weathering, and ionic exchange processes control the hydrogeochemistry in GW. The GWQI revealed that 34% of samples were poor to very poor quality for drinking purposes, whereas irrigation water quality indices suggested moderate suitability of GW. The mean hazard quotient (HQ) and hazard index (HI) exceeded the tolerable level for adults and children, making substantial chronic health impacts on humans. The estimated carcinogenic risk of As and Pb surpassed the upper level of 1 × 10^-4 for both aged populations. Overall, the results indicate that the local inhabitants have detrimental health risks; hence, effective regulation and proper measures should be concentrated for continuous monitoring, assessment, and remediation of As, Mn, Pb, and Hg in the study area.

Preliminary assessment of heavy metals in surface water and sediment in Nakuvadra-Rakiraki River, Fiji using indexical and chemometric approaches

River water and sediment embody environmental characteristics that give valuable environmental management information. However, indexical and chemometric appraisal of heavy metals (HMs) in river water and sediment is very scarce in Island countries including Fiji. In this research, forty-five sediment and fifteen water samples from the Nakuvadra-Rakiraki River, Fiji were analyzed for appraising spatial distribution, pollution, and source identification of selected heavy metals (HMs) using the coupling tools of self-organizing map (SOM), compositional data analysis (CDA), and sediment and water quality indices. The mean concentration of HMs increased in the order of Cd < Co < Pb < Cu < Zn < Ni < Cr < Mn < Fe for sediment and Cd < Pb < Cu < Ni < Zn < Co < Cr < Fe < Mn for water, respectively. The outcomes of the enrichment factor, geo-accumulation index and contamination factor index varied spatially and most of the sediment samples were polluted by Pb, Mn, and Cu. The potential ecological risk recognized Cd, and Pb as ecological and public health risks to the surrounding communities. Based on SOM and CDA, three potential sources (e.g., point, nonpoint and lithological sources) of HMs for sediment and two sources (e.g., geogenic and human-induced sources) of HMs for water were identified. The spatial patterns of EWQI values revealed that the northern and northeast zones of the studied area possess a high degree of water pollution. The entropy weight indicated Ni and Cd as the main pollutants degrading the water quality. This study gives a baseline dataset for combined eco-environmental measures for the river's water and sediment pollution as well as contributes to an inclusive appraisal of HMs contamination in global rivers.

Water resources pollution associated with risks of heavy metals from Vatukoula Goldmine region, Fiji

Although mining is essential for human economic development, is amongst the most polluting anthropogenic sources that influence seriously in water resources. Thus, understanding the presence and concentration of heavy metals in water and sediment in the vicinity of mines is important for the sustainability of the ecosystem. In this work, a multidisciplinary approach was developed to characterize the contamination level, source apportionment, co-existence, and degree of ecological and human health risks of HMs on water resources in the Vatukoula Goldmine region (VGR), Fiji. The outcomes suggested significant contamination by Cd (range: 0.01-0.95 g/L), Pb (range: 0.03-0.53 g/L), and Mn (range: 0.01-3.66 g/L) in water samples surpassed the level set by Fiji and international laws, whereas higher concentration of Cd (range: 2.60-23.16 mg/kg), Pb (range: 28.50-200.90 mg/kg) and Zn (range: 36.50-196.66 mg/kg) were detected in sediment samples. Lead demonstrated a strong significant co-existence network with other metals (e.g., Mn, Ni). Source apportionment recognized four source patterns (Cd, Pb, Ni, and Mn) for water and (Cr, Cd-Pb, Mn, and Zn) for sediment which was further confirmed by principal component analysis. The mine inputs source mainly contributed to Cd (66.07%) for water, while mineral processing mostly contributed to Zn (76.10%) for sediment. High non-carcinogenic (>1) and carcinogenic (>10^-4) health risks, particularly in children, are related to the elevated Cd, Pb and Cr contents from the VGR. Uncertainty analysis demonstrates that the 90th quantile of Cd led to higher carcinogenic risk. Pollution indices disclosed a moderate to extremely contamination status mainly along the Toko dam which poses high ecological risks identified by index calculation. However, sediment quality indicators based on probable effect levels showed that there was a 75% of likelihood that the concentrations of Cd and Pb adjacent to the VGR have a severe toxic impact on aquatic lives.