Long-term exposure to chromium contaminated waters and the associated human health risk in a highly contaminated industrialised region

Groundwater quality assessment using machine learning models: a comprehensive study on the industrial corridor of a semi-arid region

Water plays a significant role in sustaining the lives of humans and other living organisms. Groundwater quality analysis has become inevitable, because of increased contamination of water resources and global warming. This study used machine learning (ML) models to predict the water quality index (WQI) and water quality classification (WQC). Forty groundwater samples were collected near the Ranipet industrial corridor, and the hydrogeochemistry and heavy metal contamination were analyzed. WQC prediction employed random forest (RF), gradient boosting (GB), decision tree (DT), and K-nearest neighbor (KNN) models, and WQI prediction used extreme gradient boosting (XGBoost), support vector regressor (SVR), RF, and multi-layer perceptron (MLP) models. The grid search method is used to evaluate the ML model by F1 score, accuracy, recall, precision, and Matthews correlation coefficient (MCC) for WQC and the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), and median absolute percentage error (MAPE) for WQI. The WQI results indicate that the groundwater quality of the study area is very poor and unsuitable for drinking or irrigation purposes. The performance metrics of the RF model excelled in predicting both WQC (accuracy = 97%) and WQI (R2 = 91.0%), outperforming other models and emphasizing ML's superiority in groundwater quality assessment. The findings suggest that ML models perform well and yield better accuracy than conventional techniques used in groundwater quality assessment studies.

Evaluating hydrogeochemistry and heavy metal contamination of groundwater at Ranipet environs: employing multivariate statistics, agricultural indices, and health risk assessment

Water plays an essential role in sustaining life on Earth as an indispensable natural resource. In recent decades, dependence on groundwater for domestic and industrial purposes has become inevitable. The Ranipet industrial environs (RIE) has many tanneries and chemical industries, which affects the groundwater quality. This study assessed groundwater quality and its suitability for domestic, agricultural, and human health risk assessments. 40 groundwater samples (28 open wells and 12 bore wells) were collected during pre-monsoon 2022 and analyzed by employing multivariate statistics, standard scatter plots, irrigation indices, and health risk assessment. The results of hydrogeochemical analysis and multivariate statistics affirmed that electrical conductivity (EC), total dissolved solids (TDS), calcium (Ca2+), and magnesium (Mg2+) have controlled the hydrochemistry of RIE. Cadmium (Cd) at 46% and chromium (Cr) at 33% have contaminated the groundwater in the study area, making it unsuitable for human consumption and irrigation. The agricultural indices analysis results show groundwater quality ranging from very poor to unsuitable making it unsuitable and also affects crop productivity. Hazard index (HI) results infer that Cr and Cd severely contaminated the RIE's groundwater, encompassing 14 villages, making the groundwater unfit for drinking, domestic use, and irrigation. Hazard quotient (HQ) and incremental lifetime cancer risk (ILCR) analysis revealed that 2 in 100 infants and 3 in 1000 people over the age of 63 are likely to develop cancer due to Cr and Cd in the REI. This is a need-of-the-hour problem, addressing this issue with preventive measures to ensure the protection of groundwater sources will lead to achieving the Sustainable Development Goal 6 (Clean Water and Sanitation).

Groundwater Contamination Monitoring for Pollution Measurement and Transmission Applying WQI Approaches from a Region of the Erode District, Tamilnadu, India

The present research deals with the Risk assessment of groundwater quality. 79 groundwater samples were collected from domestic and agricultural usage open and bore wells during January 2021(COVID-19 Pandemic Period). Groundwater samples were tested to determine the physicochemical parameters using standard testing procedure for the preparation of spatial distribution maps of each parameter based on the World Health Organization (WHO) standard. Multivariate statistical analysis has shown the source of groundwater pollution from secondary leaching of chemical weathering of rocks. From the Water Quality Index and bivariate plot reveals that less than 20% of the area comes under high and very high-risk zone. The types of hardness diagram showed 32.91% of the samples fall in hard brackish water as illustrated by the Piper trilinear diagram. The research outcome result shows that the least percentage of industrials effluents due to the COVID-19 pandemic, not working for all industries during lock down period.

Differentiating Wild and Apiary Honey by Elemental Profiling: a Case Study from Mangroves of Indian Sundarban

Honey is a natural substance produced by honeybees from the nectar or secretion of flowering plants. Along with the botanical and geographical origin, several environmental factors also play a major role in determining the characteristics of honey. The aim of this study is to determine and compare the elemental concentration of various macro and trace elements in apiary and wild honeys collected from different parts of Indian Sundarbans. The elemental analysis was performed in inductively coupled plasma optical emission spectroscopy preceded by microwave digestion method. The concentrations of 19 elements (Ag, Al, As, B, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Ni, Pb, Se and Zn) were investigated from thirteen locations of Indian Sundarbans. This comparative study shows in wild honey samples, the concentration of K was highest followed by Ca, Mg and Na and Zn was lowest among all. In contrast, in apiary honey samples, Ca had maximum concentration followed by K, Mg and Na and Ag had minimum among all. The elemental concentration in honey from apiary was either equal or higher than their wild counterpart. The results of the factor analysis of PCA algorithm for wild and apiary honey samples were highly variable which implies that the elements are not coming from the same origin. The concentration of element was found to be highly variable across sites and across sources of honey samples.

Complexation behaviour and removal of organic-Cr(III) complexes from the environment: A review

Chromium (Cr) is mainly found in the form of organic-Cr(III) complexes in the natural environment and industrial waste. The widespread existence of composite contaminants composed of organic matter (OM) and Cr pose a serious ecological threat, and its potential interaction and removal need to be further summarised. Organic ligands, such as carbohydrates, nitrogen compounds, phenolic compounds, humus substances (HS), and low molecular weight organic acids (LMWOAs), play an important role in governing the speciation, mobility, and absorption and desorption of Cr in the environment. Moreover, growing evidence indicates that oxygen-containing functional groups (e.g., carboxyl, hydroxyl, and phosphate) are closely related to the complexation of Cr(III). Advanced oxidation processes (AOPs) are efficient and widely applicable technologies. However, the re-complexation of oxidation intermediates with Cr(III) and the formation and accumulation of much more toxic Cr(VI) species hinder the possible utilisation of AOPs. In this paper, the sources and harmful effects of organic-Cr(III) complexes are reported in detail. The complexation behaviour and structure of the organic-Cr(III) complexes are also described. Subsequently, the application of AOPs in the decomplexation and degradation of organic-Cr(III) complexes is summarised. This review can be helpful for developing technologies that are more efficient for organic-Cr(III) complex removal and establishing the scientific background for reducing Cr discharge Cr into the environment.

Impact of copper mining wastes in the Amazon: Properties and risks to environment and human health

Improper disposal of copper mining wastes can threaten the ecosystem and human health due to the high levels of potentially toxic elements released into the environment. The objective of this study was to determine the properties of Cu mining wastes generated in the eastern Amazon and their potential risks to environment and human health. Samples of forest soil and artisanal/industrial Cu mining wastes were collected and subjected to characterization of properties and pseudo-total concentrations of Al, As, Ba, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, and Zn, in addition to chemical fractionation of Cu. The pH ranged from near neutrality to alkaline. Pseudo-total concentrations of Cu were high in all wastes, mainly in the artisanal rock waste, with 19,034 mg kg^-1, of which 61% is concentrated in the most reactive fractions. Pollution indices indicated that the wastes are highly contaminated by Cu and moderately contaminated by Cr and Ni. However, only the artisanal rock waste is associated with environmental risk. Non-carcinogenic and carcinogenic human health risks were detected, especially from exposure to Cr in the artisanal rock waste. Prevention actions and monitoring of the artisanal mining area are necessary to avoid impacts to the local population.

Spatial distribution and health risk assessment of dissolved heavy metals in groundwater of eastern China coastal zone

Environmental changes and human activities have deteriorated the quality of groundwater, which is an important source of freshwater in coastal areas. The Jiangsu Coastal Zone (JCZ), which is a typical area of the eastern China coastal zone (ECCZ), has a great demand for clean water resources due to its dense population. The groundwater in the JCZ is affected by both human activities and seawater intrusion. However, research on heavy metals in the groundwater of the JCZ is limited. This study investigated the spatial distribution characteristics and influencing factors of heavy metals in coastal groundwater of Jiangsu Province and conducted a health risk assessment (HRA). Relatively high concentrations of Cu, Cd, Pb, Co, Zn, and Ba existed in the northern JCZ, while As and B predominated in the central JCZ. The main heavy metal pollutants in the groundwater are B and As, with mean values at 0.61 mg/L and 0.02 mg/L, exceeding the standard rate reaching 48.28% and 18.07% respectively. The HRA results showed that B had the largest hazard quotient (HQ), accounting for 50.22% of the total HQs, and As was attributed to the pollutant with the largest cancer risk (CR), accounting for 99.74% of the total CRs. According to the results of the correlation analysis, heavy metals in the groundwater of JCZ mainly originated from industrial pollution, seawater intrusion, and mineral dissolution. Seawater intrusion increases the content of As and B in groundwater, leading to higher health risks. Therefore, the government should strengthen the supervision of seawater intrusion by implementing more effective water resource management policies, or adopting engineering measures such as installing subsurface physical barriers to prevent and control seawater intrusion.