Heavy Metals in Groundwater of Southern Italy: Occurrence and Potential Adverse Effects on the Environment and Human Health

Trace element pollution tracking in the complex multi-aquifer groundwater system of Al-Hassa oasis (Saudi Arabia) using spatial, chemometric and index-based techniques

In a global context, trace element pollution assessment in complex multi-aquifer groundwater systems is important, considering the growing concerns about water resource quality and sustainability worldwide. This research addresses multiple objectives by integrating spatial, chemometric, and indexical study approaches, for assessing trace element pollution in the multi-aquifer groundwater system of the Al-Hassa Oasis, Saudi Arabia. Groundwater sampling and analysis followed standard methods. For this purpose, the research employed internationally recognized protocols for groundwater sampling and analysis, including standardized techniques outlined by regulatory bodies such as the United States Environmental Protection Agency (USEPA) and the World Health Organization (WHO). Average values revealed that Cr (0.041) and Fe (2.312) concentrations surpassed the recommended limits for drinking water quality, posing serious threats to groundwater usability by humans. The trace elemental concentrations were ranked as: Li < Mn < Co < As < Mo < Zn < Al < Ba < Se < V < Ni < Cr < Cu < B < Fe < Sr. Various metal(loid) pollution indices, including degree of contamination, heavy metal evaluation index, heavy metal pollution index, and modified heavy metal index, indicated low levels of groundwater pollution. Similarly, low values of water pollution index and weighted arithmetic water quality index were observed for all groundwater points, signifying excellent groundwater quality for drinking and domestic purposes. Spatial distribution analysis showed diverse groundwater quality across the study area, with the eastern and western parts displaying a less desirable quality, while the northern has the best, making water users in the former more vulnerable to potential pollution effects. Thus, the zonation maps hinted the necessity for groundwater quality enhancement from the western to the northern parts. Chemometric analysis identified both human activities and geogenic factors as contributors to groundwater pollution, with human activities found to have more significant impacts. This research provides the scientific basis and insights for protecting the groundwater system and ensuring efficient water management.

Electrochemical sensor based on gum Arabic nanoparticles for rapid and in-situ detection of different heavy metals in real samples

The key solution to combat trace metal pollution and keep the environment, ecosystem, animals, and humans safe is earlier and rapid trace metal detection. For all these reasons, we propose in this work the design of a simple electrochemical sensor functionalized with green nanoparticles for electrochemical detection of the fourth most dangerous heavy metal ions namely copper, zinc, lead, and mercury. The green nanoparticles are fabricated by a one-step, consisting of reducing platinum nanoparticles by a natural gum Arabic polymer. To guarantee the success of these nanoparticles' design, the nanoparticles have been characterized by Fourier-transform infrared spectroscopy FTIR, and thermogravimetric TGA techniques. While, for the electrochemical characterization, we have adopted cyclic voltammetry CV and electrochemical impedance spectroscopy EIS to control different steps of surface modification, and the differential pulse anodic stripping DPAS was monitored to follow up the electrochemical detection of different heavy metals.

Results

have confirmed the good chemical and physical properties of the elaborated nanoparticles. As, the developed sensor showed a specific electrochemical response toward the heavy metal ions separately, with a lower limit of detection lower LOD than that recommended by the World Health Organization, in order of 9.6 ppb for Cu2+, 1.9 ppb for Zn2+, 0.9 ppb for Hg2+, and 4.2 ppb for Pb2+. Impressively, the elaborated sensor has demonstrated also high stability, outstanding sensitivity, and excellent analytical performance.In addition, the elaborated analytical tool has been successfully applied to the determination of various heavy metal ions in real samples, reflecting then its promising prospect in practical application.

Trace elements in the muscle and liver tissues of Garra shamal from the freshwater ecosystem of Oman: an exposure risk assessment

Anthropogenic activities lead to environmental contamination with foreign substances such as heavy metals. This work was aimed to monitor trace elements (total arsenic (As), cadmium (Cd), chrome (Cr), cobalt (Co), copper (Cu), lead (Pb), manganese (Mn), mercury (Hg), nickel (Ni), and zinc (Zn)) contamination levels (dry weight base) in three natural freshwater reservoirs of Oman including Al Khawd and Al Amarat (Muscat Governorate) and Surur area (Ad Dakhiliyah Governorate as control area) using a native benthic inland fish (Garra shamal; Cyprinidae) for the first time. The muscle and liver of a hundred and twenty G. shamal were collected to assess the degree of metal contamination. Atomic absorption spectrometry was used as an analytical technique. From the spectrum of analyzed elements, we found Zn as a major element in monitored areas. The statistically significant (P < 0.05) highest concentrations of Zn liver (0.275 ± 0.065 µg/g) were in Al Amarat compared to the other areas. The concentrations of monitored elements in the fish muscle were lower than the liver samples. Furthermore, the fish length was significantly correlated with the accumulation of Hg and Co in both muscle and liver samples. In all analyzed fish from Oman inland water, the concentrations of elements were below the permissible limits; however, additional research is needed.

Assessing Heavy Metals in the Sele River Estuary: An Overview of Pollution Indices in Southern Italy

Rapid industrialization, coupled with a historical lack of understanding in toxicology, has led in an increase in estuary pollution, frequently resulting in unexpected environmental situations. Therefore, the occurrence of heavy metals (HMs) constitutes a major environmental issue, posing a serious risk both to aquatic ecosystems and public health. This study aimed to evaluate the levels of eight HMs (As, Hg, Cd, Cr, Cu, Ni, Pb, and Zn) in water, suspended particles, and sediment near the Sele River estuary (Italy) in order to assess their environmental impacts on the sea and health risks for humans. The results revealed an increasing order of HM concentration according to the scheme suspended particulate matter (SPM) > sediment (SED) > dissolved phase (DP) and a moderate contamination status in sediment. The health risk assessment indicated that the non-carcinogenic risk was negligible. Carcinogenic risk, expressed as the incremental lifetime cancer risk (ILCR), was negligible for Cd and Ni and within tolerable limits for As, Pb, and Cr. The findings suggested that, even if there are currently no specific limits for chemical parameters in the transitional waters of Italy, monitoring systems should be implemented to determine pollution levels and implement effective steps to improve river water quality and reduce human health risks.

Viability of two adaptive fuzzy systems based on fuzzy c means and subtractive clustering methods for modeling Cadmium in groundwater resources

The Adaptive Neuro-Fuzzy Inference System (ANFIS) combines the strengths of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) into a single framework. By doing so, it allows for quicker learning and adaptable interpretation capabilities, which are useful for modeling complex patterns and identifying nonlinear relationships. One significant challenge in assessing water quality is the difficulty and time-consuming nature of determining the various factors that impact it. Given this situation, predicting heavy metal levels in groundwater resources, both urban and rural, is essential. This paper investigates two methods, ANFIS-FCM and ANFIS-SUB, to determine their effectiveness in modeling Cadmium (Cd) in groundwater resources. ‏The parameters to be considered are: dissolved solids (TDS), electroconductivity (EC), turbidity (TU), and pH were assumed to be the independent variables. A total of 51 sampling location were used with in the groundwater resource were used to develop the fuzzy models. For evaluating the performance of ANFIS-FCM and ANFIS-SUB models, three different performance criteria including the correlation coefficient, root mean square error, and sum square error were used for comparing the model outputs with actual outputs‏.‏ Based on the obtained results from scatter plots of actual and predicted value by ANFIS-SUB and ANFIS- FCM models, the determination coefficient (R²) value for total data, test and train sets is equal to 0.978, 0.982, 0.993 and to 0.983, 0.999 and 0.998 respectively. This result proved the Cd predictions of the implemented ANFIS-FCM model was significantly close to the measured all experimental data with R² of 0.983. The performance of the implemented ANFIS-FCM model was compared with the ANFIS-SUB model and it is found that the ANFIS-FCM provided slightly higher accuracy than the ANFIS-SUB model. Also, the results obtained from the comparison between the predicted and the actual data indicated that the ANFIS-FCM and ANFIS-SUB have a strong potential in estimating the heavy metals in the groundwater with a high degree of accuracy.