Environmental Assessment of Potentially Toxic Elements Using Pollution Indices and Data-Driven Modeling in Surface Sediment of the Littoral Shelf of the Mediterranean Sea Coast and Gamasa Estuary, Egypt

Metal contamination in the Ashtamudi Wetland ecosystem: Source identification, toxicological risk assessment of Ni, Cd, Cr, and Pb and remediation strategies

This study examines the presence of potentially toxic elements (PTEs) in the surface sediments and water of the Ashtamudi wetland, a Ramsar site on India's southwest coast. The average concentration of PTEs in water(μg/L) and in sediments (mg/kg) follows the order Fe(147.89) > Zn(107.53) > Cu(5.73) > Pb(4.57) > Mn(4.41) > Ni(3.07) > Cr(2.98) > Cd(0.32) > Co(0.14) and Fe (37,311.91) > Mn (341.59) > Zn (147.97) > Cr (88.07) > Ni (74.24) > Cu (42.23) > Pb (30.84) > Co (15.61) > Cd (1.85) respectively. Contamination and ecological risk indices (e.g., EF, CF, Igeo, mCd, EI, RI, mHQ, TRI, PLI) reveal moderate to considerable ecological hazards and contamination. Health risk assessments identify elevated cancer risks associated with Ni, Cd, Pb, and Cr in high-contamination zones. Statistical tools (PCC, PCA, and HCA) elucidate pollution sources and sediment dynamics, showing that urban runoff and industrial discharge are the major contributors. In contrast to previous studies, this work integrates seasonal variations, advanced risk indices health risk assessments and remediation techniques, which are critical for sustainable management. The findings thus call for targeted remediation strategies to mitigate heavy metal contamination and safeguard the ecological integrity and public health of Ashtamudi Wetland.

Comprehensive evaluation and prediction of groundwater quality and risk indices using quantitative approaches, multivariate analysis, and machine learning models: An exploratory study

Assessing and predicting quality of groundwater is crucial in managing groundwater availability effectively. In the current study, groundwater quality was thoroughly appraised using various indexing methods, including the drinking water quality index (DWQI), pollution index of heavy metals (HPI), pollution index (PI), metal index (MI), degree of contamination (Cd), and risk indicators, like hazard quotient (HQ) and total hazard indicator (HI). The assessments were augmented through multivariate analytical techniques, models based on recurrent neural networks (RNNs), and integration of geographic information system (GIS) technology. The analysis measured physicochemical parameters across 48 groundwater wells from El-Menoufia region, revealing distinct water types influenced by ion exchange, rock-water interactions, and silicate weathering. Notably, the groundwater showed elevated levels of certain metals, particularly manganese (Mn) and lead (Pb), exceeding the drinking water limits. The DWQI deemed the bulk of the tested samples suitable for consumption, assigning them to the "good" category, whereas a small number were considered inferior quality. The HPI, MI, and Cd indices indicated significant pollution in the central study region. The PI revealed that Pb, Mn, and Fe were significant contributors to water pollution, falling between classes IV (strongly affected) and V (seriously affected). HQ and HI analyses identified the central area of the study as particularly prone to metal contamination, signifying a high risk to children via oral and dermal routes and to adults through oral exposure alone (non-carcinogenic risk). The adults had no health risks due to dermal contact. Finally, the RNN simulation model effectively predicted the health and water quality indices in training and testing series. For instance, the RNN model excelled in predicting the DWQI, with three key parameters being crucial. The model demonstrated an excellent fit on the training set, achieving an R2 of 1.00 with a very low root mean of squared error (RMSE) of 0.01. However, on the testing set, the model's performance slightly decreased, showing an R2 of 0.96 and an RMSE of 2.73. Regarding HPI, the RNN model performed exceptionally well as the primary predictor, with R2 values of 1.00 (RMSE = 0.01) and 0.93 (RMSE = 27.35) for the training and testing sets, respectively. This study provides a unique perspective for improving the integration of various techniques to gain a more comprehensive understanding of groundwater quality and its associated health risks, with a strong focus on feature selection strategies to enhance model accuracy and interpretability.

Anthropogenic and climatic impacts on historic sediment, carbon, and phosphorus accumulation rates using 210Pbex and 137Cs in a sub-watershed linked to Zarivar Lake, Iran

To estimate a watershed's response to climate change, it is crucial to understand how human activities and climatic extremes have interacted over time. Over the last century, the Zarivar Lake watershed, Iran, has been subjected to various anthropogenic activates, including deforestation and inappropriate land-management practices alongside the implementation of conservation measures like check dams. To understand the effects of these changes on the magnitude of sediment, organic carbon (OC), and phosphorus supplies in a small sub-watershed connected to the lake over the last century, a lake sediment core was dated using 210Pbex and 137Cs as geochronometers. The average mass accumulation rate (MAR), organic carbon accumulation rates (OCAR), and particulate phosphorus accumulation rates (PPAR) of the sediment core were determined to be 6498 ± 2475, 205 ± 85, and 8.9 ± 3.3 g m-2 year-1, respectively. Between the late 1970s and early 1980s, accumulation rates were significantly higher than their averages at 7940 ± 3120, 220 ± 60, and 12.0 ± 2.8 g m-2 year-1 respectively. During this period, the watershed underwent extensive deforestation (12%) on steep slopes, coinciding with higher mean annual precipitations (more than double). Conversely, after 2009, when check dams were installed in the sub-watershed, the sediment load to the lake became negligible. The results of this research indicate that anthropogenic activities had a pronounced effect on MAR, OCAR, and PPAR, causing them to fluctuate from negligible amounts to values twice the averages over the last century, amplified by climatic factors. These results imply that implementing climate-smart watershed management strategies, such as constructing additional check dams and terraces, reinforcing restrictions on deforestation, and minimum tillage practices, can facilitate protection of lacustrine ecosystems under accelerating climate change conditions.

Effect of Moringa oleifera Seeds Powder on Metallic Trace Elements Concentrations in a Wastewater Treatment Plant in Senegal

A wastewater treatment plant (WWTP) prototype coupled with Moringa oleifera seeds (MOSs) was developed to evaluate its effectiveness to reduce metallic trace elements (MTEs) in domestic wastewater. The WWTP is composed of a septic tank (F0) where wastewater is treated by biological processes under anaerobic conditions, followed by a bacterial filter (F1) where wastewater is filtered under aerobic conditions, followed by an infiltration well (F2), which provides additional filtration of wastewater before discharge into the soil. MTEs present in waters can bind with humic substances contained in colloid particles and then be eliminated by coagulation-flocculation with a cationic polyelectrolyte. MOSs contain positively charged cationic polymers that can neutralize the colloids contained in waters, which are negatively charged. Based on this observation, 300 mg·L-1 of MOS was added into F0, 50 mg·L-1 into F1, and 50 mg·L-1 into F2 mg·L-1. MOS activation in samples was performed by stirring rapidly for 1.5 min, followed by 5 min of gentle stirring and 3 h of settling. The data analysis shows that wastewater samples had significant concentrations of MTEs, particularly for Cu, Ni, Sr, and Ti, and sediment samples had high amounts of Cr, Cu, Ni, Sr, Ti, and V. The addition of MOS to F0, F1, and F2 samples resulted in reductions in MTE concentration of up to 36%, 71%, 71%, 29%, 93%, 81%, 13%, 52%, and 67% for Co, Cr, Cu, Ni, Pb, Se, Sr, Ti, and V, respectively. The quantified MTEs (As, Co, Cr, Cu, Ni, Pb, Se and V) in treated samples were reported to be lower than UN-EP standards for a safe reuse for irrigation and MOS proved to be as effective as chemical coagulants such as lime and ferric iron for the removal of MTEs contained in wastewater. These results highlight the potential of MOSs as natural coagulants for reducing MTE content in domestic wastewater. This study could be the first to evaluate the effectiveness of MOS in reducing 10 MTEs, including As, Co, Se, Sr, Ti, and V, which are currently understudied. It could also provide a better understanding of the origin of MTEs found in domestic wastewaters and how an effective treatment process can result in high-quality treated wastewaters that can be reused for irrigation without posing health or environmental risks. However, more research on MOSs is needed to determine the type and composition of the coagulant substance found in the seeds, as well as the many mechanisms involved in the decrease in MTEs by MOSs, which is currently understudied. A better understanding of MOS structure is required to determine the optimum alternative for ensuring the optimal effect of MOS paired with WWTP in removing MTEs from domestic wastewaters.

Distribution and accumulation of UV filters (UVFs) and conservation status of Posidonia oceanica seagrass meadows in a prominent Mediterranean coastal tourist hub

This study investigates the presence and impact of UV filters in Posidonia oceanica meadows in Formentera, a Mediterranean tourist hotspot. It highlights the distribution of inorganic (TiO2 and ZnO) and organic UV filters (UVFs) in different environmental matrices, their accumulation in seagrass tissues and their impact on the seagrass health. In the overlying and canopy waters of P. oceanica, Zn concentrations surpassed Ti, with three organic UVFs (benzophenone-3 [BP-3], avobenzone and homosalate [HMS]) consistently detected. Ti concentrations were generally higher than Zn in rhizosphere sediments, along with recurrent presence of octocrylene, HMS, 2-ethylhexyl methoxycinnamate (EHMC), and 4-methylbenzylidene camphor (4-MBC). Maximum Zn concentrations were found in canopy waters (3052.9 ng L-1). Both Ti and Zn were found in all P. oceanica tissues and leaf epiphytes across all study sites. Additional UVFs like octocrylene, avobenzone, and BP-8 were also detected in P. oceanica tissues and epiphytes. Elevated levels of octocrylene in leaf epiphytes (2112.1 ng g-1 dw) and avobenzone in leaves (364.2 ng g-1 dw) and leaf epiphytes (199.6 ng g-1 dw) were observed in the Port of La Savina, the island's main entry port. Octocrylene concentrations (up to 2575 ng g-1 dw) in rhizosphere sediments near sewage discharge points exceeded reported maxima, highlighting wastewater treatment plants as significant sources of organic UVFs. Correlational analyses suggested that the accumulation of octocrylene, avobenzone, and BP-3 negatively impacted P. oceanica's conservation status, affecting global density, density at 100 % cover, and leaf morphometry. Positive correlations were observed between leaf polyphenols (antioxidants) and concentrations of avobenzone, benzophenone-8 (BP-8), and BP-3, indicating potential oxidative stress induced by UVFs in P. oceanica. Our study underscores the pervasive presence of UV filters in P. oceanica habitats, with implications for seagrass health and conservation, especially in areas of high tourism and sewage discharge.

Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review

Water resources are constantly threatened by pollution of potentially toxic elements (PTEs). In efforts to monitor and mitigate PTEs pollution in water resources, machine learning (ML) algorithms have been utilized to predict them. However, review studies have not paid attention to the suitability of input variables utilized for PTE prediction. Therefore, the present review analyzed studies that employed three ML algorithms: MLP-NN (multilayer perceptron neural network), RBF-NN (radial basis function neural network), and ANFIS (adaptive neuro-fuzzy inference system) to predict PTEs in water. A total of 139 models were analyzed to ascertain the input variables utilized, the suitability of the input variables, the trends of the ML model applications, and the comparison of their performances. The present study identified seven groups of input variables commonly used to predict PTEs in water. Group 1 comprised of physical parameters (P), chemical parameters (C), and metals (M). Group 2 contains only P and C; Group 3 contains only P and M; Group 4 contains only C and M; Group 5 contains only P; Group 6 contains only C; and Group 7 contains only M. Studies that employed the three algorithms proved that Groups 1, 2, 3, 5, and 7 parameters are suitable input variables for forecasting PTEs in water. The parameters of Groups 4 and 6 also proved to be suitable for the MLP-NN algorithm. However, their suitability with respect to the RBF-NN and ANFIS algorithms could not be ascertained. The most commonly predicted PTEs using the MLP-NN algorithm were Fe, Zn, and As. For the RBF-NN algorithm, they were NO3, Zn, and Pb, and for the ANFIS, they were NO3, Fe, and Mn. Based on correlation and determination coefficients (R, R2), the overall order of performance of the three ML algorithms was ANFIS > RBF-NN > MLP-NN, even though MLP-NN was the most commonly used algorithm.

Geochemical Responses to Natural and Anthropogenic Settings in Salt Lakes Sediments from North-Eastern Romanian Plain

Chemical analysis was performed on sediment core samples collected from three salt lakes, Amara Lake, Caineni Lake, and Movila Miresii Lake, located in the northeast of the Romanian Plain. The concentration of 10 main elements, 6 heavy metals (HMs), 8 rare earth elements (REEs), and 10 trace elements (TEs)-determined using neutron activation analysis (NAA)-showed variability dependent on the depth sections, lake genesis and geochemical characteristics (oxbow, fluvial harbor/liman and loess saucer type). The assessment of pollution indices (contamination factor, pollution load index, geoaccumulation index, and enrichment factor) highlighted low and moderate degrees of contamination for most of the investigated elements. Principal component analysis (PCA) extracted three principal components, explaining 70.33% (Amara Lake), 79.92% (Caineni Lake), and 71.42% (Movila Miresii Lake) of the observed variability. The principal components extracted were assigned to pedological contribution (37.42%-Amara Lake, 55.88%-Caineni Lake, and 15.31%-Movila Miresii Lake), salts depositions (due to the lack of a constant supply of freshwater and through evaporation during dry periods), atmospheric deposition (19.19%-Amara Lake, 13.80%-Caineni Lake, and 10.80%-Movila Miresii Lake), leaching from soil surface/denudation, rock weathering, and mixed anthropogenic input (e.g., agricultural runoff, wastewater discharges) (13.72%-Amara Lake, 10.24%-Caineni Lake, and 45.31%-Movila Miresii Lake).