Receptor model-based source apportionment and ecological risk assessment of metals in sediment of river Ganga, India

Distribution of heavy metals in the sediments of Ganga River basin: source identification and risk assessment

Sediment serves as a heavy metal store in the riverine system and provides information about the river's health. To understand the distribution of heavy metal content in the Ganga River basin (GRB), a total of 25-bed sediment and suspended particulate matter (SPM) samples were collected from 25 locations in December 2019. Bed sediment samples were analyzed for different physio-chemical parameters, along with heavy metals. Due to insufficient quantity of SPM, the samples were not analyzed for any physio-chemical parameter. The metal concentrations in bed sediments were found to be as follows: Co (6-20 mg/kg), Cr (34-108 mg/kg), Ni (6-46 mg/kg), Cu (14-210 mg/kg), and Zn (30-264 mg/kg) and in SPM, the concentrations were Co (BDL-50 mg/kg), Cr (10-168 mg/kg), Ni (BDL-88 mg/kg), Cu (26-80 mg/kg), and Zn (44-1186 mg/kg). In bed sediment, a strong correlation of 0.86 and 0.93 was found between Ni and Cr, and Cu and Zn respectively and no significant correlation exists between organic carbon and metals except Co. In SPM, a low to moderate correlation was found between all the metals except Zn. The risk indices show adverse effects at Pragayraj, Fulhar, and Banshberia. Two major clusters were formed in Hierarchal Cluster Analysis (HCA) among the sample points in SPM and bed sediment. This study concludes that the Ganga River at Prayagraj, Banshberia, and Fulhar River is predominately polluted with Cu and Zn, possibly posing an ecological risk. These results can help policymakers in implementing measures to control metal pollution in the Ganga River and its tributaries.

Metal pollution assessment in the surface sediments of a river system in Türkiye: Integrating toxicological risk assessment and source identification

This study investigates potentially toxic elements (PTEs) in the surface sediments of the Abdal River system, a critical water source for Samsun province, Türkiye, due to the presence of the Çakmak Dam. PTE concentrations, measured in mg/kg, show significant variability: Hg (0.03) < Cd (0.26) < As (10.98) < Pb (13.88) < Cu (48.61) < Ni (62.45) < Zn (70.97) < Cr (96.28) < Mn (1015) < Fe (38357). Seasonal variations were observed, in particular increased concentrations of As, Cd and Pb in summer (p < 0.05). Contamination and ecological risk indices (mHQ, EF, Igeo, CF, PLI, Eri, mCd, NPI, PERI, MPI, and TRI) indicate moderate to low levels of contamination, suggesting potential ecological effects. Health risk assessments suggest minimal risks to human health from sediment PTEs. Statistical analyses (PCC, PCA and HCA) improve the understanding of the sediment environment and contamination sources, while the coefficient of variation assists in source identification.

Evaluation and monitoring of the water quality of an Argentinian urban river applying multivariate statistics

In this work, we present the water quality assessment of an urban river, the San Luis River, located in San Luis Province, Argentina. The San Luis River flows through two developing cities; hence, urban anthropic activities affect its water quality. The river was sampled spatially and temporally, evaluating ten physicochemical variables on each water sample. These data were used to calculate a Simplified Index of Water Quality in order to estimate river water quality and infer possible contamination sources. Data were statistically analyzed with the opensource software R, 4.1.0 version. Principal component analysis, cluster analysis, correlation matrices, and heatmap analysis were performed. Results indicated that water quality decreases in areas where anthropogenic activities take place. Robust inferential statistical analysis was performed, employing an alternative of multivariate analysis of variance (MANOVA), MANOVA.wide function. The most statistically relevant physicochemical variables associated with water quality decrease were used to develop a multiple linear regression model to estimate organic matter, reducing the variables necessary for continuous monitoring of the river and, hence, reducing costs. Given the limited information available in the region about the characteristics and recovery of this specific river category, the model developed is of vital importance since it can quickly detect anthropic alterations and contribute to the environmental management of the rivers. This model was also used to estimate organic matter at sites located in other similar rivers, obtaining satisfactory results.

Trace metals and pesticides in water-sediment and associated pollution load indicators of Netravathi-Gurupur estuary, India: Implications on coastal pollution

Various environmental indicators were used to evaluate the water and sediment quality of the Netravathi-Gurupur estuary, India, for trace metals and pesticide pollution. The descended order of studied metal concentrations (μg/L) in the water was Fe (592.71) > Mn (98.35) > Zn (54.69) > Cu (6.64) > Cd (3.24) > Pb (2.38) > Cr (0.82) and in sediment (mg/kg) was Fe (11,396.53) > Mn (100.61) > Cr (75.41) > Zn (20.04) > Cu (12.77) > Pb (3.46) > Cd (0.02). However, pesticide residues were not detected in this estuarine environment. The various metal indexes categorised the water as uncontaminated, whereas contamination factor, enrichment factor, geo-accumulation index, degree of contamination and pollution load index indicated low to moderate sediment contamination. Multivariate statistics showed that the dominance of natural sources of trace metals with little anthropogenic impact. Improvement in water/sediment quality during the study period might be due to COVID-19 imposed lockdown.