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

Water quality assessment of river Ganga, India using water quality index and multivariate statistical techniques

The River Ganga is a vital lifeline for millions, and several authors reported that its quality has been deteriorating. Several initiatives have been taken by the government, but the situation is not up to the mark. Hence, water quality assessments are essential for sustainable river water management and restoring its ecological balance. This work employed spatiotemporal analysis of 20 hydrochemical variables, water quality indices (WQI), and multivariate statistics to assess the water quality of River Ganga. Water samples (n = 220) were collected at 20 locations (divided into four zones, namely upper (UZ), middle (MZ), lower (LZ), and estuarine (EZ)) based on the geographical nature and anthropogenic pressure. The study found that most of the water variables (specific conductivity (SC), pH, BOD, total alkalinity (TA), total hardness (TH), Ca-H, Mg-H, chlorinity (Cl-), salinity, nitrate-N, silicate and total dissolved solids (TDS)) were lowest reported at UZ while the highest at EZ. TH, salinity, Mg-H, Ca-H, TDS, Cl-, and SC in EZ, were above the drinking water limits and these variables markedly affect the river's water quality attributes, possibly increased by tidal influences. WQI indicated that the UZ was clean (26-50) in all seasons, MZ and LZ were good to poor (50-75) in all seasons, and EZ was unsuitable for drinking (> 100) in all seasons. Seasons were grouped into three clusters: less polluted (monsoon); slightly polluted (post-monsoon); and polluted (winter and pre-monsoon). The principal component analysis formed five clusters based on eigenvalue > 1: PC1 having TH, salinity, Mg-H, Ca-H, TDS, Cl-, and SC mainly influenced by tidal factor; PC2 having temperature, transparency, and DO was influenced by metrological source; PC3 (pH, TA, velocity) and PC4 (BOD, silicate) thought to be both natural as well as manmade; and PC5 was influenced by agricultural runoff (total phosphorus and NO3-N) and sewage water (TN) discharge. The study emphasized the significance of multivariate statistical techniques in discerning the variability patterns of parameters, as well as in formulating management strategies to enhance river water quality by pinpointing the most impactful parameters contributing to water quality degradation.

Potential risk of heavy metals release in sediments and soils of the Yellow River Basin (Henan section): A perspective on bioavailability and bioaccessibility

The ecology of watersheds plays an important role in regulating regional climate and human activities. The sediment-soil system in the middle and lower reaches of the Yellow River Basin (Henan section) was explored. The spatial distribution characteristics of heavy metals (HMs) showed that tributaries, which are affected by anthropogenic activities, contain higher concentrations of HMs than the main channel. Sequential extraction experiments indicated that Cd had the strongest potential to be released, followed by Mn. In vitro simulation experiments showed that gastric and pulmonary fluids rendered these two orders of magnitude more bioaccessible compared to sweat. Moreover, Cd exhibited the highest bioaccessibility in both gastric and lung fluids. When bioaccessibility was considered in the evaluation of health risks, more than 82 % of reductions in non-carcinogenic and carcinogenic risk indices were observed in children and adults. A positive matrix factorization model was utilized to determine the potential sources of HMs: industrial sources, natural sources, and mixed agricultural and transportation sources were identified as the three main sources of HMs in sediments and soils. In addition, mining activities were also an HMs source in sediments.

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.