Metal Contents and Pollution Indices Assessment of Surface Water, Soil, and Sediment from the Arieș River Basin Mining Area, Romania

Ecological risk of metals in Andean water resources: A framework for early environmental assessment of mining projects in Peru

Metallic contaminants in Andean water resources influenced by mining activities poses risks to aquatic ecosystems and a challenge to regulatory agencies responsible for environmental compliance. In this study, the Ecological Risk Assessment (ERA) framework was adapted to assess dissolved heavy metal concentrations at 283 surface water monitoring stations near to six mining projects during the dry and wet seasons. Reports from OEFA-Peru on Early Environmental Assessment (EEA) were used to apply various criteria and non-parametric statistical tests. They included ecological, ecotoxicological, chemical, and regulatory factors. The main goal of this research was to identify, analyze, characterize, and compare the risks present at different trophic levels. These levels were categorized as T1 (Microalgae), T2 (Zooplankton and Benthic invertebrates), and T3 (Fish). Individual risk (IR) was estimated using the quotient model, while total risk (TR) was assessed using the additive probability rule. Rainbow trout (Oncorhynchus mykiss), representing trophic level T3, showed the highest sensitivity to Fe and Cu. Statistical tests ranked the IR as Fe > Cu > Zn > Mn > Pb (p < 0.01). The TR was more prevalent during the wet season compared to the dry season (p < 0.01). Notably, around 50 % of the monitoring stations (n = 142) were classified as high risk, and 9 % (n = 13) showed extremely high-risk values for Cu and Fe. The adapted ERA framework demonstrated great effectiveness in identifying critical points of metal contamination in high Andean aquatic ecosystems under mining influence. However, specialized studies are suggested that allow the sources of pollution to be associated with specific regulatory actions.

Assessment of Microplastics and Potentially Toxic Elements in Surface Sediments of the River Kelvin, Central Scotland, United Kingdom

Contamination of the environment by microplastics (MPs), polymer particles of <5 mm in diameter, is an emerging concern globally due to their ubiquitous nature, interactions with pollutants, and adverse effects on aquatic organisms. The majority of studies have focused on marine environments, with freshwater systems only recently attracting attention. The current study investigated the presence, abundance, and distribution of MPs and potentially toxic elements (PTEs) in sediments of the River Kelvin, Scotland, UK. Sediment samples were collected from eight sampling points along the river and were extracted by density separation with NaCl solution. Extracted microplastics were characterised for shape and colour, and the polymer types were determined through attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. Pollution status and ecological risks were assessed for both the microplastics and PTEs. Abundance of MPs generally increased from the most upstream location (Queenzieburn, 50.0 ± 17.3 particles/kg) to the most downstream sampling point (Kelvingrove Museum, 244 ± 19.2 particles/kg). Fibres were most abundant at all sampling locations, with red, blue, and black being the predominant colours found. Larger polymer fragments were identified as polypropylene and polyethylene. Concentrations of Cr, Cu, Ni, Pb and Zn exceeded Scottish background soil values at some locations. Principal component and Pearson's correlation analyses suggest that As, Cr, Pb and Zn emanated from the same anthropogenic sources. Potential ecological risk assessment indicates that Cd presents a moderate risk to organisms at one location. This study constitutes the first co-investigation of MPs and PTEs in a river system in Scotland.

Impact of Arieş River Contaminants on Algae and Plants

The Arieş River (Western Romania) represents one of the most important affluents of the Mureş River, with great significance in the Mureş Tisza basin. The environmental quality of the Arieş basin is significantly affected by both historic mining activities and contemporary impacts. Thus, an evaluation of the effects of the main contaminants found in water (organochlorine pesticides-OCPs, monocyclic aromatic hydrocarbons-MAHs, polycyclic aromatic hydrocarbons-PAHs, and metals) on cyanobacteria and plants was performed. Among OCPs, hexachlorocyclohexane isomers, dichlorodiphenyltrichloroethane, and derivatives were detected in plants while admissible concentrations were detected in water. Among MAHs, high levels of benzene were detected both in water and in plants. The levels of PAHs exceeded the allowable values in all samples. Increased concentrations of metals in water were found only at Baia de Arieş, but in plants, all metal concentrations were high. The pH, nitrates, nitrites, and phosphates, as well as metals, pesticides, and aromatic hydrocarbons, influenced the physiological characteristics of algae, test plants, and aquatic plants exposed to various compounds dissolved in water. Considering that the Arieş River basin is the site of intense past mining activities, these data provide information about the impact on water quality as a consequence of pollution events.

Investigation and comparative analysis of ecological risk for heavy metals in sediment and surface water in east coast estuaries of India

The sediments and surface water from 8 stations each from Dhamara and Paradeep estuarine areas were sampled for investigation of heavy metals, Cd, Cu, Pb, Mn, Ni, Zn, Fe, and Cr contamination. The objective of the sediment and surface water characterization is to find the existing spatial and temporal intercorrelation. The sediment accumulation index (I_sed), enrichment index (I_En), ecological risk index (I_EcR) and probability heavy metals (p-HMI) reveal the contamination status with Mn, Ni, Zn, Cr, and Cu showing permissible (0 ≤ I_sed ≤ 1, I_En ˂ 2, I_EcR ≤ 150) to moderate (1 ≤ I_sed ≤ 2, 40 ≤ R_f ≤ 80) contamination. The p-HMI reflects the range from excellent (p-HMI = 14.89-14.54) to fair (p-HMI = 22.31-26.56) in off shore stations of the estuary. The spatial patterns of the heavy metals load index (I_HMc) along the coast lines indicate that the pollution hotspots are progressively divulged to trace metals pollution over time. Heavy metal source analysis coupled with correlation analysis and principal component analysis (PCA) was used as a data reduction technique, which reveals that the heavy metal pollution in marine coastline might originate from redox reactions (FeMn coupling) and anthropogenic sources.

Developing an Intelligent Data Analysis Approach for Marine Sediments

(1) Background: As the chemical and physicochemical properties of marine sediments are closely related to natural and anthropogenic events, it is a real challenge to use their specific assessment as an indicator of environmental pollution discharges. (2) Methods: It is addressed in this study that collection with intelligent data analysis methods, such as cluster analysis, principal component analysis, and source apportionment modeling, are applied for the assessment of the quality of marine sediment and for the identification of the contribution of pollution sources to the formation of the total concentration of polluting species. A study of sediment samples was carried out on 174 samples from three different areas along the coast of the Varna Gulf, Bulgaria. This was performed to determine the effects of pollution. As chemical descriptors, 34 indicators (toxic metals, polyaromatic hydrocarbons, polychlorinated biphenyls, nutrient components, humidity, and ignition loss) were used. The major goal of the present study was to assess the sediment quality in three different areas along the Gulf of Varna, Bulgaria by the source apportionment method. (3) Results: There is a general pattern for identifying three types of pollution sources in each area of the coastline with varying degrees of variation between zone A (industrially impacted zones), zone B (recreational areas), and zone C (anthropogenic and industrial wastes). (4) Conclusions: The quantitative apportionment procedure made it possible to determine the contribution of each identified pollution source for each zone in forming the total pollutant concentrations.