The determination of background concentration values (BGVs) in areas, characterised by the presence of natural geochemical anomalies and anthropogenic impact, appears essential for a correct pollution assessment. For this purpose, it is necessary to establish a reliable method for determination of local BGVs. The case of the Orbetello lagoon, a geologically complex area characterized by Tertiary volcanism, is illustrated. The vertical concentration profiles of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn were studied in four sediment cores. Local BGVs were determined considering exclusively samples not affected by anthropogenic influence, recognized by means of multivariate statistics and radiochronological dating ((137)Cs and (210)Pb). Results showed BGVs well-comparable with mean crustal or shale values for most of the considered elements except for Hg (0.87 mg/kg d.w.) and As (16.87 mg/kg d.w.), due to mineralization present in the catchment basin draining into the lagoon.
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http://dx.doi.org/10.1016/j.envpol.2015.03.017 | DOI Listing |
Geobiology
December 2024
Department of Earth and Planetary Sciences, University of California, Riverside, California, USA.
The majority of large iron formations (IFs) were deposited leading up to Earth's great oxidation episode (GOE). Following the GOE, IF deposition decreased for almost 500 Myr. Subsequently, around 1.
View Article and Find Full Text PDFSci Total Environ
December 2024
School of Environment and Resource, Xichang University, Xichang 615000, PR China.
Mining activities have led to significant rare earth elements (REEs) contamination and ecotoxicological risks in aquatic systems. However, the concentration, speciation, and primary controlling factors of REEs in aquatic systems in southwest China have remained unclear. This study investigated the water geochemistry, concentration, speciation, fractionation patterns, and anomalies of REEs in the surface water, shallow groundwater, and deep groundwater within a mining-impacted catchment area in southwest China across different seasons.
View Article and Find Full Text PDFAnal Methods
December 2024
State Key Laboratory of Biobased Material and Green Papermaking, Key Laboratory of Pulp & Paper Science and Technology of Shandong Province/Ministry of Education, Qilu University of Technology, Shandong Academy of Sciences, Jinan, 250353, China.
Viscosity is a crucial indicator of the flow state of proteins, lipids, and polysaccharides in the cell microenvironment and plays a vital role in maintaining normal cellular activities. Abnormal viscosity in any part of the cell constituents can lead to various diseases in the organism. For instance, abnormal mitochondrial viscosity can lead to diseases, such as diabetes and Parkinson's disease.
View Article and Find Full Text PDFEnviron Pollut
December 2024
School of Resources and Environmental Engineering, Anhui University, Anhui Province Engineering Laboratory for Mine Ecological Remediation, Hefei, 230601, Anhui, China. Electronic address:
Rare earth elements (REEs), functioning as indicators for environmental tracking, revealing the impacts of human activities on changes in aquatic ecosystems. However, systematic research on the geochemical characteristics of REEs in large river basins remains relatively scarce. Therefore, this research investigates the geochemical properties of REEs within the Yangtze River basin, analyzing the quantity and spatial distribution of REEs in surface aquatic environments across the upstream, midstream, and downstream segments of the Yangtze River, and quantitatively assessing their sources.
View Article and Find Full Text PDFSci Total Environ
December 2024
Key Laboratory of Groundwater Conservation of MWR, China University of Geosciences, Beijing 100083, PR China; School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing 100083, PR China.
Establishing natural background levels (NBLs) of nitrate‑nitrogen (NO-N) is crucial for groundwater resource management and pollution prevention. Traditional statistical methods for evaluating NO-N NBLs generally overlook the hydrogeochemical processes associated with NO-N pollution. We propose using a method that combines principal component factor analysis and K-means clustering (PCFA-KM) to identify NO-N anomalies in three typical areas of the Huaihe River Basin and evaluate the effectiveness of this method in comparison with the hydrochemical graphic method (Hydro) and the Gaussian mixture model (GMM).
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