Metal pollution assessment in surface sediments of Namak Lake, Iran

A case study of using artificial neural networks to predict heavy metal pollution in Lake Iznik

Artificial neural networks offer a viable route in assessing and understanding the presence and concentration of heavy metals that can cause dangerous complications in the wider context of water quality prediction for the sustainability of the ecosystem. In order to estimate the heavy metal concentrations in Iznik Lake, which is an important water source for the surrounding communities, characterization data were taken from five different water sources flowing into the lake between 2015 and 2021. These characterization results were evaluated with IBM SPSS Statistics 23 software, with the addition of the lake water quality system. For this purpose, seven distinct physicochemical parameters were measured and monitored in Karasu, Kırandere, Olukdere and Sölöz water sources flowing into the lake, to serve as input data. Concentration levels of 15 distinct heavy metals in Karsak Stream originating from the lake were as the output. Specifically, Sn for Karasu (0.999), Sb for Kırandere (1.000), Cr for Olukdere (1.000) and Pb and Se for Sölöz (0.995) indicate parameter estimation R2 coefficients close to 1.000. Sn stands out as the common heavy metal parameter with best estimation prospects. Given the importance of the independent variable in estimating heavy metal pollution, conductivity, COD, COD and temperature stood out as the most effective parameters for Karasu, Olukdere, Kırandere and Sölöz, respectively. The ANN model emerges as a good prediction tool that can be used effectively in determining the heavy metal pollution in the lake as part of the efforts to protect the water budget of Lake Iznik and to eliminate the existing pollution.

Natural and anthropogenic impacts on mercury accumulation in Xiaohai Lagoon, South China over the last 1200 years

Anthropogenic use and release of mercury (Hg) have profoundly affected the global Hg cycle since preindustrial times. However, it is often difficult to quantify the relative contributions of natural and anthropogenic factors to environmental Hg accumulation. Here, we have presented a 1200-year record of Hg deposition in a sediment core from the Xiaohai Lagoon (South China), in combination with multiple environmental indicators (e.g., geochemical elements, grain size and total organic carbon, etc.). Using principal component analysis (PCA) and stepwise regression analysis (SRA), we aimed to explore the latent processes governing the accumulation of Hg over time and to quantitatively assess the natural and anthropogenic impacts on Hg deposition over the last millennium in Xiaohai Lagoon. Our results have demonstrated that between ∼ 870 and ∼ 1860 CE, natural factors were the main drivers controlling Hg concentrations in the lagoon. These were directly driven by higher soil erosion and increased inputs of fine-grained matter. However, from 1860 to 2013 CE, enhanced anthropogenic activities played a significant role in Hg accumulation in Xiaohai Lagoon. Anthropogenic Hg fluxes increased significantly from ∼ 1860 CE, peaked several times during the 1860s to the 1950s, accelerated from the late 1950s to the early 2000s, and then declined gradually owing to the stringent environmental protection strategies and efficient pollutant control technologies. Our results have suggested that the increased anthropogenic Hg inputs between the 1860s and mid-1970s were mostly attributed to wars, the "Westernization Movement", and global industrial activity, with a surge mainly after the 1980s dominated by industrial activities in China and numerous developing countries in Southeast Asia. This study has shown the natural and anthropogenic influences associated with mercury pollution through quantitative analysis and can deepen our understanding of the processes and mechanisms of mercury deposition in natural environments under the influence of human activities.

Simulation and modelling of heavy metals and water quality parameters in the river

A one-dimensional qualitative model was created for this study's modelling and phase transfer of the heavy metal dissolved in the river. The advection-diffusion equation considers the environmental variables temperature, dissolved oxygen, pH, and electrical conductivity as influencing parameters on the change in the concentration of the dissolved phase of heavy metals lead, cadmium, and zinc in springtime and winter. Hec-Ras hydrodynamic model and Qual2kw qualitative model have been used to determine the hydrodynamic and environmental parameters in the created model. The approach of minimizing simulation errors and Vba coding was employed to identify the constant coefficients for these relations, and the linear relation incorporating all of the parameters is thought to be the final connection. In order to simulate and calculate the concentration of the heavy metals in the dissolved phase at each site, the kinetic coefficient of the reaction corresponding to that point should be employed because the kinetic coefficient of the reaction varies in different portions of the river. Additionally, if the above mentioned environmental parameters are used in the spring and winter term of advection-diffusion equations, the accuracy of the developed model significantly improves, and the effect of other qualitative parameters is negligible, indicating that the model is effective at simulating the dissolved phase of heavy metals in the river.

Health and ecological risks assessment of heavy metals and metalloids in surface sediments of Urmia Salt Lake, Northwest of Iran

Urmia Lake, in the northwest of Iran, is the largest body of saline water in the Middle East, which has been desiccated in recent decades. To investigate the pollution status and ecological-health risks of heavy metals and metalloids in the surface sediments of this lake, 26 sediment samples were collected along the salt marshes of the lake and were analyzed for heavy metals and metalloid concentrations. The potential ecological risk assessment was carried out using enrichment factor (EF), geo-accumulation index (I_geo), contamination factor (CF), and potential ecological risk (E_rⁱ) standard indices. The average concentrations (mg kg^-1) of heavy metals and metalloids were as follows: Fe (11,714) > Sr (320.8) > Mn (274.3) > V (28.5) > Cu (24.7) > Zn (21.2) > As (17.3) > Ni (14.8) > Cr (12.6) > Pb (11) > Co (4.0) > U (1.7), Hg (0.6) > Mo (0.36). The concentrations of As, Hg, and Sr in lake sediments were higher than geochemical background values. The non-carcinogenic risks caused by heavy metals and metalloids were insignificant regarding health risks. Levels of carcinogenic risk for metal(loid)s were in the acceptable ranges (10^-6-10^-4). The ecological risk was low, except for As, Sr, and Hg which showed moderate to significant EF, I_geo, and CF values. Arsenic and Sr were enriched in the surface sediments in desiccated parts of the lake due to complete lake water evaporation. It seems that further drying of the lake increases the potential ecological risk of heavy metals and metalloids in the surface sediments of Urmia Lake.

Distribution, risk assessment, and source identification of trace metal pollution along the Babolsar coastal area, Caspian Sea

The Caspian Sea is exposed to numerous anthropogenic activities such as untreated wastewater discharge and agricultural activities which increased trace metals contamination. The current study was employed to assess the distribution, ecological risk assessment, and source identification of some trace metals in 125 samples of surface sediments and soil from 6 distinctive sections of Babolsar in the coastal line of the Caspian Sea. The sediment quality guidelines (SQGs) and individual risk assessment indices including enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (Cf), and potential risk factor (Er) suggested a hazardous level of Cd and Cr contamination. There was a relatively high level of Pb contamination while other studied trace metals were at a low contamination level. Cumulative risk indices such as modified degree of contamination (mCD), pollution load index (PLI), and the potential ecological risk index (RI) identified that the river, wetland, and farmland sites were more contaminated compared to the Caspian Sea samples which exhibited a moderate level of contamination. The lowest level of contamination was recorded in the coastline and river delta sites. Two analytical methods including Pearson's correlation coefficient and multivariate clustering dendrogram were also applied to identify the potential sources of contamination. The results suggested that wastewaters, nitrogen, and phosphate fertilizers were the main anthropogenic source of Cd, Co, Pb, and Ni while fossil fuels and transportation activities were the predominant sources of anthropogenic Cu, V, and Zn contamination. Further studies about trace metals risk assessment and fractionation could contribute to more effective decisions for reducing the anthropogenic trace metal pollution in the Caspian Sea.

Chronological deposition record of trace metals in sediment cores from Chaohu Lake, Anhui Province, China

Lakes located in the mid-low reaches of the Yangtze River watershed have been subjected to various degrees of human perturbation that would have resulted in toxic metal concentrations and would pose potential risk to the natural habitats. Therefore, in the present study, two sediment cores from Chaohu Lake were collected to determine any such concentration, expressed as the enrichment characteristics of major and trace metals (Al, Fe, Mn, Cu, Pb, Zn, Cr, Cd, As, Hg, and Ni) in response to natural and anthropogenic changes. An approximate 180-year (1840-2021) deposition record of trace metals in sediment cores was obtained on the basis of ¹³⁷Cs and ²¹⁰Pb dating. Enrichment factors (EFs) and the geo-accumulation index (I_geo) were adopted to evaluate the enrichment state and pollution status of trace metals. The results showed that Cu and Zn were persistently more enriched in sediment, whereas Mn, Pb, Cd, and Hg has shown remarkable increasing trends in the west lake since the 1970s. Evaluation of the I_geo confirmed that sediment was moderately polluted with Cu and Zn within the whole lake and with Cd in the west lake area, whereas it was uncontaminated with Cr and slightly contaminated with other metals. Furthermore, source identification based on multivariate statistical analysis including correlation analysis, principal component analysis, and cluster analysis suggested similar pollution sources for the studied metals. Combined with the natural and anthropogenic changes within the watershed, enhanced soil erosion due to population expansion and agricultural intensification was a major contributor to sedimentary metals before the 1970s while industrial wastewater, urban runoff, and domestic sewage were predominant inputs of trace metals after the 1970s.

Trace Metal Contamination of Bottom Sediments: A Review of Assessment Measures and Geochemical Background Determination Methods

This paper provides an overview of different methods of assessing the trace metal (TM) contamination status of sediments affected by anthropogenic interference. The geochemical background determination methods are also described. A total of 25 papers covering rivers, lakes, and retention tanks sediments in areas subjected to anthropogenic pressure from the last three years (2019, 2020, and 2021) were analysed to support our examination of the assessment measures. Geochemical and ecotoxicological classifications are presented that may prove useful for sediment evaluation. Among the geochemical indices, several individual pollution indices (CF, Igeo, EF, Pi (SPI), PTT), complex pollution indices (PLI, Cdeg, mCdeg, Pisum, PIAvg, PIaAvg, PIN, PIProd, PIapProd, PIvectorM, PINemerow, IntPI, MPI), and geochemical classifications are compared. The ecotoxicological assessment includes an overview of Sediment Quality Guidelines (SQG) and classifications introduced nationally (as LAWA or modified LAWA). The ecotoxicological indices presented in this review cover individual (ERi) and complex indices (CSI, SPI, RAC, PERI, MERMQ). Biomonitoring of contaminated sites based on plant bioindicators is extensively explored as an indirect method for evaluating pollution sites. The most commonly used indices in the reviewed papers were Igeo, EF, and CF. Many authors referred to ecotoxicological assessment via SQG. Moreover, PERI, which includes the toxic response index, was just as popular. The most recognised bioindicators include the Phragmites and Salix species. Phragmites can be considered for Fe, Cu, Cd, and Ni bioindication in sites, while Salix hybrid cultivars such as Klara may be considered for phytostabilisation and rhizofiltration due to higher Cu, Zn, and Ni accumulation in roots. Vetiveria zizanoides demonstrated resistance to As stress and feasibility for the remediation of As. Moreover, bioindicators offer a feasible tool for recovering valuable elements for the development of a circular economy (e.g., rare earth elements).