Spatial Distributions of Heavy Metals in the Water and Sediments of Lake Çıldır, Turkey

Identifying the sources of heavy metals in soils is a crucial issue for soil remediation and management. Most regions in China have been undergoing a rapid industrialization and urbanization since the last three decades. However, there is little information available on the spatial distribution of heavy metals in soils experiencing a rapid transition from agricultural-based to industrial-based economy. To resolve this problem and to provide references on similar regions, we carried out an investigation on heavy metals in soils in Ju country to identify potential sources and to map their spatial distributions. A total of 646 samples including 511 samples in topsoils (0–20 cm, regular grid of 2 × 2 km2) and 135 samples in subsoils (150–200 cm, regular grid of 4 × 4 km2) were collected in Ju country, and the total contents of Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, V, and Zn were determined. Enrichment factor method and multivariate analyses (correlation analysis, principal component analysis, and cluster analysis) were applied to identify the sources of ten heavy metals in topsoils. Additionally, ordinary kriging was used to map the spatial distributions of heavy metals concentration in topsoils. The overall levels of all heavy metals did not show high values, but the enrichment factor results suggested that Hg, Cd, Cu, Pb, and Zn in topsoils showed significant accumulation. Ten heavy metals can be grouped into three groups. Co, Cr, Mn, Ni, and V were associated with parent material and seemed to originate from a natural source. Cd, Cu, Pb, and Zn seemed to be related to the combination of parent material and anthropic inputs. Hg as an isolated element was dominated by atmospheric deposition inputs related to various human activities. Distribution maps derived by ordinary kriging suggested that Cd, Cu, Hg, Pb, and Zn were linked to the western part, corresponding well to the surroundings of urban areas located in the western part of Ju country. Hg, Cd, Cu, Pb, and Zn were the main metals affected by human inputs in Ju country, and the high risk resulting from human influence was mainly shown around urban areas, consistent with the spatial distribution of industrial and traffic sites. Agricultural practices did not have significant influence on the spatial distribution of metals. The combination of multivariate analysis and geostatistics was found to be an effective approach to identify the origins and spatial distribution of heavy metals in soils.

This paper presents an analysis of the content and spatial distribution of heavy metals (HM) in street dust in Suwałki, a city located in northeastern Poland. The HM content of street dust was also evaluated using the geochemical index (Igeo), enrichment factor (EF), and contamination factor (CF), and local HM sources were identified using chemometric methods. The arithmetic averages of HM contents in dust arranged in the following order: Fe > Zn > Mn > Cu > Cr > Ni > Pb, were 11,692.80, 215.97, 194.78, 142.84, 63.59, 17.50, 17.04 mg∙kg−1, respectively. Higher values than the local background occurred for Cr, Cu, Ni, Zn and Pb. The values of Igeo, CF, and EF indicate that the highest pollution in dust is due to Zn and Cu. The spatial distribution of metals was evaluated using maps of HM content in road dust samples from Suwałki. The spatial distribution of HM showed areas with high contents of Cr, Cu, Ni, Zn and Pb located mainly in the central and eastern parts of the city. In these areas, high traffic volume, the presence of shopping centers, administrative buildings and bus stops dominate. Statistical models of multivariate analysis (FA) and cluster analysis (CA) identified two sources of HM. The first source of pollution was associated with local industrial activity and motor vehicle travel, and the second with natural sources.

Defining and understanding the current abundance and spatial distribution of metals in soils are essential and reliable information on this aspect are needed for proper legislation. To estimate the baseline concentrations and spatial distribution of heavy metals (HMs) in Swell–shrink soils Sehore and Vidisha districts, 100 surface soil samples (0–20 cm) were randomly collected across the two districts and their physico-chemical properties and total HM contents were analysed. Spatial distribution maps of HMs were prepared and influence of soil parameters on HMs was studied. Most of the soils in the region had neutral to alkaline pH (6.58–8.60), non saline (EC 0.11–1.3 dS/m), medium organic carbon (0.6%), CaCO3 0.2–11.5% and clay >40%. The baseline concentrations of HMs (mg kg−1) were Cu, 178.1; Cd, 0.7; Pb, 24.4; Cr, 116.9; Ni, 81.8; and Zn, 85.2; respectively. The concentrations of Cd, Pb and Zn in all the samples were within the safe range but the concentrations of Cr, Cu and Ni were a little high.

The nearshore marine environment of the Caspian sea is a major repository for toxic metals originating from various sources. Since the persistent toxic metals pose serious health risks this research concentrated on investigating the concentrations and spatial distribution of metals in the nearshore sediments along the Iranian coast of the Caspian sea. Fourteen sampling sites were selected along the coast and approximately 400 g of surficial sediments were obtained. Samples were sieved and three grain size fractions from each sample plus fourteen bulk samples were selected for the analysis of metals. Laboratory analysis of the samples utilized the Cold Acetic protocol, followed by Inductively coupled plasma optical emission spectroscopy. The statistical techniques were used to analyze all obtained data. Linear regression analysis demonstrated that grain size of the sediments was not a major factor controlling the concentrations and spatial distributions of heavy metals. Box and Whisker plots emphasized that metal concentrations were not homogeneously distributed. Discriminant analysis was also proved to be useful in identifying geographic areas where heavy metal concentrations occur along the coast.

This study aimed to determine the content and spatial distribution of metals (Ca, Mg, Fe, Na, K, Mn, Zn, Cr, Cu, Pb, Co) in sediments in the coastal zone of Ełckie Lake located in the area of "Green Lungs of Poland" in the north-eastern part of the country, depending on the land use (urban area, agricultural and forest area, and beaches). The concentration of metals was determined using atomic absorption spectrometry. The average contents of major elements in 28 sediment samples occurred in the following order: Ca > Mg > Fe > Na > K > Mn. The order of these elements in the coastal sediments located within the different parts of the catchment was identical. These elements may originate from natural sources such as the Earth’s crust, soil, and wind-blown dust from unpaved roads. The average contents of potentially toxic elements (PTEs) in the sediments were as follows: Cr > Zn > Pb > Cu > Co in agricultural and forest areas and beaches (the exception was Cu for beach B, which occurred at the end of the series). A different pattern occurred in urbanized areas: Zn > Cr > Cu > Pb > Co. The spatial distribution of heavy metals in the sediments indicated the highest contents in the shoreline adjacent to the urbanized part of the catchment. The primary sources of metals in sediment are transportation, coal burning, sanitary sewage from unsewered developments on the lakeshore, and storm runoff from roads. This was confirmed by positive correlations of Zn with Cu (r = 0.58), Pb (r = 0.90), Fe (r = 0.40). No correlations between the studied metals and organic matter were found, which may indicate its insignificant influence on metal content in the sediments. Pearson correlation coefficients also showed no relationship between sediment pH and the presence of metals. Factor analysis (FA) indicated that lithogenic (geogenic) and anthropogenic factors have almost equal shares in the distribution of most of the metals studied. The analysis of variance (ANOVA) showed that the average contents of Zn, Cu, Co, and Na in the sediments from urbanized areas are statistically significantly higher than the sediments from other areas (rural/forest, beaches).

Recent years have witnessed intensification of road traffic and, with it, the amount of substances emitted by vehicles. Such emissions need to be monitored for public health purposes. The aim of this study was to evaluate the impact of the highway traffic on the total content and bioavailability of Zn, Cu, Ni, Cd, Cr and Pb in nearby soils as well as influence of an acoustic screen on spatial distribution of the metals. The material included 40 soil samples collected from 15 research points located 5, 10, 25 and 50 m away from the road acoustic screen and from 4 points between the screen and the highway. Additionally, 5 research points were located next to the metal barrier. Selected physicochemical properties of soils were determined: soil texture, soil pH, TOC and CaCO3 content. The total content of heavy metals in the soils was determined by AAS after digestion in aqua regia and bioavailable forms in 1 M diethylenetriaminepentaacetic acid. The research found low impact of the highway traffic on the content of heavy metals in soils; however, due to a very short period of this potential impact (5 years), the moderately polluted category of geo-accumulation index of cadmium and high bioavailability of lead indicate the need of repeating the research within the next several years. Furthermore, the road acoustic screen significantly influenced spatial distribution of the metals in soils.

Geographic distribution of heavy metals and identification of their sources in soils near large, open-pit coal mines using positive matrix factorization

Using the Jintan District of Changzhou City, Jiangsu Province as an example, the LUR model was used to study the spatial distribution of heavy metals and to simulate the spatial distribution of heavy metals in the study area. Compared with the traditional LUR model and the ordinary Kriging interpolation model, the following conclusions were obtained. ① The soil heavy metal content in the study area was highly and significantly correlated with land factors, with the main factor of land use and influencing factors of heavy metals in the soil environment (P<0.01). In terms of influencing factors, the soil Cu and Zn contents were significantly correlated with the area related to traffic in a 2000 m buffer area and 2000 m buffer zone, respectively. The soil Cr, Cu, and Zn contents were significantly correlated with OM, Corg, TC, and TN (P<0.01). ② The R2 of the LUR-S models of the spatial distribution of the heavy metals, Pb, Cr, Cu, and Zn, in the study area were improved by 0.041, 0.406, 0.102, and 0.501, respectively, compared with the traditional LUR model. The accuracy test R2 values were improved by 0.1477, 0.0116, 0.2310, and 0.081, respectively; and the RMSE was reduced by 2.413, 0.631, 1.112, and 2.138, respectively. It was shown that the LUR-S model, which considered the source-sink relationship, had a higher accuracy than the traditional LUR model and ordinary Kriging interpolation model. ③ The LUR-S model was more suitable for the prediction of the spatial distribution of heavy metals with lower pollution and smaller variations, while results for the prediction of the heavy metals with higher pollution and larger variations were worse.

Coking plants are typical industrial pollution sites and may release heavy metals into the environment, posing a threat to human health. Scholars have discovered that different types of heavy metals are released during different coking production processes and lead to spatial differences in heavy metals. Research on the spatial distribution and driving factors of pollutants in the soil inside and outside coking plants is important for sampling design, risk assessment, pollution prevention and control, etc.. Inverse distance weight was used to analyze the spatial distribution of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn inside and outside of the coking plant. A geo-detector was used to find out the difference in the driving factors for the spatial distribution of heavy metals between soil from inside and outside the coking plant. The results showed that except As, Ni, and Zn, the overall background value rate of other heavy metals was above 50%, and the continuity of the spatial distribution of heavy metals in the soil was poor. The coefficient of variation (CV) exceeded 30%, representing a moderate variation. The average degree of CV inside the coking plant was Hg > Cd > As > Cu > Zn > Cr > Pb > Ni, and the external average degree of CV was Hg > Cu > Cd > As > Zn > Pb > Cr > Ni. An analysis of heavy metal content showed that the content of As, Cd, Cr, Pb, and Zn outside the coking plant was bigger than inside. According to geo-detector results, the physicochemical properties factors with a large contribution rate to the spatial distribution of heavy metals inside and outside the coking plant was the soil's total nitrogen, organic matter, and available medium-micro element content. Pollution source factors that contributed the most to the spatial distribution of heavy metals inside were the crude benzol and cold drum section, while the coke oven and quench section determined the outside spatial distribution of heavy metals. The q value of the strongest factor inside the coking plant was more than 0.5 while outside the coking plant it was less than 0.5. According to the interaction detector result, the interaction factors values of pollution sources and soil physicochemical properties to the inside spatial distribution of heavy metals was higher than outside. According to the distribution and geo-detector results, the strongest physicochemical properties driving factors that determined the inside and outside spatial distribution of heavy metals were relatively consistent. These factors were soil nutrient factors, which mainly influenced the availability of heavy metals. The differences in the production processes led to the difference between the inside and outside spatial distribution of heavy metals. The content of heavy metals outside the coking plant was higher than inside because the heavy metals came from various pollution sources. The driving forces for the distribution of heavy metals inside the plant were higher than outside and showed that the heavy metals inside of the plant were mainly from the coking plant. Heavy metal distribution inside the coking plant was mainly driven by the pollution source factor of the coking refining process and coking water, while heavy metal distribution outside the coking plant was mainly driven by the coking gas production process and other emission pollution source factors.

To understand the water pollution status and environmental risks of Changshou Lake, the concentrations of heavy metals (Cr, Cu, Zn, As, Cd, and Pb) in the water were collected and analyzed during different seasons. The study investigated temporal and spatial variations, distribution characteristics, pollution levels, and health risks associated with heavy metals in Changshou Lake. The results showed that all six heavy metals were below than the Class Ⅰ standard of the Surface Water Environmental Quality Standard (GB 3838-2002), but recent years have witnessed an increasing trend, with Cu, As, and Pb showing a significant increase (P<0.05). The temporal and spatial distributions of these heavy metals were different. Temporally, Cr and Cd concentrations in surface water were higher in summer, As and Zn were higher in spring, and Pb and Cu were higher in autumn and winter. Spatially, the concentrations of Cr, As, Cu, Zn, and Pb showed higher concentrations in the southern outlet of the reservoir, the northwestern Longxi River inlet, and the central part of the reservoir, whereas Cd was higher in the northern stagnant area. The overall levels of heavy metals in the water body of Changshou Lake were low, with Cr and Cu slightly polluted, while other heavy metals were identified as having an insignificant pollution level. Drinking water was the primary exposure pathway to carcinogenic and non-carcinogenic heavy metals in surface water bodies. The health risk values of Cr and As in water bodies were high, ranging from 6.2×10-10 to 3.0×10-4 and 5.1×10-8 to 3.9×10-5, respectively. The corresponding contribution rates for children and adults to the total health risk were high, with Cr accounting for 87.18% and 87.20%, respectively, while As accounted for 12.73% and 12.71%, respectively. Therefore, it is crucial to prioritize environmental risks associated with Cr and Cu, as well as the health risks associated with Cr and As in Changshou Lake These findings provide a scientific foundation for water pollution control and environmental quality improvement in Changshou Lake, and rational development and utilization of water resources.

Trace metal uptake by mussels in a recently deceased community, Lake Breitling, Germany: a Laser Ablation ICP-MS study.

The paper presents the results of studying the spatial distribution of heavy metals (Zn, Cu, Ni, Cr, Sr, Co, Fe, Mn) in the bottom sediments of the bays of the Sevastopol region. The data obtained during the 2003 to 2018 expeditions are analyzed. The spatial distribution and accumulation of heavy metals in the bottom sediments of the Sevastopol, Kazachya and Balaklava bays are studied. Correlations between their content and the physico-chemical characteristics of bottom sediments of the studied water areas (content of Corg, CaCO3, fractional composition) were obtained. The metals with the same correlation coefficients for both the content of Corg and the silt fraction content are identified. These are Fe (r = 0,7) and Ni (r = 0,6) for bottom sediments of the Sevastopol Bay, Fe (r = 0,6) and Cu (r = 0,7) for the Kazachya Bay and only Ni (r = 0,8) for the Balaklava Bay. High correlation between Sr and CaCO3 content (r = 0,8) has been established for bottom sediments of all studied water areas. It is shown that the spatial distribution of heavy metals is determined by physico-chemical characteristics of bottom sediments, which is confirmed by the magnitude of correlations in terms of the content of fine fraction and organic carbon, while the difficult water exchange with the open sea contributes to their accumulation up to maximum values.

Spatial distribution, contamination levels, and risk assessment of heavy metals along the Eastern India coastline.

Soil is an important natural resource. The excessive amount of heavy metals in soil can harm and threaten human health. Therefore, monitoring of soil heavy metal content is urgent. Monitoring soil heavy metals by traditional methods requires many human and material resources. Remote sensing has shown advantages in the field of monitoring heavy metals. Based on 971 heavy metal samples and Sentinel-2 multi-spectral images in Tai Lake, China, we analyzed the correlation between six heavy metals (Cd, Hg, As, Pb, Cu, Zn) and spectral factors, and selected As and Hg as the input factors of inversion model. The correlation coefficient of the best model of As was 0.53 (p &lt; 0.01), and of Hg was 0.318 (p &lt; 0.01). We used the methods of partial least squares regression (PLSR) and back propagation neural network (BPNN) to establish inversion models with different combinations of spectral factors by using 649 measured samples. In addition, 322 measured samples were used for accuracy evaluation. Compared with the PLSR model, the BP neural network builds the model with higher accuracy, and B1-B4 combined with LnB1-LnB4 builds the model with the highest accuracy. The accuracy of the best model was verified, with an average error of 19% for As and 45% for Hg. Analyzing the spatial distribution of heavy metals by using the interpolation method of Kriging and IDW. The overall distribution trend of the two interpolations is similar. The concentration of As elements tends to increase from north to south, and the relatively high value of Hg elements is distributed in the east and west of the study area. The factories in the study area are distributed along rivers and lakes, which is consistent with the spatial distribution of heavy metal enrichment areas. The relatively high-value areas of heavy metal elements are related to the distribution of metal products factories, refractory porcelain factories, tile factories, factories and mining enterprises, etc., indicating that factory pollution is the main reason for the enrichment of heavy metals.

This paper mainly from the structural equation through conventional measurement and visual measurement model, analyzes the relationship between groundwater heavy metals and it, to establish the measurement model on the spatial distribution of groundwater heavy metals influencing factor model, analyzes the influence effect of various indicators on the spatial distribution of groundwater heavy metals, and their mutual influence effect. However, due to many characteristics of heavy metal groundwater, long-term use and management, it has become the research focus in the field of potential environmental protection, and has become an indispensable part of this study. Heavy metal pollution in groundwater not only endangers the safety of ecosystem, but also seriously damages the health of residents. A typical integration area between urban and rural areas is selected to understand the current situation of heavy metal pollution in groundwater, analyze the spatial distribution characteristics of heavy metal ions, and conduct qualitative and quantitative research on heavy metal pollution sources in groundwater, which has important theoretical and practical value for promoting urban sprawl, It also provides a scientific basis for the local government to formulate the strategy of protecting water resources. Of course, this problem also has a certain impact on leisure agricultural tourism, and leisure agricultural tourism is one of the important ways to promote the structural adjustment of agricultural sector and increase farmers' income, which is also an important practical process of developing multifunctional agriculture. In this paper, through structural equation analysis of underground heavy metal factor model, to understand its impact on leisure agricultural tourism, promote the development of leisure agricultural tourism.