Spatial Distribution Of Heavy Metals Research Articles

A GIS-based analysis of heavy metals around Otukpo rice mill in Benue state

Heavy metals are primarily sourced from natural pedo-geochemical backgrounds and anthropogenic occurrences like composting, sewage sludge, aerosol deposition, and waste animal manure. The study aims to determine the heavy metals within the rice mill in Otukpo to understand the spatial distribution with the objectives to examine the heavy metals within the identified activity areas in the Otukpo rice mill, to determine the spatial distribution of heavy metals within the study area and to compare heavy metal values with the acceptable limits. Soil samples were collected from ten locations at Otukpo Rice Mill to analyze heavy metal concentrations. The samples were collected using sterile bottles and a stainless trowel, and the soil samples were analyzed for the concentrations of Cadmium, Copper, Lead, Nickel, Zinc, Mercury, Iron, Arsenic, and Chromium using the Atomic Absorption Spectrophotometry method. The result was analyzed using interpolation to see the variation of the Heavy metals within the various activity areas. The result of the study reveals that Iron has the highest concentration at the Entrance point and lowest at the Generator house with a range value of 3.92 – 1.12mg/kg respectively. The highest concentration of Zinc was recorded at the Mechanic workshop while the lowest is at the Drying point with a range value of 0.135 – 0.872mg/kg respectively. The Husk dumpsite recorded the highest concentration of Copper (0.666mg/kg) with the Drying point recording the least (0.088 mg/kg). The study reveals uneven concentrations and highlights the effectiveness of the Geographic Information System in mapping heavy metal concentrations.

Heavy metal content of mountainous agricultural soils and ecological risk assessment in Gadabay district, Azerbaijan

Objective: The objective of this study was to i) investigate the distribution characteristics of heavy metals, ii) examine their relationships with basic soil properties, and iii) characterize their potential sources and ecological risks. Material and Methods: A total of 85 samples were collected from the surface horizon (0-15 cm) of Chernozem soil in a representative agricultural area located in the Lesser Caucasus Mountains (Gadabay district), and heavy metal contents and basic soil properties were determined. Results: The mean of the heavy metals followed a decreasing order: Mn>Zn>Cu>Cr>Ni>Co>Pb>As>Se>Cd and some heavy metals (As, Cd, Se) exhibited a fragmented distribution. Co, Cr, Mn, Se and Zn contents were higher than the background concentration, while others (As, Cd, Co, Cu, Pb and Zn) exceeded the maximum permissible concentration. Conclusion: The spatial distribution of heavy metals was characterized by their typical and element-specific distribution. The noted variability was likely related to geologic features (soil mineralogy), mining history and agricultural practices. Notably, the presence of limestone and clay minerals contributed to the association of Cd, Cu and Se and Mn and Pb, respectively. Sand content influenced the mobility of Cr and Cu. The relations between pH and Cr, Ni and Se was the indication of the influence of the parent material on the distribution of these metals.

Geospatial evaluation and bio-remediation of heavy metal-contaminated soils in arid zones

Introduction: Soil pollution directly impacts food quality and the lives of both humans and animals. The concentration of heavy metals in Egypt’s drain-side soils is rising, which is detrimental to the quality of the soil and crops. The key to reducing the detrimental effects on the ecosystem is having accurate maps of the spatial distribution of heavy metals and the subsequent use of environmentally sustainable remediation approaches. The objective of this work is to assess soil contamination utilizing spatial mapping of heavy metals, determine contamination levels using Principal Component Analysis (PCA), and calculate both the contamination severity and the potential for bioremediation in the soils surrounding the main drain of Bahr El-Baqar. Furthermore, evaluating the capacity of microorganisms (bacteria, fungi, and “Actinomycetes) to degrade heavy elements in the soil.Methodology: 146 soil sample locations were randomly selected near the Bahr El-Baqar drain to examine the degree of soil pollution Ordinary Kriging (OK), method was used to map and analyze the spatial distribution of soil contamination by seven heavy metals (Cr, Fe, Zn, Cd, Pb, As, and Ni). Modified contamination degree (mCd) and PCA were used to assess the research area’s soil pollution levels. The process involved isolating, identifying, and classifying the microorganisms present in the soil of the study area. The study findings showed that variography suggested the Stable model effectively matched pH, SOM, and Cd values. Furthermore, the exponential model proved suitable for predicting Fe, Pb and Ni, while the spherical model was appropriate for Ni, Cr, and Zn.Results: The study revealed three levels of contamination, with an extremely high degree (EHDC) affecting approximately 97.49% of the area. The EHDC exhibited average concentrations of heavy metals: 79.23 ± 17.81 for Cr, 20,014.08 ± 4545.91 for Fe, 201.31 ± 112.97 for Zn, 1.33 ± 1.37 for Cd, 40.96 ± 26.36 for Pb, 211.47 ± 13.96 for As, and 46.15 ± 9.72 for Ni. Isolation and identification of microorganisms showed a significant influence on the breakdown of both organic and inorganic pollutants in the environment. The study demonstrated exceptionally high removal efficiency for As and Cr, with a removal efficiency reached 100%, achieved by Rhizopus oryzae, Pseudomonas aeruginosa, and Bacillus thuringiensis.Conclusion: This study has designated management zones for soil contamination by mapping soil pollutants, geo-identified them, and found potential microorganisms that could significantly reduce soil pollution levels.

Pollution Assessment and Spatial Distribution of Heavy Metals in Surface Waters and Bottom Sediments of the Krzna River (Poland)

Due to their toxicity, lack of bioaccumulation and biodegradability, and ease of binding to sediments, heavy metals are considered the main pollutants of rivers. It is, therefore, necessary to control and monitor these pollutants. The present study analyzed the Krzna River, which flows in southeastern Poland and has an outlet to the Bug River. Over much of its length, the Bug River forms Poland’s border with Belarus, while its origin is in Ukraine. The main purpose of the conducted research was a qualitative and quantitative analysis of selected heavy metals, i.e., Ni, Pb, Zn, Cd, and Cu, in bottom sediments and surface waters of the Krzna River. The secondary objectives were to evaluate the level of contamination of the studied matrices and identify the sources of pollution. Eighty samples of water and bottom sediments from the Krzna River were collected for the analysis. Due to the varying distribution of metals under the influence of changes in temperature, precipitation, and humidity, the samples were collected in May and August 2023. The average cadmium content in the sediments studied was the same in both May and August, at 0.6 mg/kg. In contrast, the nickel content of the sediments ranged from 4.6 to 6.1 mg/kg in May and from 4.8 to 6.8 in August. Only nickel and cadmium of the five heavy metals tested were present in amounts exceeding the geochemical background value. Analysis of the results indicates that only a minimal amount of heavy metals remain dissolved in the surface waters, and the remainder contaminates the sediments. The average concentrations of metals in the studied bottom sediments and surface waters were as follows: zinc > lead > nickel > copper > cadmium. The content of metals in the studied sediments was not high, but at the same time, their presence above the geochemical background indicates anthropogenic human activity. Any changes in hydrodynamic conditions and various environmental factors may result in the re-release of heavy metals contained in sediments into surface waters.

Seasonal variation and spatial distribution of heavy metal (loid)s concentration in groundwater and surface water from hard-rock terrain, Ranchi, India

Seasonal variation and spatial distribution of heavy metal (loid)s concentration in groundwater and surface water from hard-rock terrain, Ranchi, India

[Spatial and Temporal Distribution and Risk Assessment of Heavy Metals in Surface Water of Changshou Lake Reservoir, Chongqing].

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.

Bibliometric and visual analysis of heavy metal health risk assessment: development, hotspots and trends

Due to the widespread presence and harmfulness of heavy metals in the environment, scholars around the world have evaluated the exposure characteristics and health risks of heavy metals. To understand the status, hotspots, and development treads of heavy metal health risk assessment research, we used bibliometric analysis tools to conduct scientometric analysis of the literature related to the health risk assessment of heavy metals in the Web of Science database from 2000 to 2022. The analysis results indicate that research related to heavy metal health risk assessment is rapidly developing in both developed and developing countries. China’s significant international influence in this field is worth noting, as there are many publications and highly cited documents related to China. France and other developed countries also play an important role in this field due to their high centrality and strong bursts. The results of co-citation cluster analysis and keyword co-occurrence analysis indicate that in the past two decades, the primary research domains and hotspots of heavy metal health risk assessment have been the study of heavy metals in soil, dust, drinking water, vegetables, fish, and sediment. There is a specific focus on bioaccumulation, bioavailability, source apportionment, and spatial distribution of heavy metals. The main types of heavy metals studied are lead, cadmium, mercury, and zinc. The results of the bursts keywords analysis suggest that future research trends may focus more on the health risks of heavy metals in different functional areas of cities.

Ecological Risk Assessment and Source Analysis of Heavy Metals in Farmland Soil in Yangchun City Based on APCS-MLR and Geostatistics

Yangchun City, a typical polymetallic ore distribution area in Guangdong Province (China), was selected as the research region to study the content, distribution, source, and possible impacts of heavy metals (HMs) (Arsenic: As; Cadmium: Cd; Chromium: Cr; Copper: Cu; Mercury: Hg; Nickel: Ni; Lead: Pb; and Zinc: Zn) on the farmland soil of this City. According to our findings, the spatial distribution of HMs in Yangchun City shows higher concentrations in the north and southeast and lower in the west and other regions. Metal content in some sampled sites of the agricultural land exceeded the soil pollution risk screening values, particularly As (7.5%), Cd (12%), Cu (4%), Hg (14.5%), and Pb (3%). Additionally, the average content of As, Cu, Cd, Pb, Hg, and Zn from the studied areas surpassed the soil background value of Guangdong Province for all metals. The absolute principal component score-multiple linear regression (APCS-MLR) was used to identify potential sources of HMs in the soil samples. There are three potential sources identified by the model: traffic emissions, natural sources, and agricultural activities, accounting for 28.16%, 16.68%, and 14.42%, respectively. Based on the ecological risk assessment, the potential ecological risk (Eri = 310.77), Nemero pollution index (PN = 2.27), and multiple possible effect concentration quality (mPECQs = 0.23) indicated that the extent of heavy metal pollution in the soil samples was moderate. Three sources were identified: traffic emissions, natural sources, and agricultural activities. We suggest that by combining the above results, a monitoring and early warning system focused on Cd and Hg can be established. The system could utilize geographic information systems and remote sensing technologies to achieve dynamic monitoring and prediction of pollution. Regular testing of soils and sustainable management practices are also recommended to control and remediate contamination.

Zoning of Dust Heavy Metals in Arak Plain Using Pollution Indicators and Geographic Information System (GIS)

Background: In regions with heightened pollutant concentrations, especially in industrial and urban areas, dust plays a crucial role in carrying complex metal components, posing environmental challenges and health risks. This study utilized pollution indicators and geographic information system (GIS) to delineate the spatial distribution of heavy metals in the Arak plain. Methods: Dust samples from 30 stations across the Arak plain were systematically collected through random sampling. Analysis using inductively coupled plasma spectrometry (ICP-OES) allowed the calculation of pollution indices (PI) and the Nemerow Integrated Pollution Index (NIPI) for lead (Pb), zinc (Zn), copper (Cu), nickel (Ni), and iron (Fe). GIS generated spatial distribution maps depicting metal pollution. Results: The average concentrations were 45.5 mg/kg for Pb, 10.7 mg/kg for Zn, 0.47 mg/kg for Cu, 30.8 mg/kg for Ni, and 0.206 mg/kg for Fe. Analysis of PI, NIPI, and spatial distribution maps revealed heightened pollution in the northeast, center, south, and southwest areas of the Arak plain, attributed to human activities like heavy vehicle traffic, high population density, concentrated agriculture, and specific industrial operations. Conclusion: The study recommends mitigation strategies, including biological methods like phytoremediation, promotion of public transportation, mandatory environmental standards for industries, and encouragement of green practices. These initiatives aim to address and reduce environmental pollution in the Arak plain.

Correcting correlation quality of portable X-ray fluorescence to better map heavy metal contamination by spatial co-kriging interpolation

High-precision mapping based on portable X-ray fluorescence (PXRF) data is currently being studied extensively; however, owing to poor correlation with soil metal concentration, the original PXRF data directly used for co-kriging interpolation (CKI) cannot accurately map contaminated sites with heterogeneous concentrations. Therefore, this study selected a landfill-contaminated site for research, explored the best correlation mode between PXRF variants and actual heavy metal concentration, analyzed the impact of improving the correlation model on the CKI of the spatial distribution of heavy metals, and explored the most appropriate CKI mode and point density. The results showed the following: (1) After nonlinear transformation, the correlation model between PXRF and the actual concentration was significantly improved, and the correlation coefficients of five heavy metals increased from 0.214–0.232 to 0.936–0.986. (2) The introduction of corrected PXRF data significantly improves the accuracy of CKI. Compared with the original PXRF co-kriging interpolation (OP-CKI), the ME of the corrected PXRF co-kriging interpolation (CP-CKI) for Zn, Pb, and Cu decreased by 78.2 %, 45.5 %, and 65.3 %, respectively. In terms of the spatial distribution of heavy metal pollutant concentrations, CP-CKI effectively improved the influence of local anomalous high-value points on the interpolation accuracy. (3) When the sample density measured by inductively coupled plasma mass spectrometry (ICP-MS) was less than 4 boreholes/hm2, CKI accuracy decreased significantly, indicating that the sample density should not be less than a certain threshold during CKI. (4) When the sample density measured by PXRF exceeded 7 boreholes/hm2, the mean error and root mean square error of CKI continued to decrease, suggesting that the introduction of enough sample density measured by PXRF can effectively improve the accuracy of CKI.

[Prediction of Spatial Distribution of Heavy Metals in Cultivated Soil Based on Multi-source Auxiliary Variables and Random Forest Model].

Spatial prediction of the concentrations of soil heavy metals (HMs) in cultivated land is critical for monitoring cultivated land contamination and ensuring sustainable eco-agriculture. In this study, 32 environmental variables from terrain, climate, soil attributes, remote-sensing information, vegetation indices, and anthropogenic activities were used as auxiliary variables, and random forest (RF), regression Kriging (RK), ordinary Kriging (OK), and multiple linear regression (MLR) models were proposed to predict the concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn in cultivated soils. In comparison to those of RK, OK, and MLR, the RF model had the best prediction performance for As, Cd, Cr, Hg, Pb, and Zn, whereas the OK and RK models had highest prediction performance for Cu and Ni, respectively, showing that R2 was the highest, and mean absolute error (MAE) and root mean square error (RMSE) were the lowest. The prediction performance of the spatial distribution of soil HMs under different prediction methods was basically consistent. The high value areas of eight HMs concentrations were all distributed in the southern plain area. However, the RF model depicted the details of spatial prediction more prominently. Moreover, the importance ranking of influencing factors derived from the RF model indicated that the spatial variation in concentrations of the eight HMs in Lanxi City were mainly affected by the combined effects of Se, TN, pH, elevation, annual average temperature, annual average rainfall, distance from rivers, and distance from factories. Given the above, random forest models could be used as an effective method for the spatial prediction of soil heavy metals, providing scientific reference for regional soil pollution investigation, assessment, and management.

Health risk assessment of heavy metals based on source analysis and Monte Carlo in the downstream basin of the Zishui

In this study, stone coal mines in the lower reaches of the Zijiang River were adopted as the research object. To analyze the spatial distribution, sources, and health risks of heavy metals in the surrounding soil of stone coal mines, 82 topsoil samples were collected in the study area, and the contents of 8 heavy metals including Cd, Hg, As, Cr, Pb, Cu, Ni, and Zn were determined. The spatial distribution of heavy metals was analyzed using ArcGIS, and the pollution sources of heavy metals were identified using Positive matrix factorization (PMF). Then, Monte Carlo and health risk assessment models were used to evaluate the health risks of different populations. The results showed that the average content of heavy metals followed the order of Zn > Cr > Pb > Cu > Ni > As > Cd > Hg, and the contents of all heavy metals were higher than the soil background values of Hunan Province. The high-value areas of heavy metals content were mostly concentrated in the central region close to areas with a notable concentration of stone coal mines. PMF identified four pollution sources, namely, mining activities (26.9%), atmospheric deposition (18.8%), natural sources (32.8%) and agricultural sources (21.5%). The carcinogenic and non-carcinogenic risks for children were higher than those for adults, with As and Cd posing higher carcinogenic risks to children. Based on the source of health risks, it was determined that the health risks could be primarily attributed to agricultural sources, and As was the main heavy metal causing health risks. This study provides theoretical support for treating heavy metal pollution in mining basins.

Suspended particulate matter affects the distribution and migration of heavy metals in the Yellow River

Suspended particulate matter (SPM) is an important heavy metal transporter in water. As a well-known high-SPM river, its impact on the distribution and migration of heavy metals in the Yellow River (YR) deserves special attention. In this study, the spatial distributions of heavy metals in surface water and SPM of the YR were investigated. The results indicate that the concentrations (dissolved and particulate phases) and bioavailability (particulate phase) of most heavy metals were higher during the rainy season than during the dry season. A considerable proportion of heavy metals (>70 %) was transported by SPM and fine particles (clay) controlled the pollution status of heavy metals in the YR. This could lead to higher heavy metal concentrations in the SPM midstream and downstream during the rainy season and higher heavy metal concentrations in upstream during the dry season. Heavy metal adsorption experiments showed that specific combination methods (such as binding with carbonate) between Cd and SPM may cause SPM to act as a source of Cd midstream and downstream. This study provides a new perspective on the effects of SPM on heavy metal distribution and migration in the YR.

Spatial distribution of heavy metals in the sediments of River Ganges, India: Occurrence, contamination, source identification, seasonal variations, mapping, and ecological risk evaluation

Present study analyzed the seasonal and spatial distribution patterns, sources, and ecological risks of seven heavy metals (Cr, Fe, Ni, Cu, Zn, Cd and Pb) in the sediments of River Ganges, finding that the majority of concentrations were lithologic, except for Cd, which was significantly higher than background standards. Elevated values of geochemical indices viz. Igeo, CF, RI, Cd, mCd, HQ, mHQ, and PN suggest moderate to high ecological risk in the benthic environment and its organisms due to the synergistic effect of heavy metals. The PEC-Qmetals revealed 8–10 % toxicity in the upstream and downstream sites, due to the influence of agricultural activities. Multivariate statistical techniques (PCM and PCA) indicated that Cd and Pb predominantly originated from anthropogenic sources, while other metals primarily derived from geological background. These geochemical findings may help to understand the potential risks and recommend strategies to mitigate the effects of metallic contamination in river sediments.

Spatial distribution prediction of soil heavy metals based on sparse sampling and multi-source environmental data

Predicting the precise spatial distribution of heavy metals in soil is crucial, especially in the fields of environmental management and remediation. However, achieving accurate spatial predictions of soil heavy metals becomes quite challenging when the number of soil sampling points is relatively limited. To address this challenge, this study proposes a hybrid approach, namely, Light Gradient Boosting Machine plus Ordinary Kriging (LGBK), for predicting the spatial distribution of soil heavy metals. A total of 137 soil samples were collected from the Shengli Coal-mine Base in Inner Mongolia, China, and their heavy metal concentrations were measured. Leveraging environmental covariates and soil heavy metal data, we constructed the predictive model. Experimental results demonstrate that, in comparison to traditional models, LGBK exhibits superior predictive performance. For copper (Cu), zinc (Zn), chromium (Cr), and arsenic (As), the coefficients of determination (R²) from the cross-validation results are 0.65, 0.52, 0.57, and 0.63, respectively. Moreover, the LGBK model excels in capturing intricate spatial features in heavy metal distribution. It accurately forecasts trends in heavy metal distribution that closely align with actual measurements. Environmental ImplicationThis study introduces a novel method, LGBK, for predicting the spatial distribution of soil heavy metals. This method yields higher-precision predictions even with a limited number of sampling points. Furthermore, the study analyzes the spatial distribution characteristics of Cu, Zn, Cr, and As in the grassland coal-mine base, along with the key environmental factors influencing their spatial distribution. This research holds significant importance for the environmental regulation and remediation of heavy metal pollution.

Spatial distribution of heavy metals, source identification, risk assessment and particulate matter in the M4 motorway.

Metal-containing dust is a potential severe environmental and human health threat. Metals present in dust may originate from car exhausts, tear and wear of tires, and vehicular emissions, which are less manageable. Metal-containing dust from roads can contaminate the soils, and crops alongside motorway. This study aimed to investigate the Pb Cd, Cu, Ni, and Zn concentrations in dust, soil, and vegetation collected from the M4 motorway Faisalabad. The results indicated that average metal concentrations in dust from all sites varies (Pb) 44.01mgkg-1, (Cd) 1.22mgkg-1, (Cu) 49.5mgkg-1, (Ni) 28.3mgkg-1, and (Zn) 113.7mgkg-1. The pollution assessment indices CF and PLI of Industrial city and Painsra had comparatively maximum levels of environmental pollution. Moreover, the geo-accumulation index (Igeo) of metals was high at Chak 115 and Painsra, while Igeo at ten sites was in the following descending order: Cd > Pb > Cu > Ni > Zn. Furthermore, it was identified that the maximum ecological risk index (Eir) was in declining order, i.e., Cd > Pb > Cu > Ni > Zn, at all sites. The potential ecological risk was categorized as high risk in all respective sites. The particulate matter fractions PM2.5 and PM10 represented the maximum risk at the Industrial city site, which was unhealthy, although the Painsra site had poor air quality. The total suspended particulate was classified as hazardous at FDA city and Painsra. In contrast, food crops (maize, sugar cane, and sesame) and soil along the M4 motorway have similar Pb, Cd, Cu, Ni, and Zn contamination patterns like dust. However, two crops, maize and sugarcane, along the M4 motorway were found to be more polluted. The level of metals contamination through dust disposition was consistently higher adjacent to roads, possibly indicating depraved impacts on food crops.

The Spatial Distribution and Influencing Factors of Heavy Metals in Soil in Xinjiang, China

Heavy metal pollution has been a problem of concern in soil ecology in recent decades. This study investigated the spatial distribution of heavy metals and their pollution levels in the soil of Xinjiang, based on the data of heavy metals published in the literature in the past five years, by using a geostatistical method, pollution index method, and geographic information system (GIS)-based spatial analysis. Additionally, the effects of five economic development indicators, such as population and industrial activities on the accumulation of heavy metals in soil, were explored by correlation analysis. The results showed that the average contents of Cd, Cr, Cu, Ni, Pb, and Zn in the soils were 2.858, 1.062, 1.194, 1.159, 1.192, and 1.086 times higher than the background values in Xinjiang, respectively. The semi-variance functions indicated that the Cd and Pb block gold coefficients of soils were greater than 25% and less than 50%, with an obvious spatial correlation. The spatial patterns showed that the high values of Cd, Cr, Cu, Ni, Pb, and Zn were mainly distributed in Karamay, Changji, Tacheng, and Kashi areas, with an overall decreasing trend from north to south, and the pollution index showed that the pollution of heavy metal Cd in soil was the most serious. Furthermore, Karamay, Changji, and Kashi areas were at heavy pollution levels. Correlation analysis showed that heavy metal Pb in the soil was significantly positively correlated with the agricultural GDP in Xinjiang, while Cd was correlated significantly and positively with comprehensive energy consumption and more significantly with industrial GDP. Thus, this study could provide a scientific basis for local evaluation of soil environmental quality and prevention and control of soil heavy metal pollution, which is of great significance for understanding the impact of human activities.

[Evaluation and Prediction of Environmental Capacities of Heavy Metals in the Surface Sediments of Lakes in Huoqiu County].

To clarify the environmental capacity of heavy metals in sediments of Chengxi Lake and Chengdong Lake in Huoqiu County, 30 sediment samples were collected from Chengxi Lake and Chengdong Lake, respectively. The environmental capacity characteristics and spatial distribution of heavy metals in sediments of Chengxi Lake and Chengdong Lake were studied using the comprehensive environmental capacity index, the static and dynamic environmental capacity was estimated, and the change trend of nearly one hundred years was predicted. The results showed that the average contents of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn in the sediments of Chengxi Lake and Chengdong Lake did not exceed the national soil risk screening values. The average values of the single environmental capacity index of heavy metals in the sediments of Chengxi Lake and Chengdong Lake were in the following orders:Ni(0.81)<Zn(0.83)<Cu(0.88)<Cr(0.91)<As(0.95)<Cd(0.98)=Pb(0.98)<Hg(1.00) and Ni(0.89)<Zn(0.91)<Cu(0.96)<Cr(0.98)=As(0.98)<Hg(1.01)=Pb(1.01)<Cd(1.17), respectively. Except for Hg, Pb, and Cd in the sediments of Chengdong Lake, the other elements were basically in the medium capacity. The mean of PI of Chengdong Lake was significantly higher than that of Chengxi Lake, and Chengxi Lake and Chengdong Lake were in the middle capacity level as a whole. The static total capacity of heavy metal elements in the sediments of Chengxi Lake was smaller than that of Chengdong Lake sediments, and the static total capacity was in the same order as the existing capacity, with the order of Zn>Cr>Pb>Cu>Ni>As>Hg>Cd. Within 5 to 20 years, the static and dynamic annual capacity changed faster than that of other years, and the dynamic annual capacity of each year was greater than the static annual capacity.

Spatial distribution of heavy metals in surface sediments of the Kalametiya Lagoon in southern Sri Lanka: Insights into the pollution status and socio-economic interactions&amp;nbsp;

Heavy metal pollution has become a serious threat to coastal aquatic ecosystems. Therefore, this study, aimed to assess the spatial distribution of five selected heavy metals/metalloids, arsenic (As), cadmium (Cd), chromium (Cr), lead (Pb), and mercury (Hg), in surface sediment samples collected from the Kalametiya Lagoon in southern Sri Lanka. Sixteen (16) areas of the lagoon were sampled. The sediment samples were analysed for heavy metal content by using ICP-MS while the water samples were measured for salinity and pH. A questionnaire survey was conducted to investigate the possible sources of heavy metal pollution in the Lagoon. Water pH and salinity showed significant variations across the lagoon. The overall mean value of pH and salinity were 6.68 ± 0.17 and 2.9 ± 2.2 PSU, respectively. The spatial distribution of the heavy metals was not monotonous and showed a high spatial variation. The kernel density maps of the measured heavy metals demarcated several spatially different patches in the lagoon. The mean levels of As, Cd, Cr, Hg, and Pb were lower than the threshold effect level (TEL) although it was higher for Hg in the North inlet. Nevertheless, it was still lower than the potential effect level (PEL). Industrial sewage, river suspended sediments, and agrochemicals such as fertilizers and pesticides were identified as the possible sources for heavy metal loads. Accumulation of toxic heavy metals can be minimized by by-passing the freshwater inflow to the lagoon.  

Pollution Characteristics of Heavy Metals in Surface Sediments of the Shuimo River in Urumqi, China

Heavy metal pollution in the surface sediments of urban rivers has a significant influence on the safety of city residents. This study explores the features of heavy metal pollution in the surface sediments of the Shuimo River and provides a theoretical basis for decision makers regarding river management and restoration. This study uses principal component analysis and kriging interpolation to analyse the pH values and pollution characteristics of nine heavy metals (As, Pb, Zn, Cu, Ni, Fe, Mn, Cr, and V) in 23 surface sediments of the Shuimo River. The results showed that the pH value of the surface sediments along the direction of water flow had a quadratic curve trend. Kriging interpolation revealed consistency in the spatial distribution of heavy metals and Fe, and the peak value was from Qidaowan to Weihuliang. There were significant positive correlations (p &lt; 0.05) between Fe and Pb; Mn, Cr, V, Cu, and Zn; and Mn, Cr, and V. The principal component analysis showed that the main heavy metals in the surface sediments of the Shuimo River were Fe, Zn, Cu, and As. The total amount of heavy metals was in the order of Fe &gt; Mn &gt; Zn &gt; V &gt; Cr &gt; Ni &gt; Cu &gt; Pb &gt; As, ranging from 11.27~18,760.97 mg·kg−1. The cluster analysis classified the nine heavy metals into four categories: Zn and Cu in the first category; Ni in the second; As and Pb in the third; and V, Cr, Mn, and Fe in the fourth.