Spatial Distribution Of Heavy Metals Research Articles

Multiple effects of submerged plants on microplastics-heavy metals redistribution and combination in the hyporheic sediment

Submerged plants (SP) in the hyporheic sediment (HS) dynamically alter the spatial distributions of heavy metals (HMs) and microplastics (MPs). In this study, we examined the redistribution and combination of HMs and MPs in the HS surrounding the SP (SSP) and non-nearby the SP (NSP) in the Weihe River Basin. The strong bioconcentration capacity of SP directly caused a decrease of HMs in the SSP (Bioconcentration Factors: SSP>NSP, 1.07>1.00). Algal proliferation at high nutrient concentrations strengthened the interception of MPs by SP (SSP-MPs >NSP-MPs, 495>315 items/kg). The significant correlation between SSP-HMs and SSP-MPs indicates the formation of MPs-HMs. The concentration of SSP-HMs was greater than NSP-HMs (Mn (462.95>437.66mg/kg)>Zn (63.46>60.51mg/kg)>V (53.98>50.67mg/kg)>Pb (21.98>18.47mg/kg)>As (18.36>15.65mg/kg). This finding implies that the MPs trapped by the SP indirectly contribute to elevating SSP-HMs, which showed higher pollution risk (Nemerow Pollution Index: 1.37>1.22; Contamination Factor: V, 0.87>0.82, Zn, 0.95>0.90, As, 1.61>1.41, Pb, 0.98>0.88). Furthermore, SP can reduce NSP contamination by proactively collecting pollutants into SSP, endangering the integrity of rivers through the ingesting of hydrobiont. Our study provides theoretical suggestions for the application SP to improve ecological health in complex environment.

Current levels of inorganic and organic pollutants in Romanian marine waters: Implications for ecosystem health

Human activities continue to impact the Romanian Black Sea, with implications for the marine ecosystem. This study presents new data (2023) on the spatial distribution of heavy metals, persistent organic pollutants, and nutrients in Romanian Black Sea waters. Our findings reveal the influence of various pollution sources, including the Danube River, municipal and industrial discharges, and maritime activities. Nutrient loads, primarily from the Danube and diffuse sources, exert pressure on the pelagic habitat. While there has been some improvement in nutrient conditions, the potential for increased eutrophication due to climate change remains a concern. Understanding the spatial distribution of pollutants and identifying their sources is crucial for developing effective management strategies to protect the marine environment.

Moss Technique Used to Monitor the Atmospheric Deposition of Trace Elements on the Territory of the Ryazan Region, Russia

Instrumental neutron activation analysis in combination with the method of moss biomonitoring for the assessment of atmospheric deposition of heavy metals and other elements was applied for the first time in the Ryazan Region in Russia. Concentrations of 42 macro, micro and trace elements in 63 moss samples (Hylocomium splendens and Pleurozium schreberi, as well as Hypnum cupressiforme, Sciuro-hypnum sp., Plagiothecium sp. and Ptilium sp.), collected on a relatively uniform grid in the study area, were determined. On the basis of analytical results using GIS technologies, maps of the spatial distribution of heavy metals and other toxic elements were constructed for the Ryazan region. The method of multivariate statistical analysis (factor analysis) was used to identify the main sources of pollution—large industrial facilities located in this region. The obtained results were included in the Atlas of Atmospheric Deposition of Heavy Metals, which is published by the UN Commission on Long-Range Transboundary Air Pollution (UNECE ICP Vegetation).

A geographically weighted neural network model for digital soil mapping of heavy metal copper in coastal cities

Assessing the spatial distribution of heavy metals in soil is essential for mitigating risks to human health and ensuring the sustainable use of soil resources. This study proposed a geographically weighted neural network (GWNN) model, leveraging deep learning and geographically weighted regression (GWR). The model was designed based on the GWR concept to address the spatial autocorrelation of copper (Cu) in soil and incorporated convolutional neural network (CNN) to capture the nonlinear relationships between Cu and environmental covariates. The GWNN model was compared with GWR, extreme gradient boosting (XGBoost), and artificial neural network (ANN) models. XGBoost was employed to select important environmental covariates and spatial autocorrelation of Cu concentrations was assessed using Moran’s I. The model’s performance was evaluated using 10-fold cross-validation, and prediction uncertainty was quantified with 100 bootstrap models. The results indicated that temperature covariates were the most significant predictors of soil Cu concentrations. The R2 values for Cu prediction accuracy were 0.60 for GWNN, 0.53 for ANN, 0.49 for XGBoost, and 0.32 for GWR. The spatial distribution of Cu showed a trend of higher concentrations in the north and lower concentrations in the south, consistent with spatial clusters identified by local Moran’s I. The mean uncertainty of the 90% confidence interval for GWNN was 16.49%, closely aligning with XGBoost (15.44%) and ANN (16.29%) and significantly outperforming the GWR (18.25%). Overall, the GWNN model demonstrated strong predictive accuracy and low uncertainty, offering an improved approach for digital soil mapping applications.

Prediction of soil heavy metal content around mine tailings using multiple methods combined with transformed hyperspectral reflectance data

The extensive accumulation of tailings can potentially cause heavy metal contamination in the surrounding farmland soil. Accurately predicting the spatial distribution of heavy metals in farmland soil is crucial for assessing the potential environmental hazards of tailings.This study focuses on the spatial distribution and the quantitative prediction of heavy metals (chromium (Cr), vanadium (V), and copper (Cu)) in soils surrounding mine tailings using advanced spectral data analysis and multiple prediction models. The original hyperspectral reflectance data were processed using first-order differential (FD), second-order differential (SD), reciprocal logarithmic (LR), and continuum removal (CR) transformations to highlight the positions of characteristic bands. Multiple linear regression (MLR), stepwise linear regression (SLR), partial least squares regression (PLSR), random forest (RF), and back propagation artificial neural network (BP-ANN) models were used to establish inversion models for Cr, V, and Cu based on bands with high correlation coefficients. The performance of the inversion models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and residual predictive deviation (RPD). The results indicate that the raw hyperspectral data from the measured soil exhibit a weak response to heavy metal content in the study area. However, applying FD, SD, and CR transformations significantly enhances the sensitivity of soil spectral data to heavy metal concentrations, facilitating subsequent modeling. Among these, the SD transformation is particularly beneficial for modeling the Cr and Cu elements in the soil. For the V element, the FD transformation yields data that are more suitable for modeling. Regarding the inversion models based on the measured spectral data, the BP-ANN model exhibited the best predictive performance. Specifically, when combined with SD spectral data, the BP-ANN achieved the highest predictive accuracy for Cu content (R² = 0.85, RPD = 2.12). The RF model demonstrated the next best performance, with its optimal inversion model also utilizing SD spectral data for predicting Cu content (R² = 0.76, RPD = 1.90). On the other hand, the MLR model exhibited the poorest performance and is unsuitable for predicting heavy metal content in the region using the measured spectral data. This study highlights the potential of spectral data in environmental monitoring and provides a technical reference for the inversion assessment and regulation of heavy metals in farmlands surrounding tailing sites.

Chemometric evaluation, source apportionment, and health risk analysis of natural spring water in Murree, outer Himalayas

IntroductionContamination of water is a critical threat to human health at a global level. Water pollutants, such as heavy metals, can have adverse effects on the well-being of humans, animals, and the natural ecosystem of a region. Study AreaMurree is the most visited tourist destination in Pakistan. The rural population of Murree uses natural spring water for drinking, household use, and irrigation. MethodsThis study assessed the elemental concentration of water from 20 natural springs in Murree using Inductively Coupled Plasma Optical Emission Spectroscopy. Source apportionment and spatial distribution of heavy metals were assessed using statistical approaches such as Pearson's correlation coefficient, principal component analysis, and interpolation. The study assessed the quality of water for drinking and irrigation in Murree's natural spring water using the metal index, sodium absorption ratio, magnesium absorption ratio, percentage sodium, Kelly's ratio, and salinity hazard. The health risks associated with heavy metals were assessed by computing Average daily dose, Hazard quotient, Hazard Index, and Cancer Risk. ResultsThe mean concentration of metals in mgL-1 varied in the following order: Ca (51.23) > Na (22.3) > Mg (16.26) > Si (6.51) > K (1.59) > Se (1.17) > Sr (0.48) > Ba (0.209) > Al (0.060) > Li (0.015) > Zn (0.005) > Fe (0.0033) > Ni (0.0032) > Cr (0.001). Metal index was calculated for Al (0.3), Ba (0.29), Cr (0.024), Ni (0.14), Fe (0.004), Zn (0.002), Sr (0.07), Mg (0.32), and Ca (0.25), revealing low levels of metal pollution. The geology of the study area was identified as the primary source of heavy metals in the water. The estimated values of health hazards showed that ingestion is the primary exposure pathway, with children having a higher risk. The health hazards posed by the heavy metal contact from the waters of Murree are not alarming. Quality indices show that the irrigation water sourced from the natural springs is of satisfactory quality. ConclusionThe study concluded that the spring water of Murree has low concentrations of heavy metals; their concentration is dependent upon the geology of the study area and is good for drinking and irrigation.

Machine learning-driven source identification and ecological risk prediction of heavy metal pollution in cultivated soils

To overcome challenges in assessing the impact of environmental factors on heavy metal accumulation in soil due to limited comprehensive data, our study in Yangxin County, Hubei Province, China, analyzed 577 soil samples in combination with extensive big data. We used machine learning techniques, the potential ecological risk index, and the bivariate local Moran’s index (BLMI) to predict Cr, Pb, Cd, As, and Hg concentrations in cultivated soil to assess ecological risks and identify pollution sources. The random forest model was selected for its superior performance among various machine learning models, and results indicated that heavy metal accumulation was substantially influenced by environmental factors such as climate, elevation, industrial activities, soil properties, railways, and population. Our ecological risk assessment highlighted areas of concern, where Cd and Hg were identified as the primary threats. BLMI was used to analyze spatial clustering and autocorrelation patterns between ecological risk and environmental factors, pinpointing areas that require targeted interventions. Additionally, redundancy analysis revealed the dynamics of heavy metal transfer to crops. This detailed approach mapped the spatial distribution of heavy metals, highlighted the ecological risks, identified their sources, and provided essential data for effective land management and pollution mitigation.

Pollution Indices, Potential Ecological Risks and Spatial distribution of Heavy Metals in soils around Delta State, Nigeria

Pollution Indices, Potential Ecological Risks and Spatial distribution of Heavy Metals in soils around Delta State, Nigeria

Soil Heavy Metal(loid) Pollution Evaluation, Risk Assessment, and Source Analysis of a Mineral Processing Plant

Yunnan Province is rich in mineral resources. Early mining, processing, metallurgy, and other mining activities produce three industrial wastes (waste water, waste gas, and waste residue) causing environmental pollution. Considering the legacy site of a mineral processing plant in Yunnan as the research object, 21 sampling points in the study area and 12 control sampling points in the periphery were set up to determine the contents of the heavy metal(loid)s As, Hg, Cd, Cu, Ni, Pb, and Cr in the soil. The spatial distribution of heavy metal(loid)s was interpolated and analyzed using Arcmap10.8, and combined with the single-factor index, Nemero Comprehensive Pollution Index, and the health risk assessment method for the heavy metal(loid) pollution status and health risk of the soil were evaluated. The soil in the study area was acidic, with the largest average value of elemental As and the largest percentages of control and screening values. The results of the single-factor and Nemero composite pollution index showed the following trend: As > Pb > Cd > Cu > Ni > Hg. Cd, Cu, and Pb mainly originate from mining and metallurgy and Hg from the combustion of fossil fuels, while soil-forming substrates are the main sources of Ni. Pollution by As was the most prominent element, whereas pollution by Cd, Cu, and Pb in some areas also cannot be ignored to prevent negative impacts on residents. It is recommended to remediate and treat the soil on site for public events; therefore, this study fills the gap in studying potential ecological risks, human health risk assessments, and sources of exposure (oral ingestion, respiratory ingestion, dermal contact).

Risk assessment of heavy metals and total petroleum hydrocarbons (TPHs) in coastal sediments of commercial and industrial areas of Hormozgan province, Iran.

The significant increase in the pollution of heavy metals and organic pollutants, their stable nature, and their high toxicity are gradually becoming a global crisis. In a recent study, a comprehensive assessment of the spatial distribution of heavy metals and total petroleum hydrocarbons (TPHs), as well as an assessment of their ecological risks in the sediments of 32 stations located in commercial and industrial areas (Mainly focusing on petrochemical and power industries, desalination plants and transit Ports) of Hormozgan province (East and West of Jask, Bandar Abbas, Qeshm, and Bandar Lengeh) was performed during 2021-2022. The sediment samples were digested with HNO3, HCl and HF solvents. The concentration of heavy metals was determined with furnace and flame systems of atomic absorption spectrometer. The concentration of heavy metals showed significant spatial changes between stations. The ecological assessment indices between the regions indicated that the stations located in Shahid Bahonar Port, Suru Beach, and Khor gorsouzuan had a higher intensity of pollution than other places and significant risks of pollution, especially in terms of Cr and Ni. The average contamination degree (CD) (14.89), modified contamination degree (MCD) (2.48), pollution load index (PLI) (2.32), and potential ecological risk index (PERI) (100.30) showed the sediments in the area of Shahid Bahonar Port, Suru beach and Khor gorsouzuan, experience significant to high levels of pollution, especially Cr and Ni. Using contamination factor (CF) and Geoaccumulation index (Igeo), Cr was considered the most dangerous metal in the studied areas. Based on the global classification of marine sediment quality for the concentrations of TPHs, the sediments of the studied stations were classified as non-polluted to low pollution. In all regions, indices of the PELq (General toxicity) and CF (Contamination factor) were much lower than 0.1 and 1 respectively, showing the absence of adverse biological effects caused by TPHs in sediments. It is necessary to consider comprehensive and impressive strategies to control and reduce pollution of heavy metals, especially in the areas of Shahid Bahonar Port, Suru Beach, and Khor gorsouzuan, so that the sources of this pollution are required to be identified and managed.

Analysis of the Spatial Distribution of Heavy Metals in Soil of Sewage-Irrigated Agricultural Fields in Arid Regions and Comprehensive Evaluation Methods

Analysis of the Spatial Distribution of Heavy Metals in Soil of Sewage-Irrigated Agricultural Fields in Arid Regions and Comprehensive Evaluation Methods

Heavy Metal Concentration Estimation for Different Farmland Soils Based on Projection Pursuit and LightGBM with Hyperspectral Images.

Heavy metal pollution in farmland soil threatens soil environmental quality. It is an important task to quickly grasp the status of heavy metal pollution in farmland soil in a region. Hyperspectral remote sensing technology has been widely used in soil heavy metal concentration monitoring. How to improve the accuracy and reliability of its estimation model is a hot topic. This study analyzed 440 soil samples from Sihe Town and the surrounding agricultural areas in Yushu City, Jilin Province. Considering the differences between different types of soils, a local regression model of heavy metal concentrations (As and Cu) was established based on projection pursuit (PP) and light gradient boosting machine (LightGBM) algorithms. Based on the estimations, a spatial distribution map of soil heavy metals in the region was drawn. The findings of this study showed that considering the differences between different soils to construct a local regression estimation model of soil heavy metal concentration improved the estimation accuracy. Specifically, the relative percent difference (RPD) of As and Cu element estimations in black soil increased the most, by 0.30 and 0.26, respectively. The regional spatial distribution map of heavy metal concentration derived from local regression showed high spatial variability. The number of characteristic bands screened by the PP method accounted for 10-13% of the total spectral bands, effectively reducing the model complexity. Compared with the traditional machine model, the LightGBM model showed better estimation ability, and the highest determination coefficients (R2) of different soil validation sets reached 0.73 (As) and 0.75 (Cu), respectively. In this study, the constructed PP-LightGBM estimation model takes into account the differences in soil types, which effectively improves the accuracy and reliability of hyperspectral image estimation of soil heavy metal concentration and provides a reference for drawing large-scale spatial distributions of heavy metals from hyperspectral images and mastering soil environmental quality.

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.

Quantitative effects of anthropogenic and natural factors on the spatial distribution of heavy metals and bacterial communities in sediments of the Pearl River Delta

Heavy metal (HM) contamination of aquatic ecosystems has increasingly posed threats to ecological balance and health. Analysis of sediments to determine water pollution sources is widely reported, while quantitative research about the effects of environmental factors on the distribution of HMs and relevant microbial communities is limited. Here, we investigated 6 kinds of HM pollution characteristics (As, Cd, Cr, Cu, Pb, and Zn) of the Pearl River Delta (PRD) by collecting surface sediment samples around the main stream and estuary and explored the influencing factors with a geographical detector method (GDM). The shifts in the corresponding microbial community was further analyzed, and the influence mechanism was clearly elucidated using partial least squares path modeling (PLS-PM). Results showed that (i) most HM contents of the sediments greatly exceeded the local background values, wherein, Cd had the highest pollution level; (ii) temperature was considered the dominant natural factor of HM distribution, except for Pb, with an influence degree of 0.68–0.97; (iii) industrial activity is an significant anthropogenic factor for HM distribution, and the electroplating industry was the main source for Cr, Cu and Zn; (iv) the network analysis revealed that As, Cu and Cr were enriched in some HM-tolerant bacteria (mainly Chloroflexi, Acidobacteriota and Proteobacteria), while heavy Cd pollution induced a reduction in sensitive bacterial taxa; (v) PLS-PM demonstrated that human activities largely affect bacterial communities by causing HM pollution. This study could provide guidance for better management of HM pollution control practices, such as specific industrial governance and layout optimization.

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.