Spatial Distribution Of Heavy Metals Research Articles

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.

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.