Distribution Of Heavy Metals Research Articles

Heavy metal concentration in marine water: An assessment of ONGC’s blocks in Mumbai High Region, Arabian Sea

Heavy metal pollution, in the aquatic ecosystem, has become an area of concern garnering increasing attention since the past few decades. Some metals like manganese (Mn), copper (Cu), iron (Fe) and zinc (Zn) are biologically important for marine life. Other non-essential metals like lead (Pb), cadmium (Cd), and Arsenic (As) become toxic at higher concentrations. These metals are introduced into the marine ecosystem mainly due to anthropogenic activities. Hence, monitoring the heavy metal concentrations in these marine water over a period of time is of great help in checking the pollution level and identifying the trend, which in turn will be instrumental in formulating sustainable practices. The paper mainly focuses on the study of the concentration of lead (Pb), cadmium (Cd), and Arsenic (As) in sea water around the operational areas of ONGC in Waestern offshore. The distribution of heavy metals in the seawater of ONGC’s exploratory blocks in Mumbai High Region, Arabian Sea was studied. Fifty four sea water samples were collected as per OSPAR Commission guidelines from each blocks (Block A, Block B, Block C, Block D and Block E) of Mumbai High Region, Arabian Sea and processed samples were analyzed by ICP-MS for Pb, As, and Cd. Comparison of average results in studied 5 blocks with various seawater quality guidelines is discussed to assess the present level of contamination. The study reveals that seawater in study area is not contaminated with respect to perceived heavy metals. Generated data will assist in future for proactive measures to minimize the impact of anthropogenic sources.

Distribution of heavy metals in the sediments of Ganga River basin: source identification and risk assessment.

Sediment serves as a heavy metal store in the riverine system and provides information about the river's health. To understand the distribution of heavy metal content in the Ganga River basin (GRB), a total of 25-bed sediment and suspended particulate matter (SPM) samples were collected from 25 locations in December 2019. Bed sediment samples were analyzed for different physio-chemical parameters, along with heavy metals. Due to insufficient quantity of SPM, the samples were not analyzed for any physio-chemical parameter. The metal concentrations in bed sediments were found to be as follows: Co (6-20mg/kg), Cr (34-108mg/kg), Ni (6-46mg/kg), Cu (14-210mg/kg), and Zn (30-264mg/kg) and in SPM, the concentrations were Co (BDL-50mg/kg), Cr (10-168mg/kg), Ni (BDL-88mg/kg), Cu (26-80mg/kg), and Zn (44-1186mg/kg). In bed sediment, a strong correlation of 0.86 and 0.93 was found between Ni and Cr, and Cu and Zn respectively and no significant correlation exists between organic carbon and metals except Co. In SPM, a low to moderate correlation was found between all the metals except Zn. The risk indices show adverse effects at Pragayraj, Fulhar, and Banshberia. Two major clusters were formed in Hierarchal Cluster Analysis (HCA) among the sample points in SPM and bed sediment. This study concludes that the Ganga River at Prayagraj, Banshberia, and Fulhar River is predominately polluted with Cu and Zn, possibly posing an ecological risk. These results can help policymakers in implementing measures to control metal pollution in the Ganga River and its tributaries.

Distribution of heavy metals and annual effective doses from radiotoxic 210Po in different types of tobacco plant grown in Türkiye

Distribution of heavy metals and annual effective doses from radiotoxic 210Po in different types of tobacco plant grown in Türkiye

Accumulation of Heavy Metals in the Leaves of Some Woody Plants in Osh City

Pollution of the environment with harmful substances occupies a leading place among global environmental problems. This phenomenon is closely related to anthropogenic activity, especially in cities. The origin and chemical nature of harmful substances are different, but heavy metals occupy a special place among them. Over the past ten years, research aimed at studying the distribution of heavy metals in the environment and their accumulation in plants has significantly developed. Because the Sustainable Development Goals adopted by the UN in 2015 provide for by 2030, to reduce the negative environmental impact of cities per capita, including by paying special attention to air quality and the removal of urban and other waste. The purpose of our research was to determine the characteristics of the accumulation of heavy metals in the leaves of trees and shrubs growing in the park areas of Osh. The trees selected as objects were Salix babylonica L., Acer pseudoplatanus L., Populus ×canescens (Aiton) Sm., Platanus orientalis L., Juniperus virginiana L., planted in park areas near highways. The concentration of heavy metals was determined by the atomic absorption method in the Central Laboratory State Enterprise under the Ministry of Natural Resources, Ecology and Technical Control of the Kyrgyz Republic. Quantitative analysis revealed the presence of some heavy metals: manganese, copper, lead, strontium and zinc in all plant samples. Laboratory tests showed a high concentration of copper, exceeding the MAC. Based on the research results, we note the need to continue studying the influence of heavy metals on the physiological processes of various tree species in Osh and expanding the geography of research in the city’s microdistricts.

Strategy on rapid selection of woody species for phytoremediation in soils contaminated with copper, lead and zinc in Shanghai

The use of woody species for the remediation of heavy metal-contaminated soils is an environmentally friendly and economically viable strategy. This study investigates the phytoextraction abilities of 15 woody species for copper, lead and zinc in contaminated soil. The results indicated that all species showed phytoextraction ability, with metal concentrations varying from 5.59 to 27.45 mg·kg−1 for Cu, 2.79 to 16.75 mg·kg−1 for Pb and 22.13 to 185.72 mg·kg−1 for Zn in the stem tissues depending on the species. Pterocarya stenoptera, Paulownia fortunei and Salix matsudana were identified as the top performers in terms of overall phytoextraction capacity. Notably, their capacity to transport zinc exceeded that of copper and lead. The enrichment of copper, lead and zinc in the soil showed a synergistic effect in the presence of heavy metal. The distribution of heavy metals within plant tissues was affected by water content and the inherent toxicity of metals. The study highlights that the accumulation of tree biomass and water content in the stem play a significant role in determining the amount of heavy metals phytoextracted. This insight offers a quick method for the rapid selection of woody species for phytoremediation in urban soils contaminated with heavy metals.

Spatial distribution and migration characteristics of heavy metals at an abandoned industrial site in the Southwest of China

The rapid acceleration of global industrialization has rendered heavy metal contamination at abandoned industrial sites a severe challenge, particularly in geologically complex and fragile karst regions of Southwest China, posing significant threats to ecosystems and public health. However, existing research lacks a comprehensive understanding of the spatial distribution and migration mechanisms of heavy metals in this region. In this study, 523 soil samples and 30 groundwater samples were collected, and the pollution levels were systematically assessed using the Geo-Accumulation Index, Single Pollution Index, and Nemerow Integrated Pollution Index. Horizontal and vertical spatial heterogeneity was explored through Moran's I and Voronoi polygon analysis. Furthermore, 3D geological modeling and groundwater flow simulations were employed to investigate the influence of hydrogeological conditions on contaminant migration. The results indicate elevated concentrations of Cd, Hg, Pb, and As in the surface layer, with concentrations initially decreasing and then increasing with depth, likely due to the presence of discontinuous clay layers. Moran's I revealed significant clustering effects at depths of 0.2 meters and 4 meters, while Voronoi analysis confirmed vertical heterogeneity. This study provides a scientific basis for pollution assessment and targeted remediation in karst regions.

Hydrothermal carbonization of primary sewage sludge in a prototype reactor: Sensitivity analysis of key parameters for hydrochar and liquid phase upscaling and reuse

Hydrothermal carbonization (HTC) of untreated sewage sludge with high water content was evaluated as an alternative to usual wastewater treatment. This bold approach allowed bioproducts useful as biofuel and nutrient sources to be obtained directly from raw sludge. HTC experiments were performed in a scalable arrangement (technology readiness level >4) composed of a 10-liter high-pressure reactor. The influence of the experimental conditions, including temperature (180-220°C), reaction time (60-120min), and pH (6.5-12.0), was evaluated on nutrients and heavy metal distribution through the liquid phase by a 23 central composite design with a triple central point. The bioproducts were characterized by a set of six analytical techniques, and the results were refined by statistical procedures (Principal Component Analysis and Response Surface Methodology). pH is a variable that impacts bioproducts’ characteristics, making it a guiding factor for application. Basic pH promotes transfer of components from solid to liquid phase, while natural pH favors the heating value of hydrochars. Liquid phase is a carbon- and nitrogen-rich source that can be useful for plant nutrition, mainly due to the increase of ammonia nitrogen from 20.6 to 51.7wt.% under pH 6.5, and an expressive decrease of chemical oxygen demand from 32 to 6-16 mgO2L-1. The hydrochar can be considered a renewable solid fuel, reaching up to 12.43 MJ kg-1 under pH 6.5; the basic pH favored an increase in the O/C ratio and lower HHV values. HTC of primary sewage sludge can replace steps of a conventional treatment station and promotes treated wastewater reuse.

Heavy Metal Content in the Soil of Baiji Refinery and Surrounding Area, Controlling its Distribution Factors

This study was conducted to analyze the presence of heavy metals within the soil inside and around Baiji Oil Refinery and their factors that control the spatial distribution of metals in soils existed as (organic matter (OM), iron-manganese, pH, clay minerals). In this study, geochemical analysis of Baiji Oil Refinery soil and nearby areas was conducted by Atomic Absorption Spectrophotometer (AAS). Twenty two samples were analysed in which the results showed heavy metal as (Cd, Pb, Cr, Ni, Fe, Mn, Zn and Cu) concentrations that most of these metals were found to be higher than their global limits in the soil, except (Fe, Mn and Zn) found to be within those limits. Controlling factors were conducted for six samples, three of highest concentrations and three of lowest one. The results reveal that the mean concentration of OM in the highest concentrations was 1.64% while the lowest concentrations was 1.59%, the mean concentration of Fe-Mn was in the highest concentrations 23380-359 ppm and in the lowest samples was 11304-114 ppm, respectively. The mean concentration for the pH in the highest concentrations was 8.5 and for the samples within the lowest concentrations was 7.9. However, the mean clay for the samples within the highest concentration were 1.04%, whereas the samples within the lowest concentration were 1.48%. The results showed that the controlling factors did not affect the soil heavy metal distribution of Baiji Oil Refinery and their nearby areas. Therefore, such results indicate the role of industrial processes (oil industries) in increasing the concentration of heavy metals in the soils in addition to the elements inherited from the parent rocks.

ASSESSMENT OF THE CONCENTRATION OF HEAVY METALS IN ELECTRONIC WASTE DUMP SITE IN ALABA INTERNATIONAL MARKET, LAGOS STATE

This research investigates the concentration of heavy metals (Pb, Cu, Cd, and Ni) at e-waste dump sites in Lagos, Nigeria. Measuring the concentration and distribution of heavy metals across different soil depths and comparing these concentrations in soil and water samples. For this purpose, 78 soil samples were taken from different sampling points, and analyses were carried out following standard procedures. The soil samples retrieved from the e-waste site consistently exhibit a sandy soil texture, as per the USDA classification system. It was found that Cu generally has the highest concentrations in the soil layers (2022.20-2312.67mgkg-1) while Cd generally has the least (16.32-22.62 mgkg-1). The elements observed for this study are Cu > Pd >Ni > Cd. Overall, this comprehensive study provides valuable insights into the environmental contamination and health risks posed by e-waste dump sites, emphasizing the need for effective management strategies to mitigate these risks and protect human health and the 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.

Distribution of heavy metals in core sediments of Southern Iraq Waterways

The object of the study is heavy metals in the Shatt Al-Arab River. Shatt Al-Arab River is considered one of the most important internal rivers in Iraq due to its multiple economic and social importance. In addition, this river has an important strategic location. Despite its importance mentioned above, the Shatt Al-Arab River suffers from various wastes from many and varied sources, which have directly affected the quality of its water and consequently the quality of the community of living organisms that live in it. Levels and distribution of certain heavy metals, Cd, Cu, Fe, Mn, Ni, and Pb, were determined in core sediments from Southern Iraq Waterways. Six sites were investigated, two in the lower reaches of Al-Hammar Marsh, 1) El-Barka, 2) El-Garmah, and four sites along Shatt Al-Arab River, 3) Al-Ashar, 4) Abu Al-Khaseeb, 5) Abu Floos Port, and 6) Al-Faw. The results indicate that Al-Faw station was distinguished by the fact that the highest values of heavy elements were obtained at this station and all depths, except Cd at Al-Ashar station at a depth of 25 cm (26.1375 μ/gm), Cu at a core depth of 50 cm (4.9635 μ/gm), Ni at a core depth of 25 cm (5.2483 μ/gm), and surface water (2.9021 μ/gm) and Pb in surface water at Abu Floos Port station (3.5001 μ/gm). The lowest concentrations of heavy elements for Cu, Mn, and Ni are in all depths of core sediments. Other elements, Cd, Pb, and Fe, were higher; on the other hand, higher levels of concentrations for all studied heavy metals were recorded at a depth of 100 cm. Iron was the highest in all depths of core sediments.

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).

Evaluation of Environmental Risk from Heavy Metals in Sediments in Port Areas

This research aims to evaluate the concentration and distribution of heavy metals in sediment in the Port area of ​​PT Pelindo Dumai Branch, which is one of the main ports in Riau Province, Indonesia. Heavy metals such as cadmium, copper, lead, mercury and zinc are known to have significant toxic effects on maritime ecosystems and can accumulate in the food chain, potentially endangering aquatic life and human health. The research methodology involves taking sediment samples from various points around the port area, which are then analyzed using spectroscopic techniques to determine heavy metal concentrations. The results showed that the concentration of heavy metals in sediments varied, with zinc having the highest concentration (average 0.019724 mg/L), followed by copper, lead, cadmium, and mercury. This variation indicates the existence of diverse sources of pollution and uneven distribution in the port area. Although cadmium and mercury concentrations are relatively low, they still raise concerns due to their high toxicity and potential accumulation in the food chain. These results emphasize the need for more intensive environmental monitoring and targeted risk management to reduce the negative impacts of heavy metals on maritime ecosystems and human health. This research provides an important scientific basis for mitigation efforts in port areas, which are crucial to ensuring the sustainability of ecosystems and the well-being of communities that depend on marine resources.

Heavy metal contamination of the Nigerian environment from e-waste management: A systematic review of exposure pathway and attendant pathophysiological implications

Nigerian environment has become a depository of worrisome levels of toxicants including heavy metals. Electrical and electronic equipment which leads to the generation of e-waste is one of the considerable sources of environmental contaminants in Nigeria. This systematic review deployed Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method to analyze various empirical and quantitative outputs on heavy metal components of e-waste management sites in Nigeria and investigated the distribution of heavy metals in several environmental matrices such as soil, water, surface dust, plant and blood of e-waste scavengers. Also, the probable environmental multi-transformation that ultimately potentiates the susceptibility of humans to the pathophysiological effects and genetic related disorders of exposure to heavy metal component of e-waste was discussed. The relative abundance of metals in soils due to inappropriate e-waste handling is given as follow: Fe>Cu>Pb>Zn>Mn>Co>Cr>Ni>As>Cd, in underground and surface water: Zn>Fe>Cu>Cr>Mn>Pb>Hg>Ni>Co>Cd>As; in surface dusts: Fe>Zn>Pb>Cu>Mn>Cr>Ni>Cd>Co; in plants: Pb>Cu>Mn>Zn>Ni>Cr>Cd obtained from the Nigerian environment. Moreso, the estimated abundance of heavy metals in the blood of e-waste scavengers is Nigeria is given as Cr>Cd>Hg>Zn>As>Pb>Cu>Mn>Fe. Conclusively, this study does not only show that various matrices of Nigerian environment is contaminated with heavy metal but also that the pattern of abundance differ in the environment and in biological systems with the latter having the abundance of Pb, Cr and Cd and the former having the abundance of Cu, Zn and Fe.

Heavy Metals Distribution in Mangrove Leaves in Various Sudanese Coastal Zones at The Red Sea

<p style="text-align:justify;"><span style="font-family:"Times New Roman",serif;font-size:10.0pt;line-height:107%;">Mangrove ecosystem contamination, especially in the Red Sea region, has caused major concerns on a worldwide scale. The heavy metal accumulation typical of a mangrove species, </span><em><span style="font-family:"Times New Roman",serif;font-size:10.0pt;line-height:107%;">Avicenna marina </span></em><span style="font-family:"Times New Roman",serif;font-size:10.0pt;line-height:107%;">L. (</span><em><span style="font-family:"Times New Roman",serif;font-size:10.0pt;line-height:107%;">A. marina</span></em><span style="font-family:"Times New Roman",serif;font-size:10.0pt;line-height:107%;">) leaves and soluble salts in sediments have not been studied on the Red Sea coast of Sudan.  The present study investigates the two nutrients calcium (Ca) and iron (Fe) and heavy metals such as barium (Ba), titanium (Ti), and strontium (Sr) in the mangrove species </span><em><span style="font-family:"Times New Roman",serif;font-size:10.0pt;line-height:107%;">A. marina</span></em><span style="font-family:"Times New Roman",serif;font-size:10.0pt;line-height:107%;"> in the leaves of six different locations in the Red Sea coastal area, as follows: (Hamasyat (HM) Keligo (KG), and Enkfel (EK) of the Gulf of Dunnabeb, and three sites were selected in the south of the Sudanese coast as follows: (Amarat Island (AM), Ibn Abbas Island (BN), and Ras Kassar (RK). The results demonstrate that the maximum calcium (Ca) and iron (Fe) concentrations in mangrove leaves were 35.9 mg/kg and 4.10 mg/kg recorded at RK and AM, respectively, in the south region of the Red Sea. The heavy metal concentrations (mg/kg) vary between different locations. The higher concentration of heavy metals in mangrove leaves increased as Ba was 1.1 mg/kg in the EG north region. While Ti (0.5 mg/kg) and Sr (2.80 mg/kg) higher concentrations were recorded in AM and EK, respectively, in the south area than in the other experimental sites.   Heavy metals and soluble salts in sediments are continuously monitored in mangrove habitats to ensure they keep within allowed limits. These results could be useful as a database for prospective ecological research, preservation efforts, and long-term sustainable management of the Sudanese mangrove ecosystems throughout the Red Sea coastal.</span></p>

Machine learning-assisted risk evaluation of heavy metals in the Hainan gold mining region, China.

This study employed machine learning (ML) to thoroughly investigate the impact of informal mining activities on the distribution and pollution status of heavy metals in soils near private gold mines in Hainan Province, southern China, a region known for its ecological sensitivity and economic importance. By systematically collecting surface soil samples and samples at depths of 0.5-1m from 175 drilling sites, a comprehensive quantitative analysis was conducted on major heavy metal elements, including lead (Pb), copper (Cu), cadmium (Cd), nickel (Ni), mercury (Hg), chromium (Cr), arsenic (As), and zinc (Zn). Combined with evaluation methods such as the Pollution Load Index (PLI), Normalized Pollution Index (NIPI), and Ecological Risk Index (ERI), the study revealed a high level of soil pollution at informal mining sites. The findings indicated that the average concentrations of Pb, Cd, Hg, As, and Zn in surface soils significantly exceeded the background values for soils in China, with a pronounced positive correlation observed between these heavy metal elements in both surface and deep soil profiles (r > 0.5). Furthermore, leveraging the heavy metal content in surface soils and the constructed environmental indicators, the predictive accuracy for metal content in deep soils was found to range from R2 = 0.27 to 0.68, suggesting that informal mining activities have led to substantial variations in metal content across different soil profiles. Through the application of a random forest model for predictive analysis of the PLI, NIPI, and ERI, high prediction accuracy was achieved (R2 = 0.78, 0.86, and 0.60, respectively). The study demonstrates that informal mining activities not only elevate the risk of soil pollution but also alter the distribution patterns of heavy metals. Also, this study provides a crucial foundation for the scientific assessment of soil quality and potential environmental hazards, while also affirming the efficacy of ML techniques in forecasting soil quality parameters.

Accumulation and Distribution of Heavy Metals in Soils and Medicinal Plants in the Impact Zone of Novocherkassk Power Station

Accumulation and Distribution of Heavy Metals in Soils and Medicinal Plants in the Impact Zone of Novocherkassk Power Station

Patterns and causes of soil heavy metals and carbon stock in green spaces along an urbanization gradient

The surge in urbanization has correspondingly given rise to exacerbated carbon footprint and burgeoning heavy metal (HM) contamination, constituting a critical impediment to the realization of environmentally sustainable urban development pathways. Therefore, it is highly essential to investigate the impacts of urbanization process on soil carbon, heavy metals (HMs), as well as their interrelationships. Employing remote sensing and land use data within a 45 km radius in Chongqing, this study selected 31 sites to assess the variation in urban green spaces (UGSs) carbon sequestration capabilities and associated HM pollution risks across differing urbanization levels. Results showed that urbanization dynamics and land use history play a more decisive role than current anthropogenic management in shaping the distribution of soil carbon and HMs within urban environments. The average soil organic carbon (SOC) distribution of UGSs is 8.47 mg ha−1 (1.50–24.20 mg ha−1), concurrent with moderate HM pollution showing Ni: 32.03–33.50 mg kg−1, Pb: 27.93–29.65 mg kg−1, Cu: 26.97–41.20 mg kg−1, while Cd exhibits the most severe pollution range of 0.23–0.30 mg kg−1. The rate of urbanization significantly influences SOC stocks, exhibiting an increase when the urbanization rate is below 70.79 % and a decrease upon surpassing this threshold. The concentration of HMs is predominantly determined by the historical usage of the land, with a marked escalation correlating with the duration of land utilization history. These relationships were quantitatively modeled through the application of fitted linear functions. The structural equation model demonstrated that UGSs operating without anthropogenic disturbance tend towards degradation, indicating that temporary homeostasis might be sustained via the addition of N and P nutrients and pH modulation. In conclusion, we formulate an Urban Green Spaces Development Hypothesis that aims to shed light on the multifaceted challenges encountered by cities amidst varying degrees of urbanization and, concurrently, to probe potential ameliorative strategies.

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.