Metal Concentrations In Soils Research Articles

PREDICTING URBAN SOIL POLLUTION USING MACHINE LEARNING ALGORITHMS

Different cities of the world are facing heavy metal pollution in soils at different levels. Previous studies have found that heavy metal concentrations in urban soils tend to increase with increasing levels of urbanization, indicating a link between heavy metal content in soils and urban expansion. Thus, understanding this relationship and considering factors related to urbanization to create reliable predictions of heavy metal distribution in soils can contribute to effective management of urban health. This study examines the sources, distribution, and environmental effects of heavy metals. These elements accumulate in soil due to vehicle emissions, tire and brake wear, and abrasion of road surfaces, which carry significant environmental and health risks. The presence of heavy metals in road soil can detrimentally affect plant growth, enter the food chain, and pose a direct threat to human health when contaminated soil is ingested, or dust particles are inhaled. In this study, a random forest (RF) machine learning model was applied to predict the extent of heavy metals in soil along highways. The results showed that the RF model has high accuracy in predicting the spatial distribution of heavy metals in soil.

Heavy Metals and Associated Risks of Wild Edible Mushrooms Consumption: Transfer Factor, Carcinogenic Risk, and Health Risk Index

This research aims to investigate the heavy metals (i.e., Cd, Cr, Cu, Ni, and Pb) in the fruiting bodies of six indigenous wild edible mushrooms including Agaricus bisporus, Agaricus campestris, Armillaria mellea, Boletus edulis, Macrolepiota excoriate, and Macrolepiota procera, correlated with various factors, such as the growth substrate, the sampling site, the species and the morphological part (i.e., cap and stipe), and their possible toxicological implications. Heavy metal concentrations in mushroom (228 samples) and soil (114 samples) were determined by Inductively Coupled Plasma—Mass Spectrometry (ICP-MS). In the first part of the study, the soil contamination (index of geo-accumulation, contamination factor, and pollution loading index) and associated risks (chronic daily dose for three exposure pathways—ingestion, dermal, and inhalation; hazard quotient of non-cancer risks and the carcinogenic risks) were calculated, while the phytoremediation capacity of the mushrooms was determined. At the end of these investigations, it was concluded that M. procera accumulates more Cd and Cr (32.528% and 57.906%, respectively), M. excoriata accumulates Cu (24.802%), B. edulis accumulates Ni (22.694%), and A. mellea accumulates Pb (18.574%), in relation to the underlying soils. There were statistically significant differences between the stipe and cap (i.e., in the cap subsamples of M. procera, the accumulation factor for Cd was five times higher than in the stipe subsamples). The daily intake of toxic metals related to the consumption of these mushrooms with negative consequences on human health, especially for children (1.5 times higher than for adults), was determined as well.

Uncovering the Relationship Between Soil Bacterial Community and Heavy Metals in a Copper Waste Pile

In the present study, the relationship between the microbial community and heavy metal content of soil was analyzed based on 16S rRNA gene high-throughput sequencing, in order to screen the corresponding heavy metal-resistant bacteria in a copper mine waste dump and adjacent shrubbery. Approximately 22 phyla, 57 classes, 128 orders, 173 families, 263 genera, 433 species, and 954 OUTs obtained from soil sample species annotation indicated the Spearman relevance analysis at the phylum level. Specifically, Gemmatimonadota is positively correlated with arsenic (As); Patescibacteria is positively correlated with arsenic (As), copper (Cu), and cadmium (Cd); Proteobacteria is positively correlated with chromium (Cr); and Acidobacteriota is positively correlated with cadmium (Cd), respectively. Meanwhile, at the genus level, Acidibacter is positively correlated with arsenic (As); norank_f__LWQ8, norank_f__Gemmataceae, and Bryobacter are positively correlated with cadmium (Cd); Acidiphilium and Conexiactor are positively correlated with Zinc (Zn); norank_f__norank_o__IMCC26256 is positively correlated with nickel (Ni); norank_f__norank_o__norank_c__AD3 is positively correlated with manganese (Mn), and nickel (Ni); and Alicyclobacillus and unclassified_f__Acidiferobactereae are positively correlated with chromium (Cr). These bacterial flora are significantly and positively related to the resistance of heavy metals, which provides a promising reference for the development of in situ remediation of heavy metal pollution in mines.

Ecological and health risk assessments of heavy metal contamination in soils surrounding a coal power plant

Coal combustion at power plants is a significant source of environmental pollution, with the deposition of heavy metals in soils leading to extensive ecosystem contamination and exacerbating the harmful impacts of human activities. This study presents a field investigation of heavy metal concentrations in soils around a coal-fired power plant, with monitoring sites located 1.7 to 15.2km from the plant. Data collection occurred in 2015, 2020, and 2024 across 11 monitoring sites. X-ray fluorescence analysis was used to assess heavy metal concentrations in soil. A life cycle assessment (LCA) with ReCiPe method was conducted to evaluate the impacts on both ecological and human health. The results revealed that the concentrations of Pb, Zn, Cu, Ni, Mn, Cd, and Cr exceeded threshold levels set by both the World Soil Average and the Regional Geochemical Background. The correlation analysis shows that organic matter and clay content play vital roles in reducing the mobility of heavy metals in soil through adsorption. Notably, strong correlations between Pb and Cd suggest a common origin from coal seam minerals. The LCA shows that freshwater ecotoxicity, marine ecotoxicity, terrestrial ecotoxicity, and human toxicity increased by over 30% compared to the World Soil Average pollutant levels. Nickel was identified as the primary contributor to marine and freshwater ecotoxicity, while manganese had the most significant impact on human toxicity. This study provides a better understanding of the long-term consequences of heavy metal accumulation in soils near coal-fired power plant. The data on anthropogenic impacts are crucial for developing effective strategies to mitigate environmental risks associated with pollution.

Irrigation with water from Beni Haroun reservoir (Algeria) changed soil physicochemical properties and the availability of metallic trace elements in croplands

The Beni Haroun Dam (BHD), situated in the province of Mila, Algeria, stands as the largest dam within the country, facilitating the irrigation of approximately 40,000 ha of agricultural lands characterized by sub-humid and semi-arid climates. Over time, the water within this reservoir has experienced an escalation in contamination, primarily attributed to its role as a major recipient of diverse municipal and industrial effluents, both treated and untreated. Consequently, mounting apprehensions regarding the potential migration of pollutants to irrigated soils have surfaced. The primary aim of this investigation was to assess the levels of contamination by mineral elements and heavy metals present in agricultural soils irrigated by waters originating from BHD. A total of 48 soil samples were systematically collected from 12 distinct sites, comprising 10 irrigated areas and 2 control sites, each spanning depths of 0, 10, 20, and 30 cm. Then subjected to chemical characterization, including the total quantification of minerals (Ca2+, Mg2+, Na+, K+), and heavy metals (Cd2+, Cu2+, Zn2+, Pb2+, Cr3+ and Fe3+). as well as the determination of cation exchange capacity (CEC), exchangeable sodium percentage (ESP), sodium adsorption ratio (SAR) as well as soil texture. This study indicated that irrigating with dam waters increased the soil exchangeable cations in comparison to the control one: Ca2+ (21.99 ± 3.65 meq 100 g−1), Mg2+ (10.53 ± 1.94 meq 100 g−1), Na+ (10.08 ± 1.78 meq 100 g−1), K+ (2.81 ± 0.8 meq 100 g−1), and enhanced soil characteristics: CEC (25.2 ± 5.55), ESP (41.69 ± 11.21) and SAR (2.51 ± 0.43). In terms of percentage of enrichment, the mineral elements are classified as follows: Na+ > Ca2+ > K+ > Mg2+. The metal contents in irrigated soils were also higher but remained less than the recommended international limits. They are classified as follows: Fe3+ > Zn2+ > Pb2+ > Cr3+ > Cu2+ > Cd2+. Consequently, the soils under investigation are deemed susceptible to salinization, sodification, and contamination with prolonged irrigation. Such conditions pose potential risks to human health should vegetable crops absorb these metals. Therefore, it is recommended to implement adequate drainage measures, emphasizing surface drainage, and to conduct regular monitoring for the accumulation of salt and sodium.

USOS DEL CANNABIS EN LA BIORREMEDIACIÓN DE SUELOS CONTAMINADOS CON METALES PESADOS

<p><strong>Background. </strong>Bioremediation of soils contaminated with heavy metals is a vital area of research given the growing concern about environmental contamination. However, understanding of the ability of <em>Cannabis sativa</em> for this task has been limited. <strong>Objective</strong>. undertake a thorough analysis of the existing scientific literature to assess the role of Cannabis systematically and critically in the bioremediation of soils contaminated with heavy metals, emphasizing its effectiveness and potential applicability in restoring ecosystems affected by pollution. <strong>Methodology. </strong>A systematic review of the scientific literature was carried out, consulting databases such as ScienceDirect, Scopus, and SpringerLink. Relevant studies investigating the ability of <em>Cannabis sativa</em> to accumulate heavy metals and the factors affecting this process were selected and analyzed. <strong>Results. </strong>It was found that <em>Cannabis sativa</em>, especially the sativa variety and subvarieties such as Henola and Bialobrzeskie, exhibit a notable hyperaccumulation capacity of heavy metals, especially in roots and stems. Factors such as soil type, metal concentration, and weather conditions affect its effectiveness. <strong>Implications. </strong>These findings have important implications for agriculture and environmental management, offering a sustainable and less invasive alternative for the remediation of contaminated soils. However, the need to balance bioremediation with agricultural production and to properly manage the resulting waste is highlighted. <strong>Conclusions. </strong><em>Cannabis sativa</em> shows promising potential in the bioremediation of soils contaminated with heavy metals. Its hyperaccumulating capacity and the factors that influence its effectiveness offer significant opportunities for environmental restoration, remediation and sustainable resource management. The importance of future research is emphasized to maximize its effectiveness and minimize its impact on agricultural production.</p>

Assessment of Selected Heavy Metals Content in Soil and Rice Grown on Farmland around Edozhigi River in Niger State, Nigeria

The global climate continues to deteriorate, resulting in excessive carbon emissions, heavy metal pollution possesses serious threats to human life and modern civilization. The concentration of Zn, Fe, Cr, Pb, Cu and Cd in soil and rice grown in two locations (A and B) AROUND Edozhigi river were determined using standard methods og analysis. Results obtained for location A, indicate high concentration of Zn 9.75±0.55, 9.61±0.54, 1.03±0.58 and 1.69±0.66 mg/kg in soil, root, stem and seed of rice plant respectively. The order of Zn concentration of Zn metal in location B is 2.07±0.55 (soil) > 1.70±0.54 (root) > 1.67±0.58 (stem) > 1.27±0.66 (seed). Concentration of Fe (mg/kg) in soil (25.50±0.00) and root (26.32±0.00) from location B were significantly higher than those of stem (8.46±0.00 mg/kg) and seed (6.98±0.00 mg/kg) respectively. Concentration of Cu (mg/kg) in soil, root, stem and seed from location A and B were between 0.00±0.00 - 0.03±0.40 mg/kg respectively. The low concentration of these metals in soil around Edozhigi river makes the soil fit for growing crops.

Revealing the synergistic spatial effects in soil heavy metal pollution with explainable machine learning models

The identification of factors that affect changes in the heavy metal content of soil is the basis for reducing or preventing soil heavy metal pollution. In this research, 16 environmental factors were selected, and the influences of soil heavy metal spatial distribution factors and the synergy amongst space factors were evaluated using a geographic detector (GD) and the extreme gradient boosting (XGBoost)-Shapley additive explanations (SHAP) model. Three heavy metal elements, namely, Cd, Cu and Pb, in the study region were examined. The following results were obtained. (1) XGBoost demonstrated high accuracy in predicting the spatial distributions of soil heavy metals, with each heavy metal having an R2 value of over 0.6. (2) Geological type map (Geomap) and enterprise density considerably affected the concentrations of Cd, Cu and Pb in soil in the GD and XGBoost-SHAP models. In addition, cross-detection revealed strong explanatory power when natural and human factors were combined. (3) Under the same geological background, the different trends of gross domestic product effects on heavy metals indicated that pollution control measures were effective in economically developed areas, and the economy and the environment could be balanced. Meanwhile, the interaction between the normalised difference vegetation index and enterprise density showed that vegetation could alleviate heavy metal pollution in the region. This study supports strategic decision-making, serving as a reference for the global management of soil heavy metal contamination, sustainable ecological development and assurance of people’s health and well-being.

Heavy metal concentrations in soil and ecological risk assessment in the vicinity of Tianzhu Industrial Park, Qinghai-Tibet Plateau.

Industrial parks in China are centers of intensive chemical manufacturing and other industrial activities, often concentrated in relatively small areas. This concentration increases the risk of soil pollution both within the parks and in surrounding areas. The soils of the Tibetan Plateau, known for their high sensitivity to environmental changes, are particularly vulnerable to human activity. In this study, we examined the concentrations (mg/kg) of 10 metal elements (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Se, and Zn) in soils at depths of 0-10 cm, 10-20 cm, and 20-30 cm from the surface at three distances (500 m, 1000 m, and 1500 m from the park boundary) on the east, south, west, and north sides of the Tianzhu Industrial Park on the Qinghai-Tibet Plateau. The concentrations of As, Cr, Mn, and Pb were close to the standard reference values for the Qinghai-Tibet Plateau, while Cu, Ni, Se, and Zn levels were found to be 1.6-2.2 times higher than the reference values. Cd and Hg concentrations were particularly concerning, at 8.0 and 6.5 times higher than reference values, respectively. The potential ecological risk indexes indicated persistent risk levels for Cd and Se across various directions and distances. Variations in soil depth and direction were observed for the concentrations of As, Cd, Hg, Pb, Se, and Zn, underscoring the need for regular or long-term monitoring. Cd, in particular, presents a significant hazard due to its high concentration and its propensity for uptake by plants in the study area.

Advanced Low-Cost Technology for Assessing Metal Accumulation in the Body of a Metropolitan Resident Based on a Neural Network Model.

This study is devoted to creating a neural network technology for assessing metal accumulation in the body of a metropolis resident with short-term and long-term intake from anthropogenic sources. Direct assessment of metal retention in the human body is virtually impossible due to the many internal mechanisms that ensure the kinetics of metals and the wide variety of organs, tissues, cellular structures, and secretions that ensure their functional redistribution, transport, and cumulation. We have developed an intelligent multi-neural network model capable of calculating the content of metals in the human body based on data on their environmental content. The model is two interconnected neural networks trained on actual measurement data. Since metals enter the body from the environment, the predictors of the model are metal content in drinking water and soil. In this case, water characterizes the short-term impact on the organism, and drinking water, combined with metal contents in soil, is a depository medium that accumulates metals from anthropogenic sources-the long-term impact. In addition, human physiological characteristics are taken into account in the calculations. Each period of exposure is taken into account by its neural network. Two variants of the model are proposed: open loop, where the calculation is performed by each neural network separately, and closed loop, where neural networks work together. The model built in this way was trained and tested on the data of real laboratory studies of 242 people living in different districts of Kazan. As a result, the accuracy of the neural network block for calculating long-term impact was 90% and higher, and the accuracy of the block for calculating short-term impact was 92% and higher. The closed double-loop model showed an accuracy of at least 96%. Conclusions: Our proposed method of assessing and quantifying metal accumulation in the body has high accuracy and reliability. It does not require expensive laboratory tests and allows quantifying the body's metal accumulation content based on readily available information. The calculation results can be used as a tool for clinical diagnostics and operational and planned management to reduce the levels of polymetallic contamination in urban areas.

Assessment of the Environmental Impact of Fly Ash on the Concentration of Heavy Metals around Cement Company of Northern Nigeria

This study assessed the impact of Coal Fly Ash on the concentration of Heavy metals in water, soil, and vegetables (moringa and spinach) in the environment of Cement Company of Northern Nigeria. The samples collected were digested, and analysed for heavy metals concentration using Atomic Absorption Spectroscopy. The concentrations of heavy metals obtained in water samples were higher than the WHO/SON permissible limit, except for Cu and Zn which are below the standard limits. The average concentration of Pb, Mn, Cd, and Ni in all plant samples was higher than the permissible limit, except Cd in spinach sample. The roots of the spinach accumulate higher concentrations of heavy metals than the leaves and stem, while leaves of moringa accumulate higher than the roots and stem. The concentration of heavy metals in soil sample was below the WHO limit. Comparatively the plants samples have the highest concentrations of heavy metals than the water and soil samples. The coal fly ash from the Cement Company must have contributed to the significant concentration of Heavy metals in the environment making the water and plants from the study area unfit for human and animal consumption.

Pollution assessment, ecological risk and source identification of heavy metals in paddy soils and rice grains from Salem, South India

The present study investigated concentrations of metals (Cu, Zn, Fe, Mn, Ni, Pb, and Cd) in paddy soils and rice tissues of nine different widely used indica rice varieties in Salem district, India. The order of DTPA soil available metal contents (mg kg-1) were Fe (33.50) > Mn (8.86) > Cu (2.58) > Pb (1.99) > Zn (1.67) > Ni (1.48) > Cd (0.13). Pollution load index (PLI) levels for Cd (1.28) were ‘moderately polluted’, contamination factor (CF) and enrichment factor (EF) values for all metals showed ‘medium contamination’ and ‘less enrichment’ except for Cu and Pb. Ecological risk factor (ERF) and potential ecological risk index (PRI) values of Cd (31.94 and 287.50) showed ‘moderate risk’ and ‘strong ecological risk’. Bio-accumulation factor (BAF) levels of Cd (48.12) and Pb (27.89) are classified as ‘high concentrators’. Translocation factor (TF) values of Pb (2.9), Cd (2.5) and Ni (1.7) were greater than one for roots to plants. Children had higher target hazard quotient (THQ) and hazard index (HI) values for Cd, Pb and Ni than adults. Principal component analysis (PCA) revealed with all parameters that they fell within the two primary factors 1 and 2, with an eigenvalue of 4.69 (33.52% variation) and 3.73 (26.66% variation), respectively. However, cluster analysis (CA) revealed three distinct groups between the metals studied. Although the soil metal contents were within the permissible levels, the grain Cd (6.07 mg kg-1), Pb (56.20 mg kg-1), and Fe (116.11 mg kg-1) contents in all the rice varieties exceeded the prescribed limits of 0.4, 0.2, and 15 mg kg-1, respectively. Our findings will provide important data for metal enrichment in soils to implement more effective measures to reduce the contamination of metals in paddy fields.

Hyperspectral Inversion of Soil Cu Content in Agricultural Land Based on Continuous Wavelet Transform and Stacking Ensemble Learning

Heavy metal pollution in agricultural land poses significant threats to both the ecological environment and human health. Therefore, the rapid and accurate prediction of heavy metal content in agricultural soil is crucial for environmental protection and soil remediation. Acknowledging the limitations of traditional single linear or nonlinear machine learning models in terms of prediction accuracy, this study developed an ensemble learning model that integrates multiple linear or nonlinear learning models with a random forest (RF) model to improve both the prediction accuracy and reliability. In this study, we selected a typical copper (Cu) polluted area in the Pearl River Delta of Guangdong Province as the research site and collected Cu content data and indoor soil reflectance spectral data from 269 surface soil samples. First, the soil spectral data were preprocessed using Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC), and continuous wavelet transform (CWT) to reduce noise interference. Next, principal components analysis (PCA) was employed to reduce the dimensionality of the preprocessed spectral data, eliminating redundant features and lowering the computational complexity. Finally, based on the dimensionality-reduced data and Cu content, we established a stacked ensemble learning model, where the base models included SVR, PLSR, BPNN, and XGBoost, with RF serving as the meta-model to estimate the soil heavy metal content. To evaluate the performance of the stacking model, we compared its prediction accuracy with that of individual models. The results indicate that, compared to the traditional machine learning models, the prediction accuracy of the stacking model was superior (R2 = 0.77; RMSE = 7.65 mg/kg; RPD = 2.29). This suggests that the integrated algorithm demonstrates a greater robustness and generalization capability. This study presents a method to improve soil heavy metal content estimation using hyperspectral technology, ensuring a robust model that supports policymakers in making informed decisions about land use, agriculture, and environmental protection.

Contamination and health risk assessment of potentially toxic elements in rice (Oryza sativa) and soil from Ashanti Region.

Anthropogenic activities release potentially toxic elements into the environment, which contaminate the food chain. The main objective of this research was to analyze the concentrations of As, Cd, Cr, Hg, and Pb in rice grains and soils, establish their correlation and transfer factors between soil and rice grains as well as evaluate their human health risk from consumption of rice cultivated in the Asante Akim area. The levels of As, Cd, Cr, Hg, and Pb in soil and rice samples were assayed using an Agilent 7700 Series inductively coupled plasma-mass spectrophotometer. The mean heavy metal content in soil was 7.5, 0.52, 0.47, 1.30, and 8.69mg/kg for As, Cd, Cr, Hg, and Pb, respectively. Mean levels of the potentially toxic elements in rice were 0.082, 0.27, 0.48, 0.028, and 0.14mg/kg for As, Cd, Cr, Hg, and Pb, respectively. Soil pollution indices showed that the soils were unpolluted with the potentially toxic elements studied. The concentrations of the potentially toxic elements in rice were below the maximum allowable concentration (MAC) recommended by the Codex Alimentary Commission except Cd which was marginally higher than the MAC. Dietary exposure to the elements to consumers was assessed by comparing the estimated daily intake (EDI) to the provisional tolerable daily intake (PTDI). The estimated daily intake values for As, Cd, Cr, Hg, and Pb were 1.45 × 10-4, 4.8 × 10-4, 8.5 × 10-4, 4.95 × 10-5, and 2.4 × 10-4, respectively. The HQ for all the potentially toxic elements was less than the permissible value of 1, suggesting that the consumption of rice from the study area constitutes no potential non-carcinogenic health risk to the population. This study is unique because the risk is evaluated from rice that is directly consumed, and this gives a clearer picture of the risk to humans. Regular monitoring studies should be conducted to ascertain the levels of heavy metals in rice cultivated in the area since heavy metals can accumulate and the concentrations could increase to toxic levels with time.

Metals in Wildfire Suppressants.

Frequent and severe wildfires have led to increased application of fire suppression products (long-term fire retardants, water enhancers, and Class A foams) in the American West. While fire suppressing products used on wildfires must be approved by the U.S. Forest Service, portions of their formulations are trade secrets. Increased metals content in soils and surface waters at the wildland-urban interface has been observed after wildfires but has primarily been attributed to ash deposition or anthropogenic impact from nearby urban areas. In this study, metal concentrations in several fire suppression products (some approved by the U.S. Forest Service, and some marketed for consumer use) were quantified to evaluate whether these products could contribute to increased metal concentrations observed in the environment postfire. Long-term fire retardants contained concentrations of toxic metals (V, Cr, Mn, Cu, As, Cd, Sb, Ba, Tl, and Pb) 4-2,880 times greater than drinking water regulatory limits, and potentially greater than some aquatic toxicity thresholds when released into the environment. Water enhancers and Class A foams contained some metals, but at lower concentrations than fire retardants. Based on these concentrations and retardant application records, we estimate fire retardant application in the U.S. contributed approximately 380,000 kg of toxic metals to the environment between 2009 and 2021.

Assessment of heavy metals concentration and health hazard indices in soil and plants from Kokona Local Government Area of Nasarawa State, Nigeria

Human dependence, on the plants grown on soil and the threat posed by contaminated soil to the ecosystem and general public has become a source of concern. The health risks caused by heavy metals concentration to the study area were evaluated. Fifteen (15) plant samples and soil samples each from the study area were collected and analyzed using an Energy Dispersive X-ray Fluorescence Spectroscopy (EDXRF) for Lead (Pb), Cadmium (Cd), Chromium (Cr), Nickel (Ni), Copper (Cu), and Zinc (Zn). The Measured concentrations of these heavy metals were in the order of Cd>Ni>Cu>Zn>Pb>Cr for the plant samples and Cr>Cd>Ni>Cu>Pb for soil samples with Cd and Cr as the highest contributors to cancer risk in term of the carcinogenic risk estimate. The Health Hazard Index value for all pathways were found to be 7.34 x 10-1 making non-carcinogenic risk component less significant to the adult population and in case of children, the Hazard Index value was 1.21, a value greater than unity (1), which present a severe non-carcinogenic risk component to children living in the study area. The carcinogenic risk was found to be 8.68 x 10-6 implying that 1 person in every 5882 adults may be affected and 1 in 2725 children may be affected (9.6 x 10-5) These carcinogenic risk values were both higher than acceptable values.

Exopolysaccharide-Producing Bacteria Regulate Soil Aggregates and Bacterial Communities to Inhibit the Uptake of Cadmium and Lead by Lettuce.

The accumulation of heavy metals in the soil not only causes serious damage to the soil ecosystem, but also threatens human health through the food chain. Exopolysaccharides have the functions of adsorbing and chelating heavy metals and reducing their bioavailability in the soil. In our study, exopolysaccharide-producing bacteria with a high efficiency in adsorbing cadmium (Cd) and lead (Pb) were screened from heavy metal-contaminated farmland. Through pot experiments, the influence of functional strains on the size distribution, heavy metal content, and bacterial community structure of soil aggregates in lettuce was studied using high-throughput sequencing technology. The results show that 11 strains secreting exopolysaccharides were initially screened from heavy metal-contaminated soil. Among them, strain Z23 had a removal rate of 88.6% for Cd and 93.2% for Pb. The rate at which Cd was removed by strain Z39 was 92.3%, and the rate at which Pb was removed was 94.4%. Both strains belong to Bacillus sp. Strains Z23 and Z39 induced the formation of Fe2Pb(PO4)2, Cd2(PO4)2, and Pb2O3 in the solution. The pot experiments showed that strains Z23 and Z39 increased (19.1~23.9%) the dry weight and antioxidant enzyme activity of lettuce roots and leaves, while reducing (40.1~61.7%) the content of Cd and Pb. Strains Z23 and Z39 increased the proportion of microaggregates (<0.25 mm) and the content of exopolysaccharides in rhizosphere soil and reduced (38.4-59.7%) the contents of available Cd and Pb in microaggregates, thus inhibiting the absorption of heavy metals by lettuce. In addition, the exopolysaccharide content and the bacterial community associated with heavy metal resistance and nitrogen (N) cycling (Patescibacteria, Saccharimonadales, Microvirga, and Pseudomonas) in microaggregates were key factors affecting the available heavy metal content in soil. These results show that the exopolysaccharide-producing bacteria Z23 and Z39 reduced the absorption of Cd and Pb by lettuce tissues, thus providing strain resources for the safe utilization of soils that exceed heavy metal standards for farmland and for reducing the heavy metal content in vegetables.

Assessment of the Interaction between Pesticide Applications and Heavy Metals Content in Soils and Plants

Assessment of the Interaction between Pesticide Applications and Heavy Metals Content in Soils and Plants

Investigating the Spatial Patterns of Heavy Metals in Topsoil and Asthma in the Western Salt Lake Valley, Utah

Mining activities, particularly in large excavations like the Bingham Canyon Copper Mine in Utah, have been increasingly linked to respiratory conditions due to heavy-metal-enriched waste and dust. Operating continuously since 1906, the Bingham Canyon Copper Mine contributes 4.4% of the Salt Lake Valley PM2.5 pollution. However, the extent of its contributions to larger-sized particulate matter (PM10) dust, soil and water contamination, and human health impacts is largely unknown. Aerosol optical depth data from Sentinel-2 imagery revealed discernible dust clouds downwind of the mine and smelter on non-prevailing-wind days, suggesting potential heavy metal dispersion from this fugitive dust and subsequent deposition to nearby surface soils. Our analysis of topsoils from across the western Salt Lake Valley found mean arsenic, copper, lead, and zinc concentrations to be well above global background concentrations. Also, the minimum values for arsenic and maximum values for lead were well above the US EPA regional screening levels for residential soils. Thus, arsenic is the metal of greatest concern for impacts on human health. Elevated concentrations of all metals were most notable near the mine, smelter, and tailings pond. Our study linked these elevated heavy metal levels to regional asthma outcomes through cluster analysis and distance-related comparison tests. Significant clusters of high asthma rates were observed in regions with elevated topsoil heavy metal concentrations, impacting both low- and high-income neighborhoods. The findings of this preliminary study suggest that the mine, smelter, and recent construction activities, especially on lands reclaimed from former tailings ponds, could be contributing to atmospheric dust containing high levels of heavy metals and exacerbating asthma outcomes for residents. However, the methods used in the study with aggregated health outcome data cannot determine causal links between the heavy metal contents of soil and health outcomes; they can only point to potential links and a need for further investigation. Such further investigation should involve individual-level data and control for potential confounding factors, such as socioeconomic status, access to healthcare, and lifestyle factors, to isolate the effect of metal exposures on asthma outcomes. This study focused on atmospheric deposition as a source of heavy metal enrichment of topsoil. However, future research is also essential to assess levels of heavy metals in subsoil parent materials and local surface and groundwaters to be able to assess the links between the sources or methods of soil contamination and health outcomes.

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.