Soil Heavy Metal Concentrations Research Articles

Organic Farming Is an Important Way to Achieve Low Accumulation of Heavy Metals in Soil

In this study, the accumulation characteristics of As, Hg, Cd, Cr, and Pb in 63 soil samples from 28 organic farms in Beijing, China, were analyzed to investigate the risk of heavy metal pollution in organic agriculture, and the key related factors were evaluated. The results revealed that the As, Hg, Cd, Cr, and Pb concentrations in the soil samples were below the risk screening values and substantially lower than those in the soil under conventional agriculture. However, the coefficients of variation for Hg and Cd were 112.45% and 38.34%, respectively, indicating a notable anthropogenic impact. Notably, 35.92% of the sampling sites had medium to high potential ecological risk values for Cd, and the Cd concentration increased considerably as the number of planting years increased. Different crop types impacted the soil heavy metal concentrations. The concentrations of Cd and As in the soil of Brassica crops were 0.265 and 12.915 mg/kg, respectively, which were substantially higher than those in the soil of other crop types. The Random Forest model indicated that soil nutrients had the most significant impact on soil heavy metal accumulation, particularly phosphorus. In conclusion, compared with conventional agriculture, organic agricultural soils have lower heavy metal concentrations and exhibit lower ecological risks, with no significant heavy metal pollution detected. However, there is a risk of Cd accumulation, and preventive measures should be implemented, especially for soils under prolonged cultivation and with potential sources of heavy Cd inputs.

P1273 High Crohn's Disease incidence associated with multiple heavy metal contamination in agricultural soils: findings from the CROPS project in Northern France

Abstract Background Crohn's Disease (CD) is a chronic intestinal disease with an unclear etiology and suspected environmental risk factors. The CROPS (CROhn’s disease and Pollution of Soils) project was initiated in Northern France to investigate links between the high incidence of CD and environmental pollutants. In this context, we combined data of a population-based registry of Inflammatory Bowel Disease (IBD) with environmental sampling data to identify the environmental profile of multi-contamination associated with high incidence. Methods This ecological study was conducted at the municipal level within the coverage area of the registry in Northern France. All patients diagnosed between 2000 and 2017 were included (n=9,019). Spatial clusters of significantly high (n=4) and low (n=4) CD incidence, adjusted for age and sex, were identified based on patients' residence addresses at diagnosis. Within these clusters, environmental sampling was performed across 240 sites using a hexagonal grid to ensure 30 samples per cluster. A total of 490 parameters were analyzed: 306 in soil (including emerging pollutants such as hormones and antibiotics, polycyclic aromatic hydrocarbons [PAHs], heavy metals, and pesticides), 152 in tap water (examining heavy metals and PAHs), and 30 in lichens (assessing heavy metals as indicators of air quality). Multi-contamination indices, such as the Mean Impregnation Ratio (MIR) and Pollution Index, were applied. Data analysis included Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Analysis of Variance (ANOVA) tests. Results High-incidence clusters, predominantly agricultural, had elevated heavy metal concentrations in soil (notably chromium, arsenic, zinc, and nickel), sulfadiazine in soils, high nitrate levels in water, and heavy metals in lichens. Low-incidence clusters, with greater urbanization, showed elevated PAHs in soil, heavy metals in water, and BPA in soils. Pollution profiles aligned with different land covers, with significant findings for agricultural lands (p<0.0001). A positive correlation between the relative risk and soil’s MIR for heavy metals in agricultural zones was noted (r=0.557, p=0.005). Conclusion These findings suggest a possible link between high CD incidence and heavy metal exposure in agricultural lands. Profiles of pollution were created for each cluster. Future studies should expand on pesticide and agricultural pollutant analysis to strengthen this hypothesis, and consider individual-level epidemiological approaches to further elucidate these potential environmental risk factors.

A data-driven framework to identify influencing factors for soil heavy metal contaminations using random forest and bivariate local Moran's I: A case study.

The efficacy of traceability analysis is often limited by a lack of information on influencing factors for heavy metal (HM) contaminations in soil, such as spatial correlations between HM concentrations and influencing factors. To overcome this limitation, a novel data-driven framework was established to identify influencing factors for soil HM concentrations in an industrialised study area, in Guangdong Province, China, mainly using random forest (RF) and bivariate local Moran's I (BLMI) on the basis of the 577 soil samples and the 18 environmental covariates. The quantitative contributions of the 18 influencing factors for the Cd, As, Pb, and Cr concentrations were determined by the optimised RF. The main influencing factors of Cd were petrol stations (10.97%) and railways (9.99%), the main ones of As were groundwater depth (8.45%) and elevation (8.24%), the main ones of Pb were soil pH (8.82%) and hazardous waste disposal sites (8.02%), and the main ones of Cr were mine tailings (13.65%) and rainfall (11.88%). The eight spatial clustering maps between the four HM concentrations and the two key influencing factors were generated by BLMI. The middle part of the study area has shown the higher concentrations of Cd, As, Pb, and Cr, the more complex human activities and the more high-high clusters. Priority attention should be paid to the middle part when taking the specific prevention and control measures for their contaminations. This data-driven framework provided rich information on influencing factors, including HM concentrations, HM contaminations, quantitative contributions, and qualitative spatial clusters.

The impact of vehicular pollution on soil health in the forest ecosystem of Sonamarg Kashmir Himalayas

This study was taken to evaluate the influence of vehicular pollution on the accumulation of heavy metals and its impact on soil enzymatic activity along the roadside of the Sonamarg forest ecosystem. Soil samples were obtained from three different sites viz., Sonamarg, Baltal, and Thajwas, each comprises of disturbed area (10 mts.) close to roadside and undisturbed area (150 mts.) away from roadside in two seasons (spring and autumn). Soil heavy metal concentration significantly decreased with distance from the roadside. The decrease in the activities of dehydrogenase and urease and soil organic carbon were found in the disturbed area as compared to undisturbed areas. The concentration of zinc (Zn2+) was found to be maximum, followed by nickel (Ni2+), lead (Pb2+), copper (Cu2+), and cadmium (Cd2+) at all the sampling sites. Significant positive correlations were shown by pH with Pb (r = 0.782**), Zn (r = 0.805**), Cu (r = 0.773**), Cd (r = 0.672**) and negative correlations with dehydrogenase activity (r = −0.795**), urease activity (r = −0.825**) and organic carbon (r = −0.764**). Similarly Pb, showed negative correlation with dehydrogenase activity (r = −0.671**,), urease activity (r = −0.673**) and organic carbon (r = −0.860**). A negative correlation was found between Zn and organic carbon (r = −0.821**) as well as between Cu, urease activity (r = −0.632**) and organic carbon (r = −0.757**). The results indicate that vehicular pollution affects the soil quality due to heavy metal contamination in the fragile Sonamarg forest ecosystem.

Assessment of Water and Soil Contamination and Land Cover Changes in the Spring Creek Bayou Watershed in Houston, Texas

Stormwater runoff and nutrient pollution are significant sources of water contamination that continue to grow in rural and suburban watersheds. The goal of this research is to analyze and evaluate the impact of urbanization and industrialization on suburban watersheds in southeast Texas. The objectives are to: (1) determine nutrient and heavy metal concentrations in soil and water samples along Spring Creek Bayou (SC), (2) analyze land cover changes over the last 30 years and (3) assess and evaluate socio-economic data within the watershed. The soil and water samples were collected from upstream, midstream and downstream locations in triplicate during the spring and fall seasons along the bayou. The samples were analyzed to determine chemical concentrations and Landsat 5, and eight imageries were used to derive thematic land cover maps. The soil and water chemical concentrations were interpolated to spatial maps for distribution analysis. The chemical analysis of water samples collected from SC Bayou revealed that N and P concentrations were at elevated levels that can pose a threat to water quality and aquatic organisms. Heavy metal concentrations of Zn were at elevated levels in water samples from the SC Bayou watershed. Land cover change patterns showed that high-vegetation surfaces decreased while low-vegetation surfaces increased slightly over the past three decades. The watershed experienced an increase in total population from 129,629 residents in 1990 to 389,977 residents in 2020. This research is important in improving our understanding on the impact of natural and human activities on suburban watersheds in the Greater Houston metropolitan region.

Applications of geographically weighted machine learning models for predicting soil heavy metal concentrations across mining sites

The accurate prediction of soil heavy metal contamination is crucial for the effective environmental management of abandoned mining areas. However, conventional machine learning models (CMLMs) often fail to account for the spatial heterogeneity of soil contamination, which limits their predictive accuracy. This study evaluated the performance of geographically weighted machine learning models (GWMLMs) in predicting soil Cd and Pb concentrations in abandoned mines in the Republic of Korea. We compared two GWMLMs (Geographically Weighted Random Forest and Geographically Weighted Extreme Gradient Boosting) with four CMLMs (Random Forest, Gradient Boosting, Light Gradient Boosting, and extreme Gradient Boosting). The data used in this study included soil samples from six abandoned mining sites with various geographical and soil input variables. The results showed that the GWMLMs consistently outperformed the CMLMs in predicting heavy metal contamination. For Cd predictions, GWMLMs exhibited on average 0.02 lower root mean square error and mean absolute error values, with a 0.26 increase in R2 values compared to CMLMs. Similarly, for Pb predictions, the GWMLMs showed 0.18 and 0.13 lower root mean square error and mean absolute error values, respectively, and a 0.17 increase in R2 relative to the CMLMs. The findings demonstrate the usefulness of GWMLMs for predicting the spatial distribution of soil heavy metals. SHapley Additive exPlanations analysis exhibited elevation and distance from abandoned mining sites as the most influential factors in predicting both Cd and Pb concentrations. This study highlights the value of GWMLMs that incorporate spatial heterogeneity into CMLMs for enhancing prediction accuracy and providing crucial insights for environmental management in mining-impacted regions.

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.

ENVIRONMENTAL RISK ASSESSMENT OF TRANSPORTATION INFRASTRUCTURE DEVELOPMENT USING GIS IN LAGOS STATE

Transportation infrastructure development poses significant environmental risks, including habitat disruption, air and water pollution, and climate change. This study develops a GIS-based framework for ecological risk assessment of transportation infrastructure development. This research identifies high-risk areas and prioritizes mitigation measures by integrating spatial analysis, multi-criteria decision analysis, and expert consultations. The GIS-based approach evaluates environmental sensitivity, proximity to ecosystems, and potential hazards. Results provide valuable insights for sustainable transportation planning, policy-making, and ecological management. This study explored its application in solving Environmental Risk Assessment of Transportation Infrastructure Development GIS application in environmental Risk Assessment monitoring has mainly been divided into three aspects: water, soil, and atmosphere. Based on ArcGIS 10.1 software and ArcGIS 9.3.1 version, GIS has been applied in Transportation system monitoring and soil heavy metal concentration monitoring, respectively. A major function of planners is to promote the best use of a community's land and resources for different construction projects; especially critical are infrastructure projects on which economic development relies. Transportation projects typically involve both environmental and economic issues facing a community as it grows and changes. Considerations of sustainability, as well as the widespread use of collaborative planning, design, and construction, require tools that facilitate long-term impact analysis and easy communication among built-environment professionals. Environmental impact assessment (EIA) is instrumental in studying transportation project impacts, but it is a time-consuming process because of the large number of dependent and independent variables involved. A vulnerability grade map and road distribution map were produced, providing an overall vulnerability score for each of the three planning alternatives considered. A road alignment was indicated as the preferred corridor, which was further recommended for a project development and environmental (PD&E) study.

Model multifactor analysis of soil heavy metal pollution on plant germination in Southeast Chengdu, China: Based on redundancy analysis, factor detector, and XGBoost-SHAP

This study assessed the levels of soil heavy metal pollution in agricultural land in southeastern Chengdu and its effects on the germination stage of higher plants. Through extensive soil sampling and laboratory analyses, 15 soil environmental factors were measured, including soil density, porosity, pH, field moisture capacity (FMC), calcium carbonate (CaCO3), and heavy metals such as arsenic (As) and cadmium (Cd). Acute toxicity tests were performed on sorghum (Sorghum bicolor) and Brassica napus (Brassica napus var. napus). The results of the geo-accumulation index (Igeo) and enrichment factor (EF) analyses indicate a higher risk of pollution and enrichment of As and Cd in the study area, with relatively lower risks for other heavy metals. Additionally, the current soil heavy metal concentrations inhibited the growth of sorghum and Brassica napus shoots and roots during the germination stage. Redundancy analysis (RDA), factor detector, and XGBoost-SHAP models identified the As, Cd, FMC, and CaCO3 contents, soil density, and porosity as the primary factors influencing plant growth. Among these factors, FMC, porosity, and Cd were found to promote plant growth, whereas soil density and As demonstrated inhibitory effects. CaCO3 had a dual effect, initially promoting growth but later inhibiting it as its concentration increased. Further analysis revealed that Brassica napus is more sensitive to soil environmental factors than sorghum, particularly to Cd and As, while sorghum has greater tolerance. Moreover, roots were found to be more sensitive than shoots to soil environmental factors, with roots being influenced primarily by physical factors such as FMC and soil density, whereas shoots were affected primarily by chemical factors such as As and Cd.This study addresses the significant lack of data regarding the impact of soil heavy metal concentrations on plant growth in southeastern Chengdu, providing a scientific basis for regional environmental monitoring, soil remediation, and plant cultivation optimization.

Assessment of soil environmental capacity for heavy metals in Shantou City, Guangdong Province, China: source analysis and enrichment evaluation.

Most studies assessing soil environmental capacity (EC) often overlook the impact of heavy metal sources. Analyzing the sources of heavy metals (HMs) provides a better understanding of regional environmental capacity characteristics and their dynamic changes. The current study focuses on the surface soil of Shantou, using 511 soil samples to assess the soil environmental capacity. Results indicate that the contents of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn in Shantou's surface soil are notable, with lead moderately enriched and other metals lightly enriched. The principal component analysis (PCA) identifies five primary sources of heavy metals: mixed natural and agricultural sources, mixed agricultural and industrial sources, industrial sources, mining sources, and quarrying sources. The primary source contributing significantly to soil HM concentrations in Shantou City is a complex interplay between natural geological processes and extensive agricultural practices. In terms of static environmental capacity, Zn, Cr, Ni, Pb, Cu, As, Hg, and Cd are ranked in descending order. The overall environmental capacity for heavy metals in the soil is at a medium level, influenced by geological backgrounds. However, regions such as Yanhong Town, Guiyu Town, and Chendian Town face lower environmental capacities due to comprehensive human activities, posing certain risks. This study provides a scientific reference for forecasting, controlling soil heavy metal pollution, and improving soil quality and environmental capacity in Shantou City.

Ecological risk assessment of heavy metals from application of sewage sludge on agricultural soils in Europe

AbstractThe present European Union (EU) Sewage Sludge Directive (86/278/EEC) is undergoing modifications aimed at enhancing its applicability in the agricultural sector. The Directive's existing limit values for heavy metal concentrations in soils are in the process of being revised. However, to comprehensively understand their effects on EU agricultural lands, additional evaluations are necessary. This is particularly important given that ecological risk assessments are often performed on a site‐specific basis, potentially overlooking broader regional implications. The main objective of the current work is to introduce a methodological approach to quantify the impact of sewage sludge (SS) application on agricultural soils in the EU and the United Kingdom. Concentrations of heavy metals (HMs) (Cd, Cu, Hg, Ni, Pb and Zn) in agricultural land from Land Use/Land Cover Area Frame Survey (LUCAS) 2009 topsoil database were used as a baseline. Maximum quantities of SS that can be safely applied to agricultural lands were obtained by a modeling procedure was used to determine the maximum safe quantities of SS that can be applied to agricultural lands for each country within the European Member States and the United Kingdom. Accumulation of HMs in soils was modelled by using a representative SS composition, distributed over 10 successive years at 5 Mg ha−1 year−1 rate. Ecological risk impact was assessed by using both the single ecological risk index (Er) and the integrated potential ecological risk index (RI). Maximum quantities of SS applied on agricultural soils in EU + UK were estimated to be 45 Mg ha−1 at the country level. We found that 19% of agricultural land (around 28,471,900 ha) in the EU + UK shows a higher RI than moderate risk after long time application of the representative SS. We show that the combination of the HM concentrations from the LUCAS topsoil survey and assumptions on the SS composition and soil HM partitioning can be used to define the actual and potential soil pollution rate in EU + UK. We demonstrate that the proposed methodology can be used by policymakers, farmers, regional authorities and other stakeholders, with possible adaptions based on local in‐depth soil and SS knowledge.

A hyperspectral metal concentration inversion method using attention mechanism and graph neural network

Soil heavy metal contamination has emerged as a global environmental concern, posing significant risks to human health and ecosystem integrity. Hyperspectral technology, with its non-invasive, non-destructive, large-scale, and high spectral resolution capabilities, shows promising applications in monitoring soil heavy metal pollution. Traditional monitoring methods are often time-consuming, labor-intensive, and expensive, limiting their effectiveness for rapid, large-scale assessments. This study introduces a novel deep learning method, SpecMet, for estimating heavy metal concentrations in naturally contaminated agricultural soils using hyperspectral data. The SpecMet model extracts features from hyperspectral data using convolutional neural networks (CNNs) and achieves end-to-end prediction of soil heavy metal concentrations by integrating attention mechanisms and graph neural networks. Results demonstrate that the OR-SpecMet model, which utilizes raw spectral data, achieves optimal prediction performance, significantly surpassing traditional machine learning methods such as multiple linear regression, partial least squares regression, and support vector machine regression in estimating concentrations of lead (Pb), copper (Cu), cadmium (Cd), and mercury (Hg). Moreover, training specialized OR-SpecMet models for individual heavy metals better accommodates their unique spectral-concentration relationships, enhancing overall estimation accuracy while achieving a 20.3 % improvement in predicting low-concentration mercury. The OR-SpecMet method showcases the superior performance and extensive application potential of deep learning techniques in precise soil heavy metal pollution monitoring, offering new insights and reliable technical support for soil pollution prevention and agricultural ecosystem protection. The code and datasets used in this study are publicly available at: https://github.com/zhang2lei/metal.git.

Assessment of seasonal variations in soil heavy metal concentrations and potential health risks in Gujarat, India.

This study investigates the prevalence, distribution, and ecological consequences of 21 heavy metals (Ag, Al, As, B, Ba, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Li, Mg, Mn, Na, Ni, Pb, Sr, and Zn) in the soils between the Mahi and Dhadhar rivers in Gujarat, India. It aims to assess the seasonal variations in soil contamination and the potential human health risk associated with the heavy metal exposure through ingestion, inhalation, and dermal contact pathways. Soil samples were collected from 57 sites across three distinct seasons and analyzed using indices such as the geo-accumulation index, contamination factor, pollution load index, ecological risk factor, and global potential ecological risk. Human health risks were also evaluated for carcinogenic and non-carcinogenic effects. The findings reveal significant soil contamination, especially during the Monsoon and Post-Monsoon seasons, with heavy metals like Ag, Cd, Cr, Cu, Co, Ni, and Pb posing considerable ecological threats. Cr and Ni were identified as presenting the highest carcinogenic risks, while Fe and Cr posed major non-carcinogenic challenges. Seasonal variations significantly influenced heavy metal concentrations and distribution patterns. The study highlights the urgent need for comprehensive policies and sustainable practices to mitigate soil contamination and protect environmental health. It emphasizes the critical role of human activities, such as industrial, agricultural, and mining operations, in soil degradation and calls for increased community awareness and action to address these challenges.

Heavy Metals in the Cultivated Soils of Central and Western Serbia

Concern over the harmful impacts of heavy metal pollution in soil has increased dramatically on a global scale. For the sake of environmental preservation, accurate estimates of the heavy metal concentrations in soil are essential. This study provides valuable data regarding heavy metal concentrations in soil collected from field crops production area in Central and Western Serbia. Five wider localities in the zones of Central and Western Serbia were selected for the collection of soil samples. Based on our research, focused on determining the total contents of heavy metals in the soil and the degree of pollution in the environment caused by their behavior, distribution, and origin, it can be concluded that there is pronounced variability in relation to localities. Heavy metal contents were mostly within the same ranges as those in similar soils from Europe and around the world. Any pollution control system must include heavy metal monitoring, including the methodical collection of data on the concentrations of heavy metals in a particular environment. Before environmental degradation occurs, it is crucial to set pollution limits and implement efficient monitoring procedures.

Combined Approaches for the Remediation of Cadmium- and Arsenic-Contaminated Soil: Phytoremediation and Stabilization Strategies

The prolonged duration of phytoremediation poses a risk of heavy metal dispersal to the surrounding environment. This study investigated a combined remediation approach for cadmium (Cd)- and arsenic (As)-contaminated soil by integrating phytoremediation with stabilization techniques. Bidens pilosa was utilized as the phytoremediator, and steel slag, pyrolusite, and FeSO4 were employed as stabilizing agents in the pot experiments. Key metrics such as soil moisture content, root length, plant height, and heavy metal concentrations in Bidens pilosa were measured to evaluate the remediation efficacy. Additionally, the bioavailability, leaching toxicity, and chemical forms of Cd and As, along with other soil properties, were analyzed. The results indicated that the optimal restoration effect was achieved by combining steel slag, pyrolusite, and FeSO4 with stabilizers in a ratio of 2:1:10. Additionally, the optimal dosage of these materials was found to be 9% by weight. Mechanistic studies, including heavy metal speciation analysis, X-ray photoelectron spectroscopy (XPS), and microbial community diversity analysis, revealed that the stabilization effects were primarily due to the interactions of anionic and cationic ions, chelation by organic acids secreted by plant roots, and enhanced microbial activity. A cost–benefit analysis demonstrated the technical, economic, and commercial viability of the combined remediation approach.

GIS- and Multivariate-Based Approaches for Assessing Potential Environmental Hazards in Some Areas of Southwestern Saudi Arabia.

Soil contamination is a major issue that endangers the ecology in most countries. Total concentrations of As, Cd, Co, Cr, Cu, Mn, Ni, Pb, VFe, and Zn were determined by analyzing soil samples from 32 surface soil samples in southwest Saudi Arabia, including certain areas of Al-Baha. Kriging techniques were used to create maps of the distribution of metal. To assess the levels of soil contamination in the research area, principal component analysis (PCA), contamination factors (CF), and pollution load index were used. The results show the stable model gave the best fit to the As and Zn semivariograms. The circular model fits the Cd, Co, and Ni semivariograms the best, while the exponential model fits the Cr, V, and Fe semivariograms the best. For Ni and Pb, respectively, spherical and Gaussian models are fitted. The findings demonstrated two clusters containing different soil heavy metal concentrations. According to the data, there were two different pollution levels in the research region: 36.58% of it is strongly contaminated, while 63.41% of it has a moderate level of contamination (with average levels of these metals 5.28 ± 5.83, 0.81 ± 0.19, 18.65 ± 6.22, 45.15 ± 23.25, 60.55 ± 23.74, 972.30 ± 223.50, 33.45 ± 14.11, 10.05 ± 5.13, 84.15 ± 30.72, 97.40 ± 30.05, and 43,245.00 ± 8942.95 mg kg-1 for As, Cd, Co, Cr, Cu, Mn, Ni, Pb, V, Fe, and Zn, respectively). The research area's poor management practices are reflected in the current results, which raised the concentration of harmful elements in the soil's surface layers. Ultimately, the outcomes of pollution concentration and spatial distribution maps could aid in informing decision-makers when creating suitable heavy metal mitigation strategies.

Investigating the heavy-metal concentrations in soils from rainwater-harvesting green spaces in Beijing

Investigating the heavy-metal concentrations in soils from rainwater-harvesting green spaces in Beijing

Seed endophytes and rhizosphere microbiome of Imperata cylindrica, a pioneer plant of abandoned mine lands.

Some plant-associated microorganisms could improve host plants biotic and abiotic stress tolerance. Imperata cylindrica is a dominant pioneer plant in some abandoned mine lands with higher concentrations of heavy metal (HM). To discover the specific microbiome of I. cylindrica in this extreme environment and evaluate its role, the microbiome of I. cylindrica's seeds and rhizosphere soils from HM heavily contaminated (H) and lightly contaminated (L) sites were studied. It was found that HM-contamination significantly reduced the richness of endophytic bacteria in seeds, but increased the abundance of resistant species, such as Massilia sp. and Duganella sp. Spearman's rank correlation coefficient analysis showed that both Massilia sp. and Duganella sp. showed a significant positive correlation with Zn concentration, indicating that it may have a strong tolerance to Zn. A comparison of the microbiome of rhizosphere soils (RS) and adjacent bare soils (BS) of site H showed that I. cylindrica colonization significantly increased the diversity of fungi in rhizosphere soil and the abundance of Ascomycota associated with soil nutrient cycling. Spearman's rank correlation coefficient analysis showed that Ascomycota was positively correlated with the total nitrogen. Combined with the fact that the total nitrogen content of RS was significantly higher than that of BS, we suppose that Ascomycota may enhance the nitrogen fixation of I. cylindrica, thereby promoting its growth in such an extreme environment. In conclusion, the concentration of HM and nutrient contents in the soil significantly affected the microbial community of rhizosphere soils and seeds of I. cylindrica, in turn, the different microbiomes further affected soil HM concentration and nutrient contents. The survival of I. cylindrica in HM severely contaminated environment may mainly be through recruiting more microorganisms that can enhance its nutrition supply.

Effects of dissolved organic matter on distribution characteristics of heavy metals and their interactions with microorganisms in soil under long-term exogenous effects

Long-term waste accumulation (LTWA) in soil not only alters its physical and chemical properties but also affects heavy metals and microorganisms in polluted soil through the dissolved organic matter (DOM) it produces. However, research on the impact of DOM from LTWA on heavy metals and microorganisms in polluted soil is limited, which has resulted in an incomplete understanding of the mechanisms involved in LTWA soils remediation. This study focuses on the DOM generated by waste accumulation and analyses the physicochemical properties, microbial community structure, and vertical distribution of heavy metals in four types of LTWA soils at different depths (0–100 cm). A causal analysis is conducted using structural equation modelling. The results indicate that due to the retention effect of the soil and microorganisms, heavy metal pollution is concentrated on the soil surface layer (>30 cm). With increasing depth, there is a decrease in heavy metal concentration and an increase in microbial diversity and abundance. DOM plays a significant role in regulating the concentration of soil heavy metals and the diversity and abundance of microorganisms. The DOM from different soils gradually transforms into substances dominated by tyrosine, tryptophan, and fulvic acid, which sustain the normal life activities and gene expression of microorganisms. Bacteria such as Pseudarthrobacter, Desulfurivibrio, Thiobacillus, and Sulfurimonas, which are involved in energy transformation, along with genes such as water channel protein and YDIF, which enhance heavy metal metabolism, ensure that microbial communities can maintain basic life processes in polluted environments and gradually select for dominant species that are adapted to heavy metal pollution. These novel discoveries illuminate the potential for modulating the composition of DOM to amplify microbial activity, while concurrently offering insights into the migration patterns of various long-term exogenous pollutants. This foundational knowledge provides a foundation for the development of efficacious remediation strategies.

Synergistic role of phenylpropanoid biosynthesis and citrate cycle pathways in heavy metal detoxification through secretion of organic acids

Excessive heavy metal contaminants in soils have serious ecological and environmental impacts, and affect plant growth and crop yields. Phytoremediation is an environmentally friendly means of lowering heavy metal concentrations in soils. In this study, we analyzed phenotypic and physiological traits, and the transcriptome and metabolome, of sheepgrass (Leymus chinensis) exposed to cadmium (Cd), lead (Pb), or zinc (Zn). Phenotypic and physiological analysis indicated that sheepgrass had strong tolerance to Cd/Pb/Zn. Transcriptomic analysis revealed that phenylpropanoid biosynthesis and organic acid metabolism were enriched among differentially expressed genes, and metabolomic analysis indicated that the citrate cycle was enriched in response to Cd/Pb/Zn exposure. Genes encoding enzymes involved in the phenylpropanoid and citrate cycle pathways were up-regulated under the Cd/Pb/Zn treatments. Organic acids significantly reduced heavy metal accumulation and improved sheepgrass tolerance of heavy metals. The results suggest that synergistic interaction of the phenylpropanoid and citrate cycle pathways in sheepgrass roots induced organic acid secretion to alleviate heavy metal toxicity. A cascade of enzymes involved in the interacting pathways could be targeted in molecular design breeding to enhance phytoremediation.