Soil Heavy Metal Pollution Research Articles

The environmental capacity of rare heavy metal calculation in the Qinghai‒Tibet Plateau region via multifractal analysis.

Accurate assessments of the soil environmental capacity are important for evaluating heavy metal pollution levels, facilitating effective prevention and control measures against such pollution. However, due to the lack of risk screening values for certain key elements, such as Rb, Sn, and Th, the assessment of the soil environmental capacity is not comprehensive. Therefore, in this study, the Menyuan-Huangzhong area of Qinghai Province was selected as the research area, and local background and risk values were established via multifractal analysis, thereby systematically examining the environmental capacity. The findings indicated that within the study area, the static environmental capacity values of 15 elements could be ranked as follows: Ba, Cu, Zn, Cr, Rb, Ni, La, Pb, Th, As, U, Sn, Tl, Cd, and Hg. In general, the residual capacity distribution of the various elements varied across the study area, with lower values primarily found in the northern and central regions and higher values obtained in the northwestern and southwestern regions. Between 2018 and 2068, there was a notable and rapid decline in the dynamic environmental capacity of Hg, Cu, and Cd in the study area. In the Menyuan-Huangzhong area of Qinghai, the average comprehensive soil environmental capacity index reached 0.91, indicating a moderate environmental capacity and slight associated health risks. The findings of this study could serve as a valuable reference for soil heavy metal pollution assessment, early warning, and management in this area; enhance the study of soil environmental capacity methods; and provide a theoretical foundation for subsequent research.

Pollution Assessment and Source Apportionment of Heavy Metals in the Surrounding Soil of Typical Mining Areas in Tongling, Anhui Province

To study the level of heavy metal pollution and ecological risks in the soil around typical mining areas in Tongling, a total of 150 soil samples were collected from the study area. The content characteristics of 10 elements, namely, As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Fe, and Zn, in the soils were analyzed. Methods including enrichment factor, the geo-accumulation index, single-factor pollution index, Nemero comprehensive pollution index, and potential ecological risk index were used to evaluate the pollution status of heavy metals in the soil of the study area. The pollution sources of heavy metals in the soil were also analyzed using correlation analysis, cluster analysis, and principal component analysis. The results showed that except for Cr and Fe, the average contents of the other eight heavy metal elements were higher than the soil background values in the study area. Pb, Zn, As, Cu, and Cd had a high degree of variation and were significantly affected by external interference. The spatial distribution showed that both Cr and Ni showed a decreasing trend from the edge to the central region, whereas the other eight heavy metals showed a decreasing trend from the central region to the surrounding areas. The pollution level of Cd and Cu in the soil of the research area was relatively severe. The overall ecological risk was at a medium to low level. Cd and Hg were the main contributing factors. As, Cd, Cu, Fe, Mn, Pb, and Zn mainly came from agricultural, industrial, and transportation sources, whereas Cr and Ni were mainly from natural sources. However, the sources of Hg were relatively complex. The research results can provide a scientific basis for the prevention and control of soil heavy metal pollution in metal mining areas, as well as the remediation of mine pollution.

Development of an integrated framework for dissecting source-oriented ecological and health risks of heavy metals in soils

Heavy metals (HMs) in soils pose significant risks on ecosystem and human health. To design targeted regulatory measures for mitigating and controlling the risk, it is necessary to accurately identify the pollution sources and environmental risks of soil HMs, as well as to reveal the linkages between them. To date, yet systematic investigation aimed at deciphering the links between source apportionment of soil HMs and their associated environmental risks is still lacking. To fill the gap, an integrated framework has been developed in this study and applied for dissecting the source-sink relationship and source-oriented ecological and health risks of soil HMs in Shanxi, a province with rich coal resource, in which long-term coal mining activities in history has resulted in soil HMs pollution and unavoidably posed environmental risks. Two advanced receptor models, multivariate curve resolution alternating least squares based on maximum likelihood principal component analysis (MCR-ALS/MLPCA) and multilinear engine 2 (ME2), have been employed for apportioning the potential sources, and their apportionment results are jointly incorporated into a modified ecological risk index and a probabilistic health risk assessment model for identifying the source-oriented ecological and health risks posed by soil metals. The results show that the soils in study area have been polluted by HMs (i.e., Cd, Cr, Hg and As) to varying degrees. Industrial activities (35%–35.8%), agricultural activities (11.1%–20.5%), atmospheric deposition (10.5%–13%) and mix source (31.5%–42.6%) are apportioned as the main contributors of soil HMs in the area. The source-oriented ecological risk assessment suggests Hg has presented significant ecological risk and largely contributed by the sources from atmospheric deposition and industrial activities. The source-oriented health risk assessment shows the non-carcinogenic hazard level and carcinogenic risk posed by soil HMs in the study area are acceptable. Relatively, industrial activities and mix source have contributed more on the health risks.

Zeolite, Humus and Biochar Remediation of Heavy Metal Contaminated Soil: A Review

With the swift advancement of industrialization and urbanization, soil heavy metal contamination has grown increasingly severe, presenting a grave menace to the environment and human health. As efficacious soil remediation substances, zeolite, humus, and biochar exhibit considerable potential in the treatment of heavy metal pollution. In this article, the research progress of zeolite, humus, and biochar in the remediation of heavy metal pollution in soil was reviewed, the remediation mechanisms, influencing factors, and practical application effects were analyzed, and the future research direction was prospected. Through comprehensive analysis and discussion, the aim is to offer useful references for the control of soil heavy metal pollution.

Geochemical Evaluation of Heavy Metals Soil Pollution in Al-Hayy District Affected by Agriculture Activities: a case study

Geochemical Evaluation of Heavy Metals Soil Pollution in Al-Hayy District Affected by Agriculture Activities: a case study

A novel spatial prediction method for soil heavy metal based on unbiased conditional kernel density estimation

Soil contamination by heavy metals has emerged as a significant global problem. Accurately mapping the spatial distribution of soil heavy metal pollutant concentrations is indispensable for effective agriculture and environmental management. Nevertheless, challenges arise in obtaining comprehensive data at all desired locations, due to limitations in measuring equipment capacity and the associated capital costs. To obtain soil heavy metal maps efficiently and accurately, this paper proposes a nonparametric spatial prediction method, based on unbiased conditional kernel density estimation (UCKDE). The proposed method incorporates the advantages of both geostatistics and machine learning, including stability, adaptability, and the ability to account for various types of auxiliary information. Additionally, it can directly predict the probabilistic density function (PDF) of soil heavy metal content at the target location based on sampling data without complex parameter settings, providing both a deterministic single value and a probabilistic prediction interval. The proposed method and ordinary kriging (OK) were implemented for the spatial prediction of the six heavy metals (As, Cd, Cu, Hg, Mn, and Sb) with the greatest coefficients of variation (CV = 0.53, 1.14, 0.66, 1.05, 0.81 and 0.74, respectively) in Qingxi Town, Chongqing, China. The results showed that the predictive capability of the proposed method (with RMSE values of 5.82, 0.61, 14.76, 0.15, 383.84, and 0.85, respectively) is superior to that of OK (with RMSE values of 5.29, 0.87, 16.37, 0.22, 493.22, and 1.58, respectively) in most cases, particularly when the CV value is high. Besides, the prediction accuracy of the proposed method can be further enhanced by incorporating parent material, resulting in RMSE values of 3.02, 0.51, 8.98, 0.08, 194.16, and 0.56, respectively. The results affirm the reliability of the proposed method and suggest its effectiveness as a tool for soil heavy metal pollution prediction in practical applications.

Spatial patterns and mechanism of the impact of soil salinity on potentially toxic elements in coastal areas

Soil salinization and heavy metal pollution in the Yellow River Delta region have elicited increasing concern. Therefore, revealing the underlying mechanism of the impact of soil salinity on potential toxic elements (PTEs) is crucial for environmental protection and the rational utilization of resources in this area. In this study, we employed CatBoost–SHAP and multiscale geographically weighted regression (MGWR) models to comprehensively investigate the spatial effects of soil electrical conductivity (EC1:5) on PTEs. Additionally, we employed a space–for–time substitution strategy with the aim of investigating how increasing soil salinity, represented by EC1:5, K+, Na+, Ca2+, and Mg2+, affects the bioavailability of PTEs over time. The primary findings are as follows: (1) for most PTEs, the influence of soil EC1:5 on the bioavailable forms of these elements surpassed its impact on their total concentrations. (2) The results of the MGWR model indicated that exchangeable Ca (aCa) in the soils of the eastern coastal areas markedly increased the bioavailable Cd (aCd), bioavailable Cu (aCu), and bioavailable Zn (aZn). (3) When the soil EC1:5 ranges between 2 and 6 dS/m, exchangeable Na (aNa) primarily competed for the adsorption sites of bioavailable Pb (aPb). However, as the soil EC1:5 increases to 6–10 dS/m, exchangeable Mg (aMg) and aCa became the primary competing ions, with aMg playing a more significant role than aCa. These findings provide valuable theoretical insights and practical guidance for saline-alkali soil improvement and PTEs pollution control in the Yellow River Delta region, thereby providing a foundation for sustainable environmental management and resource utilization.

UHPLC-QTOF-MS-Based Targeted Metabolomics Provides Novel Insights into the Accumulative Mechanism of Soil Types on the Bioactive Components of Salvia miltiorrhiza.

The root of Salvia miltiorrhiza Bunge (SMB) has been widely used to treat cardiovascular diseases. However, the contents of secondary metabolites in the roots from different production areas are significantly different, and the impact of soil factors on this accumulation remains unclear. Therefore, this study aimed to elucidate the regularity of variation between the active components and soil factors through targeted metabolomics and chemical dosimetry. Soils were collected from five different cities (A, B, C, D, and E) and transplanted into the study area. The results showed that there were significant differences in the soil fertility characteristics and heavy metal pollution levels in different soils. Ten water- and twelve lipid-soluble metabolites were identified in SMBs grown in all soil types. SMBs from D cities exhibited the highest total tanshinone content (p < 0.05). The salvianolic acid B content in SMBs from E cities was the highest (p < 0.05). Interestingly, correlation analysis revealed a significant negative correlation between the accumulation of lipid-soluble and water-soluble metabolites. Double-matrix correlation analysis demonstrated that available potassium (AK) was significantly negatively correlated with salvianolic acid B (r = -0.80, p = 0.0004) and positively correlated with tanshinone IIA (r = 0.66, p = 0.008). Conversely, cadmium (Cd) and cuprum (Cu) were significantly positively and negatively correlated with salvianolic acid B (r = 0.96, p < 0.0001 and r = 0.72, p = 0.0024) and tanshinone IIA (r = 0.40, p = 0.14 and r = 0.73, p = 0.0018), respectively. Mantel's test indicated that AK (r > 0.52, p < 0.001), Cu (r > 0.60, p < 0.005), and Cd (r > 0.31, p < 0.05) were the primary drivers of the differences in the active components of SMBs. These findings provide a theoretical framework for modulating targeted metabolites of SMB through soil factors, with significant implications for the cultivation and quality control of medicinal plants.

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.

The distribution and key influential factors of antibiotic resistance genes in agricultural soils polluted by multiple heavy metals.

Due to anthropogenic activities such as mining, several agricultural soils are polluted by multiple heavy metals. However, it is still unclear whether multiple heavy metals could affect the distribution of antibiotic resistance genes (ARGs), and how metals affect ARGs. To understand ARGs' distribution in heavy metal-polluted soils, we chose soils contaminated by different types and contents of heavy metals to determine the ARGs' number and abundance through high-throughput quantitative real-time PCR (HT-qPCR) in this study. Additionally, the factors affecting ARGs' distribution, such as soil properties, mobile genetic genes (MGEs), and bacterial communities, were explored. The results demonstrated that the sampled soils were primarily contaminated by Cd, As, Pb, and Zn, and the pollution load index (PLI) values of these metals ranged from 1.3 to 2.7, indicating a low to moderate degree of heavy metal contamination. The number and abundance of ARGs ranged from 44 to 113 and from 2.74 × 107 copies/g to 1.07 × 108 copies/g, respectively. Besides, abundant MGEs in soils, ranging from 1.84 × 106 copies/g to 5.82 × 106 copies/g, were observed. The pathway analysis suggested that MGEs were the most important factor directly affecting ARG abundance (0.89). Notably, heavy metals also affected the ARG abundance. Proteobacteria and Actinobacteria, the main heavy metal tolerant bacteria, were found to be the main hosts of ARGs through network analysis. ARG-carrying pathogens (ACPs) in agricultural soils were found to carry MGEs, indicating a high risk of dissemination. This study provided important information for understanding the ARGs' fate and also the key factors affecting ARGs' spread in multiple heavy metal-contaminated soils.

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.

Identifying heavy metal sources and health risks in soil-vegetable systems of fragmented vegetable fields based on machine learning, positive matrix factorization model and Monte Carlo simulation

Urban fragmented vegetable fields offer fresh produce but pose a potential risk of heavy metal (HM) exposure. Thus, this study investigated HM sources and health risks in the soil-vegetable systems of Chongqing’s central urban area. Results indicated that Cd was the primary pollutant, with 28.33 % of soil samples exceeding the screening value. Amaranth was particularly problematic, exceeding thresholds for Cd, Hg, and Cr, and both amaranth and celery showed significantly higher HM accumulation (p < 0.05). The HM pollution level in the soil-vegetable system was moderate or above. The sources of HMs identified via Positive matrix factorization (PMF) model included agricultural activities (18.19 %), natural soil parent material (25.88 %), mixed metal smelting and transportation (30.72 %), and coal combustion (25.21 %). Furthermore, evaluations using the Random Forest (RF) model revealed an intricate interaction of factors influencing the presence of HMs, where enterprise density, population density, and road density played significant roles in HMs accumulation. Monte Carlo assessments revealed higher non-carcinogenic risks for children (Pb, As) and greater carcinogenic risks for adults (Cd). Therefore, the issue of HM pollution in soils and vegetables from fragmented fields in industrial urban areas need attention, given the potential for elevated health risks with long-term vegetable consumption.

Multi-objective Identification Method for Influencing Factors of Soil Heavy Metal Content Change

Identifying the influencing factors of soil heavy metal content changes is the basis for reducing or preventing soil heavy metal pollution. Taking an agricultural experimental field in Changping District of Beijing as an example, the heavy metal content changes in As, Cr, Cu, Ni, Pb, and Zn from 2012 to 2022 were firstly analyzed. Secondly, the influencing factors of the heavy metal content changes were detected based on the geographical detector at the single-target and multi-target levels, respectively. Finally, comparative experiments with the correlation analysis method and existing studies were set up to evaluate the effectiveness of the identification method of influencing factors developed in this study. The results showed that human activity factors have exacerbated the changes in soil heavy metal content in the study area as follows： ① At the single-target level, the land use type was the main influencing factor on the changes in Cr, Cu, and Zn contents, and the annual deposition flux influenced the changes in As. The results of the interaction detection showed that there was an enhancement effect among the factors, and the interaction of the human activity factors dominated for the factor identification. ② The results of the multi-target level detection covered the results of the single-target level detection, which could identify more influencing factors. The land use type affected the changes in Cu, Zn, Cr, Ni, and As, and the changes in As and Zn were influenced by the annual deposition fluxes. ③ The multi-target identification method coupled with geographical detector and principal component analysis could effectively identify the influencing factors of soil heavy metal content changes, which was much more effective than the single soil heavy metal correlation method. The developed multi-target identification method for influencing factors of heavy metal content changes can provide technical support for the regional pollution monitoring and macro-management of soil heavy metals.

Soil Cadmium Prediction and Health Risk Assessment of an Oasis on the Eastern Edge of the Tarim Basin Based on Feature Optimization and Machine Learning

Soil heavy metal pollution poses a serious threat to food security, human health, and soil ecosystems. Based on 644 soil samples collected from a typical oasis located at the eastern margin of the Tarim Basin, a series of models, namely, multiple linear regression （LR）, neural network （BP）, random forest （RF）, support vector machine （SVM）, and radial basis function （RBF）, were built to predict the soil heavy metal content. The optimal prediction result was obtained and utilized to analyze the spatial distribution features of heavy metal contamination and relevant health risks. The outcomes demonstrated that： ① The average Cd content in the study area was 0.14 mg·kg-1, which was 1.17 times the soil background value of Xinjiang, making it the primary factor of soil heavy metal contamination in the area. Additionally, the carcinogenicity risk coefficients of Cd for both adults and children were less than 10-4, indicating that there were no significant long-term health risks for humans in the area. ② The estimation accuracies of the five inversion models were compared, and the validation set of the RF model had an R2 value of 0.763 7, which was the highest among the five models. Additionally, the RMSE, MAE, and MBE of the RF model were the smallest among the five models. Therefore, the predicted values of the RF model were most consistent with the measured values of the soil Cd content. The predicted map of soil Cd distribution derived from the RF model coincided best with the interpolation map. ③ The RF model outperformed the other four models in predicting health risks associated with the soil Cd element for both adults and children, resulting in better prediction results. Comparatively, the predicted values of the LR model in the validation set varied greatly, leading to unreliable results. It was demonstrated that the RF was the best model for predicting soil Cd content and evaluating health risks in the study area, considering its superior generalization capability and anti-overfitting ability.

Identification Priority Source of Heavy Metals in Urban Soils Based on Source-specific Ecological Risk Analysis in a Typical Tourist City

To understand the pollution status, distribution characteristics, and pollution sources of soil heavy metals in tourist cities in northwest China, the soil content of heavy metals As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn in the main areas of Dunhuang City was collected and analyzed. The soil heavy metal pollution level was quantitatively evaluated by the methods of the geo-accumulation index and improved Nemerow pollution index, and the sources of heavy metal pollution were quantitatively analyzed using cluster analysis and the positive matrix factorization （PMF） model. The contribution rate of each pollution source to ecological risk was determined by combining the PMF model and comprehensive ecological risk index. The results showed that except for the mean contents of As and Ni, the mean contents of Cd, Cr, Cu, Hg, Pb, and Zn were all higher than the background values in Gansu Province. However, the average content of the eight elements was lower than the screening value of construction land in the Standard for Soil Pollution Risk Control of Soil Environmental Quality Construction Land （trial） （GB 36600-2018）. Among them, the enrichment of Cd, Pb, and Hg was more serious, and the exceedance rate was higher than 90%. The results of the geo-accumulation index indicated that urban soils were mainly polluted by Hg in the study area, and Cd, Cr, Cu, Pb, and Zn pollution also existed in different degrees. The improved Nemerow pollution index illustrated that the comprehensive pollution degree of the soil was clean to moderate pollution, and the overall pollution was light pollution. Based on the PMF model, we could conclude that soil heavy metals in the study area were affected by natural sources, industrial deposition sources, industrial sources, traffic sources, and comprehensive sources, and the contribution rates were 29.28%, 25.86%, 20.13%, 16.5%, and 8.23%, respectively. The specific source-integrated ecological risk assessment model found that the industrial deposition source contributed the most to the ecological risk in the study area and could be regarded as the priority control pollution source, and Hg was considered to be the priority control pollution element for ecological risk.

Screening and Stress Responsive Characteristics of Potential Hyperaccumulator of Pb, Zn, and Cd Compound Heavy Metals

To screen for Pb, Zn, and Cd composite heavy metal hyperaccumulator plants, a survey, sampling, and analysis of dominant plants in typical lead-zinc mines and smelter areas in Baoji City were conducted. Potential Pb, Zn, and Cd composite heavy metal hyperaccumulator plants were initially screened, and a pot experiment of soil cultivation was carried out to observe the response characteristics of chlorophyll （chlorophyll a, chlorophyll b, carotenoids, and total chlorophyll）, antioxidant enzymes （SOD, CAT, and POD）, and other physiological indicators （MDA and proline） under the stress of Pb, Zn, and Cd composite heavy metals. A field experiment was also conducted to further verify and determine their enrichment ability for Pb, Zn, and Cd composite heavy metals, aiming to provide scientific basis and technical support for the remediation of Pb, Zn, and Cd composite heavy metal-polluted soil. The field survey revealed that Symphytum officinale L. met the international hyperaccumulator plant index requirements for the enrichment of Pb, Zn, and Cd, with enrichment quantity, bioconcentration factor （BCF）, and transfer factor （TF） all meeting the requirements. It was a potential hyperaccumulator plant for Pb, Zn, and Cd composite heavy metals. The soil cultivation pot experiment showed that as the gradient of Pb, Zn, and Cd composite heavy metal stress increased, the content of chlorophyll a, chlorophyll b, and total chlorophyll in S. officinale L. leaves gradually decreased, causing disruption to the plant's photosynthetic system when the gradient was greater than or equal to IV. The chlorophyll content in Ricinus communis L. leaves exhibited a "low-stimulation-high-inhibition" phenomenon, while excessive stress stimulated the activation of its own protective systems, leading to reduced toxicity. In addition, there were significant differences （P &lt; 0.05） in the content of chlorophyll a, chlorophyll b, carotenoids, and total chlorophyll between S. officinale L. and R. communis L. both compared to the control treatment and between stress gradients. The SOD activity in the leaves of S. officinale L. and R. communis L. showed a trend of increasing first, then decreasing, and then increasing. The CAT activity in the leaves of S. officinale L. exhibited a "low-stimulation-high-inhibition" effect, whereas the CAT activity in the leaves of R. communis L. showed a trend of continuous decrease. The POD activity in the leaves of S. officinale L. generally increased, whereas in the leaves of R. communis L., it increased first, then decreased, and then increased. The MDA content in the leaves of S. officinale L. generally decreased, whereas in the leaves of R. communis L., it exhibited an upward trend. In addition, whether compared to the control between stress gradients, there were significant differences （P &lt; 0.05） in the SOD, CAT, POD, MDA, and proline content of S. officinale L. and R. communis L. The field experiment results indicated that S. officinale L. could meet the hyperaccumulator plant index requirements for the enrichment of Pb, Zn, and Cd, making it a potential germplasm resource for Pb, Zn, and Cd composite heavy metal hyperaccumulator plants. It can be an ideal choice for the remediation of Pb, Zn, and Cd composite heavy metal-polluted soil.

Heavy metals pollution characteristics, source analysis, and risk assessment in soil based on Geographic Information System: A case study of a typical area in Three Parallel Rivers of Yunnan Protected Areas, China

Three Parallel Rivers of Yunnan Protected Areas, which was listed as World Natural Heritage, was suffering from increasingly serious heavy metal pollution. However, the existing research on heavy metal pollution in the area was almost blank. This study collected 115 surface soil samples from the local area and used inductively coupled plasma optical emission spectroscopy (ICP-OES) to determine the content of local 12 kinds of heavy metals. Results indicated that mining the tungsten-beryllium mine has caused serious heavy metal pollution to the soil in nature reserves. It poses risks to ecology and human health that cannot be ignored. There was severe pollution of tungsten (W) (Igeo = 8.30) and beryllium (Be) (Igeo = 1.92) in the topsoil. Subsequently, applied the pollution index assessment, potential ecological risk assessment (PERI), human health risk assessment (HHRA), and positive matrix factorization (PMF) models in conjunction with geographic information system (GIS) mapping to systematically evaluate the potential hazards of heavy metals in an abandoned tungsten-beryllium, where located in Haba Snow Mountain Nature Reserve that’s the core area of “Three Parallel Rivers”. The PMF model and GIS mapping identified four factors, nature, transportation, mining, and miners living, as the main sources of local heavy metals. The HHRA results indicated no health risks in the study area, but the GIS mapping identified areas where there are. It is the gaps in the systematic study of heavy metal pollution in the core area of the “Three Parallel Rivers” that the results of the study supplement and provide reference data for controlling soil heavy metal pollution in tungsten-beryllium mining areas and valuable information for managers to develop pollution control and risk management strategies.

Mechanisms of Cadmium stress response in watermelon: Insights from physiological, transcriptomic, and metabolic analyses

Cadmium (Cd) contamination of soil may lead to Cd stress for plants, which significantly hinders plant growth and development, posing a risk to human health through the consumption of Cd-contaminated foods. Watermelon (Citrullus lanatus), a widely consumed fruit, is particularly affected by Cd stress globally, yet the mechanisms underlying its response are not well understood. Here, we subjected watermelon seedlings to simulated Cd stress treatment and explored the physiological, transcriptomic, and metabolic response. Our findings revealed that Cd stress treatment led to increased accumulation of reactive oxygen species (ROS) in watermelon leaves. Transcriptome sequencing unveiled a multitude of osmotic and oxidative stress-responsive genes, including peroxidase (POD), MYB, voltage-dependent anion channel (SLAC1), and ABC transporter. KEGG enrichment analysis highlighted the predominant enrichment of Cd stress-responsive genes in pathways such as glutathione (GSH) metabolism, MAPK signaling, and biosynthesis of secondary metabolites. Within the GSH metabolism pathway, several glutathione S-transferase (GST) genes were up-regulated, alongside phytochelatin synthetase (PCS) genes involved in phytochelatin synthesis. In the MAPK signaling pathway, genes associated with ABA and ethylene signal transduction showed up-regulation following Cd stress. Metabolomic analysis demonstrated that Cd stress enhanced the production of amino acids, phenolamines, and esters. Overall, our study elucidates that watermelon responds to Cd stress by activating its antioxidant system, GSH metabolism pathway, MAPK signal pathway, and biosynthesis of key metabolites. These findings offer valuable insights for the remediation of heavy metal pollution in soil affecting plant life.

Organic substitution contrasting direct fertilizer reduction increases wheat productivity, soil quality, microbial diversity and network complexity

Excessive use of chemical fertilizers negatively impacts crop productivity and farmland ecosystem, impeding sustainable agricultural progress. Consequently, there is an immediate need for a chemical fertilizer reduction strategy that ensures crop productivity and improves soil quality and the ecological environment of farmland. This study implemented a three-year (2018–2020) field experiment with two chemical fertilizer reduction methods (direct fertilizer reduction and organic substitution) to investigate their effects on wheat productivity, soil quality, heavy metal pollution risk and microbial characteristics. The results showed that organic substitution treatments (OF1, OF2 and OF3) improved most wheat plant (nutrient uptake and yield and its components) and soil properties (soil nutrients and carbon and nitrogen fractions), leading to increased crop productivity index (CPI, by 9.18 %-16.39 % and 14.14 %-23.36 %) and soil quality index (SQI, by 84.67 %-138.86 % and 104.11 %-175.91 %) compared to conventional fertilization (CF) and direct fertilizer reduction treatments (RF1, RF2 and RF3) in 2019 and 2020. Additionally, organic substitution enhanced the diversity and network complexity of bacterial community, while raising the soil pollution index (SPI, by 9.30 %-12.84 % and 12.20 %-18.49 %) without causing soil heavy metal pollution. Thus, it is recommended to adopt organic fertilizer substitution as the primary chemical fertilizer reduction strategy for wheat production. This approach will ensure crop yield, and improve soil quality and microbial characteristics, but its long-term application requires monitoring changes in soil heavy metals. Overall, this study provides guidelines for implementing scientific fertilization in agricultural practices, thus contributing to the health and sustainability of farmland ecosystems.

Rapid humification of cotton stalk catalyzed by coal fly ash and its excellent cadmium passivation performance.

Due to industrialization, soil heavy metal pollution is a growing concern, with humic substances (HS) playing a pivotal role in soil passivation. To address the long duration of the compost humification problem, coal fly ash (CFA) in situ catalyzes the rapid pyrolysis of the cotton stalk (CS) to produce HS to address Cd passivation. Results indicate that the highest yield of humic acid (HA) (8.42%) and fulvic acid (FA) (1.36%) is obtained when the CS to CFA mass ratio is 1:0.5, at 275 ℃ for 120min. Further study reveals that CFA catalysis CS humification, through the creation of alkaline pyrolysis conditions, Fe2O3 can stimulate the protein and the decomposition of hemicellulose in CS, and then, through the Maillard and Sugar-amine condensation reaction synthesis HA and FA. Applying HS-CS&CFA in Cd-contaminated soil demonstrates a 26.69% reduction in exchangeable Cd within 30days by chemical complexation. Excellent maize growth effects and environmental benefits of HS products are the prerequisites for subsequent engineering applications. Similar industrial solid wastes, such as steel slag and red mud, rich in Fe2O3, can be explored to identify their catalytic humification effect. It could provide a novel and effective way for industrial solid wastes to be recycled for biomass humification and widely applied in remediating Cd-contaminated agricultural soil.