Soil Heavy Metal Concentrations Research Articles

Modeling Risk Assessment of Soil Heavy Metal Pollution using Partial Least Squares and Fuzzy Logic: A Case Study of a Gully Type Coal-Based Solid Waste Dumpsite

Continuous release and migration of heavy metals from coal-based solid waste (CSW) dumpsites often results in significant encroachment on ecological lands and pollution of natural environments. As a result, there is an urgent need for long-term and rapid monitoring, analysis, and assessment to control environmental risks associated with large CSW dumpsites. We constructed a new composite model (PLS-FL) that uses partial least squares regression (PLSR) and fuzzy logic inference (FLI) to accurately predict heavy metal concentrations in soils and assess pollution risk levels. The potential application of the PLS-FL was tested through a gully type CSW case study. We compared 20 modeling strategies using the PLS-FL: five types heavy metals (Cd, Zn, Pb, Cr and As) * four spectral transformation methods (first derivative (FD), second derivative (SD), reverse logarithm (RL), and continuum removal (CR)) * one variable selection method (competitive adaptive reweighted sampling (CARS)). The results showed that the combination of derivative transformation and CARS was recommended for estimation, with R2 C > 0.80 and R2 P > 0.50. When comparing the PLSR model with four traditional machine learning methods (Support Vector Machines (SVM), Random Forests (RF), Extreme Learning Machines (ELM), and KNN), the PLSR model demonstrated the highest average prediction accuracy. Additionally, the FLI process no longer relies on human perception and expert opinion, enhancing the model's objectivity and reliability. The evaluation results revealed that the heavy metal contamination areas of the CSW dumpsite are concentrated at the bottom of the gully, with more severe contamination in the north. Furthermore, a high-risk zone exists in the interim storage area for CSW to the east of the dump. These findings align with the initial detections at the sampling sites and highlight the need for targeted monitoring and control in these areas. The application of the model will empower regulators to quickly assess the overall situation of large-scale heavy metal pollution and provide scientific program and data support for continuous large-scale pollution risk monitoring and sustainable risk management.

Assessment of heavy metals and human health risk associated with the consumption of crops cultivated in industrial areas of Maputo, Mozambique

This study aimed to evaluate heavy metals concentrations in soils and vegetables (cabbage, lettuce, and cassava) cultivated at Matola and Beluluane Industrial Parks, and to assess health risks linked to their consumption through estimated daily intake, hazard index (HI), and incremental lifetime cancer risk. Concentrations of Al, As, Co, Cd, Cr, Ni, Pb, and Zn were determined in the two sites. Soil concentrations of As at Beluluane site and As, Cd, and Cr at Matola site exceeded reference limits of the Food and Agriculture Organization/World Health Organization, showing heavy metal contamination. At Beluluane site, all studied vegetables presented As and Pb levels higher than reference limits, Cd concentrations were higher than the reference limit in cabbage, lettuce, and cassava leaves. At Matola site crops concentrations of As, Cd, Cr, and Pb exceeded the reference limits. Zinc exceeded the reference limit in all crops except in cabbage. HIs for vegetables from Beluluane exceeded 1.0 in cabbage (2.66), lettuce (2.27), and cassava leaves (2.37). Likewise, at Matola, HIs exceeded 1.0 in lettuce (1.67), cassava leaves (1.65), and root tubers (13). We found that vegetables cultivated in industrial parks present high carcinogenic risk due to heavy metal contamination, rendering them unsuitable for human consumption.

Spatial variability of heavy metals concentrations in soil of auto-mechanic workshop clusters in Nsukka, Nigeria

The indiscriminate disposal of spent engine oils and other hazardous waste at auto mechanic workshops clusters in Nsukka, Enugu State, Nigeria is an environmental concern. This study examines the concentration of heavy metals in the soil inside the workshop cluster and in the unpolluted soil outside the workshop cluster at approximately 100 m. Ten sampling points were randomly selected from within the cluster and another ten from outside the cluster. Using a hand-held Global Positioning System, the coordinates of the selected points were established and used to create a digital map. Soil samples at depths of 0–30 cm and 30–60 cm, were analyzed for Cu, Fe, Zn, Pb, As and Cd using Spectrophotometer. Moisture content determination and particle size analysis were also done on the samples. Spatial variability of heavy metals concentrations of the studied site was also mapped with ArcGIS 10.2.2 using interpolation methods. Results showed that the soil ranged from sandy loam to sandy clay loam. Cadmium and Zinc had the lowest and highest concentration, respectively, in the studied area. Comparing the concentrations of heavy metals in soils within and outside the auto mechanic cluster revealed notable differences across various depths (0–30 cm and 30–60 cm). The analysis results for soil samples within the cluster exhibited concentration levels (mg/kg) ranging from 0.716–0.751 (Cu), 2.981–3.327 (Fe), 23.464–30.113 (Zn), 1.115–1.21 (Pb), 2.6–2.912 (As), and 0.133–0.365 (Cd) demonstrating a variation pattern in the order of Zn > Fe > As > Pb > Cu > Cd. Conversely, for soil samples outside the cluster, concentration levels (mg/kg) ranged from 0.611–0.618 (Cu), 2.233–2.516 (Fe), 12.841–15.736 (Zn), 0.887–0.903 (Pb), 1.669–1.911 (As), and 0.091–0.091 (Cd). To assess the disparity in heavy metal concentration levels between samples collected within and outside the clusters, ANOVA test was performed. The test showed significant difference in heavy metal concentrations between samples within and outside the auto mechanic cluster (p < 0.05), implying auto mechanic activities significantly impact heavy metal levels within the cluster compared to outside areas. The assessment of soil pollution utilized indices including the Geo-accumulation Index (Igeo), Contamination factor (Cf), and anthropogenic metal concentration (QoC). Zinc, Cadmium, and Arsenic showed the highest contamination factors, indicating significant soil contamination likely due to anthropogenic activities. The concentrations of the metals analyzed were within WHO permissible limits while the metals concentrations were also observed to decrease as depth was increased. Using ArcGIS 10.2.2, spatial maps showing heavy metal distribution were developed, with the Kriging method proving superior. This study suggests that heavy metal levels in the soil at the area be monitored on a regular basis.

A new simple index for characterizing the labile heavy metal concentration in soil by diffusive gradients in thin films technique

Diffusive gradients in thin films technique (DGT) is recognized as a more reliable method for determining labile heavy metal (HM) concentration in soil than traditional destructive methods. However, the current DGT measurement index, CDGT, theoretically underestimates the true labile concentration (Clabile) of HMs in soil and lacks direct comparability with the conventional soil HM content indices due to unit differences. Here, we proposed CDGT-W, a new simple index which is defined as the HM accumulation in the binding layer, normalized to the weight of soil (optimized water content = 100% of the maximum water holding capacity) filled in the open cavity-type DGT device over a specified deployment time (optimized time = 24 h). The procedure for measuring CDGT-W is analogous to that of CDGT but includes precise determination of water content (water/dry soil) and the mass of soil filled in the cavity. We conducted measurements of Cu, Pb, Cr(Ⅵ) and As(V) as CDGT-W, CDGT, solution concentration (Csoln), and CaCl2 extractable concentration (CCaCl2) on three soils with a diverse range of HM concentrations. CDGT-W showed significant linear correlations with all other tested indexes. The ratios of CDGT-W to CCaCl2 varied between 0.30 and 0.98 for all HM–soil combinations with only one exception, a range much greater than CDGT/Csoln (typically <0.1) but lower than 1. This suggested that CDGT-W may more accurately reflect Clabile than CDGT (theoretically underestimates Cliable) and CCaCl2(likely overestimates Cliable). Additionally, CDGT-W measurements for these four HMs exhibited a broad measure concentration range and a low detection limit (mg/kg level). Consequently, CDGT-W may offer a more reliable alternative to CDGT for characterizing Clabile in unsaturated soils.

Effects of drip and flood irrigation on soil heavy metal migration and associated risks in China

BackgroundDifferent irrigation methods have variable effects on the physiochemical properties of soils. The predicted widespread replacement of flood irrigation with drip irrigation will alter the micro-environment of the associated soils in terms of migration, transformation, morphology, and toxicology of heavy metals and pesticides. The dynamics of heavy metals in soil under drip irrigation have only been investigated with regard to the use of sewage and reclaimed water; studies comparing these patterns across different irrigation methods are scarce. PurposeHere, we aimed to investigate the effects of drip and flood irrigation on heavy metal distribution in soil and the related risks in three typical agricultural systems in China. MethodsWe used fixed-point sampling, digestion analytical methods, descriptive analyses, one-way analysis of variance (ANOVA), Nemerow pollution index analyses, and correlation analyses to investigate the different effects of drip and flood irrigation on heavy metals (Cd, Cr, Cu, Pb, and Zn) in soils from Hebei, Xinjiang, and Ningxia Provinces, China. Results and DiscussionAnalyses of soil samples collected from drip-irrigated fields revealed significant differences in the concentrations of soil heavy metals at depths of 0–60 cm. Drip irrigation had a greater effect on heavy metal content in the soil surface than flood irrigation. In addition, heavy metals likely accumulated at the edges of the wetting fronts following prolonged irrigation. In comparison to drip irrigation, flood irrigation elevated pollution levels in the soil. The amount of fertiliser affected the leaching and migration of heavy metals in the soils by altering the physiochemical characteristics of the drip irrigation solution. ConclusionDrip irrigation alters the dynamics of heavy metals in soil by influencing their migration and accumulation patterns. This effect can be attributed to the methods of adding water and fertilisers to the soil under drip irrigation. Altering these variables can impact the level of heavy metals that bioaccumulate in crops. This study provides a scientific basis for reducing heavy metal pollution under drip irrigation.

Exploring the influence of ground-dwelling ant bioturbation activity on physico-chemical, biological properties and heavy metal pollution in coal mine spoil

Coal mining activities increase the soil concentrations of heavy metals manifold thus impacting soil health and biodiversity. The understanding of the impact of bioturbation activities by ant colonies on soil in coal mine spoil site across different restoration ages is not studied. The study aimed to investigate the influence of bioturbation activities by two most common and distinct coal mine site inhabiting ant species (C. compressus and C. longipedem) at six different ages (2, 4, 6, 8, 10 and 12 years old) on the soil heavy metal concentrations of Fe, Zn, Mn, Cu, Ni, Pb, Cd and Cr and pH, OM, TC, TN, soil enzyme activity of DH, ACP, β-glucosidase and proteases properties of soil. Soil samples were collected from opencast coalmine spoils during October and November 2017. Reference (Ref.) soil samples (n=10 per site) were collected (from area adjacent to ant nest colony approximately 2-5m distance) from a depth of 0–15cm and ant nest debris soil of each ant species (n=10 per site) were collected from each site. Heavy metal pollution decreased and pH, OM, TC, TN and soil enzyme activity of DH, ACP, β-glucosidase and proteases properties of soil in both Ref. soil and ant nest debris soil increases with the increase of mine site restoration age. Our study revealed that different age of the mine spoil have more profound effects on the soil quality and heavy metal content. Contrary to our hypothesis, regression analysis did not support our notion that ant bioturbation activity directly accelerate heavy metal breakdown. Instead, our findings suggests that ant colonies prefer to construct their nest for the locations with lower heavy metal concentrations and higher enzyme activity and increase in soil porosity is a key factor behind the low heavy metal concentration in the nest debris soil.

Towards interpretable machine learning for observational quantification of soil heavy metal concentrations under environmental constraints

Monitoring heavy metal concentrations in soils is central to assessing agricultural production safety. Satellite observations permit inferring concentrations from spectrum, thereby contributing to the prevention and control of soil heavy metal pollution. However, heavy metals exhibit weak spectral responses, particularly at low and medium concentrations, and are predominantly influenced by other soil components. Machine learning (ML)-driven modelling can produce predictions but lacks interpretability. Here, we present an interpretable ML framework for concentration quantification modelling and investigated the contributions of spectral and environmental factors—pH and organic carbon—to the estimation of metals with multiple concentration gradients, as analysed through SHAP (SHapley Additive exPlanations) data derived from four learning-based scenarios. The results indicated that scenarios SHC (spectral, pH, and organic carbon) and SH (spectral and pH) were the most optimal for chromium (Cr) [RPD = 1.42, Adj R2 = 0.62], and cadmium (Cd) [RPD = 1.80, Adj R2 = 0.80]. Under environmental constraints, the spectral predictability for Cr and Cd was improved by 67 % and 87 %, respectively. We concluded that interpretable modelling, utilising both spectral and soil environmental factors, holds significant potential for estimating heavy metals across concentration gradients. It is recommended that samples with higher organic carbon content and lower pH be selected to enhance Cr and Cd predictions. An advanced grasp of interpretable predictions facilitates earlier warning of heavy metal contamination and guides the formulation of robust sampling strategies.

Fertility and heavy metal pollution in silage maize soil irrigated with different levels of recycled wastewater under conventional and no-tillage practices

AbstractIrrigation with recycled domestic wastewater has been known to obtain positive effects on improving soil fertility, but it may also become a risk factor in case of causing an increase in soil salinity and/or heavy metal concentration of soil. No-tillage can retain soil moisture, helping to reduce irrigation water necessity, and thus lower amounts of heavy metals and salts are added to soil under wastewater irrigation conditions. The objective of this study was to analyze the effects of wastewater irrigation at different levels of on silage maize cultivation under conventional tillage and no-tillage conditions by comparing to full irrigation with fresh water. The two-year experiment was planned according to the split-plots design in the random blocks with three replications. The results indicated that full irrigation with wastewater increased soil salinity, organic matter content, total nitrogen, plant available phosphous, exchangeable cations, exchangeable sodium percentage and soil essential and non-essential heavy metal contents, but decreased soil pH and lime content. Increasing rates in organic matter content, total nitrogen, plant available phosphorus and exchangeable potassium were higher, but in electrical conductivity, and heavy metal accumulation were lower in soil under no-tillage as compared to conventional tillage. Contamination and enrichment factors and geographic accumulation index showed that non-essential heavy metal contamination due to cadmium and nickel, increased in full irrigation with wastewater. Irrigation with wastewater also increased heavy metal accumulation in silage maize. No-tillage can be a recommendable water management practice considering that the risks of soil salinity and heavy metal accumulation can be reduced and that soil fertility can be increased. Also, in reducing the risk of accumulation of cadmium and nickel in soil, 33% deficit irrigation with wastewater can make no-tillage more available.

Heavy metals concentration in soils across the conterminous USA: Spatial prediction, model uncertainty, and influencing factors

Assessment and proper management of sites contaminated with heavy metals require precise information on the spatial distribution of these metals. This study aimed to predict and map the distribution of Cd, Cu, Ni, Pb, and Zn across the conterminous USA using point observations, environmental variables, and Histogram-based Gradient Boosting (HGB) modeling. Over 9180 surficial soil observations from the Soil Geochemistry Spatial Database (SGSD) (n = 1150), the Geochemical and Mineralogical Survey of Soils (GMSS) (n = 4857), and the Holmgren Dataset (HD) (n = 3400), and 28 covariates (100 m × 100 m grid) representing climate, topography, vegetation, soils, and anthropic activity were compiled. Model performance was evaluated on 20 % of the data not used in calibration using the coefficient of determination (R2), concordance correlation coefficient (ρc), and root mean square error (RMSE) indices. Uncertainty of predictions was calculated as the difference between the estimated 95 and 5 % quantiles provided by HGB. The model explained up to 50 % of the variance in the data with RMSE ranging between 0.16 (mg kg−1) for Cu and 23.4 (mg kg−1) for Zn, respectively. Likewise, ρc ranged between 0.55 (Cu) and 0.68 (Zn), respectively, and Zn had the highest R2 (0.50) among all predictions. We observed high Pb concentrations near urban areas. Peak concentrations of all studied metals were found in the Lower Mississippi River Valley. Cu, Ni, and Zn concentrations were higher on the West Coast; Cd concentrations were higher in the central USA. Clay, pH, potential evapotranspiration, temperature, and precipitation were among the model's top five important covariates for spatial predictions of heavy metals. The combined use of point observations and environmental covariates coupled with machine learning provided a reliable prediction of heavy metals distribution in the soils of the conterminous USA. The updated maps could support environmental assessments, monitoring, and decision-making with this methodology applicable to other soil databases, worldwide.

Improving prediction of soil heavy metal(loid) concentration by developing a combined Co-kriging and geographically and temporally weighted regression (GTWR) model

The study of heavy metal(loid) (HM) contamination in soil using extensive data obtained from published literature is an economical and convenient method. However, the uneven distribution of these data in time and space limits their direct applicability. Therefore, based on the concentration data obtained from the published literature (2000–2020), we investigated the relationship between soil HM accumulation and various anthropogenic activities, developed a hybrid model to predict soil HM concentrations, and then evaluated their ecological risks. The results demonstrated that various anthropogenic activities were the main cause of soil HM accumulation using Geographically and temporally weighted regression (GTWR) model. The hybrid Co-kriging + GTWR model, which incorporates two of the most influential auxiliary variables, can improve the accuracy and reliability of predicting HM concentrations. The predicted concentrations of eight HMs all exceeded the background values for soil environment in China. The results of the ecological risk assessment revealed that five HMs accounted for more than 90% of the area at the "High risk" level (RQ ≥ 1), with the descending order of Ni (100%) = Cu (100%) > As (98.73%) > Zn (95.50%) > Pb (94.90%). This study provides a novel approach to environmental pollution research using the published data.

Assessment of Heavy Metal Contamination and Ecological Risk in Soils Developed on Different Rock Types in Southern Bundelkhand Region, Central India

ABSTRACT This study assesses heavy metal (HM) concentrations and ecological risks in soils derived from granite (GrS), basalt (BtS), and amphibolite (AmS) rocks in southern Bundelkhand, central India. HM analysis reveals substantial variations, with Cr, Mn, Co, Ni, Zn, and Cd exhibiting higher concentrations in AmS, followed by BtS and GrS. Mn and Co content exceeds the Indian limit for soil (IS) and European Union (EU) guidelines for most samples. The geo-accumulation index shows high pollution levels of Cd in all soil types. Enrichment factor (EF) assessment reveals significant enrichment of Cd in GrS and BtS (mean 12.53, 6.91, respectively), and Cr, Ni, and Cd in AmS. The potential contamination index (Cp) and pollution load index (PLI) confirm Cd as the most significant contaminant in all soil types, with AmS exhibiting the highest pollution levels (29.31 and 650.68, respectively). The potential ecological risk (RI) assessment identifies Cd as the major contributor to ecological risk in all soil groups, with GrS and BtS displaying low to moderate risk, and AmS posing high to very high ecological risk. Our study highlights the influence of parent material on soil heavy metal concentrations, potentially leading to groundwater contamination and posing risks to human health.

Analysis of heavy metal concentrations in soil using Kriging technique using remote sensing data

Analysis of heavy metal concentrations in soil using Kriging technique using remote sensing data

Risk assessment and source identification of soil heavy metals: a case study of farmland soil along a river in the southeast of a mining area in Southwest China.

To investigate the heavy metals (HMs) contamination of surface farmland soil along the river in the southeast of a mining area in southwest China and identify the contamination sources, 54 topsoil samples were collected and the concentrations of seven elements (Zn, Ni, Pb, Cu, Hg, Cr, and Co) were determined by inductively coupled plasma optical emission spectrometry (ICP-OES) and atomic fluorescence spectrometry (AFS). The geo-accumulation index ([Formula: see text]) and comprehensive potential ecological risk index ([Formula: see text]) were used for analysis to determine the pollution degree of HMs and the risk level of the study area. Meanwhile, the Positive Matrix Factorization (PMF) model was combined with a variety of statistical methods to determine the sources of HMs. To explore the influence of the river flowing through the mining area on the concentrations of HMs in the farmland soil, 15 water samples were collected and the concentrations of the above seven elements were determined. The results showed that the concentrations of Pb, Cu, and Zn in soil all exceeded the risk screening value, and Pb in soil of some sampling sites exceeded control value of "Agricultural Land Soil Pollution Risk Control Standard".[Formula: see text] showed that Pb was heavily contaminated, while Cu and Zn were moderately contaminated. RI showed that the study area was at moderate risk. PMF and various statistical methods showed that the main source of HMs was the industrial source. In the short term, the river flowing through the mine has no significant influence on the concentration of HMs in the soil. The results provide a reference for the local government to control contamination and identify the sources of HMs.

Heavy metal status in cocoa (Theobroma cacao L.) soils and beans: the case of Abuakwa North Municipality of Eastern Region, Ghana.

In recent times, public concerns over the potential accumulation of heavy metals in agricultural soils and crops due to the excessive use of agrochemicals are increasing. This study was conducted in the Abuakwa North Municipality of Eastern Region, Ghana, to assess the status of heavy metals in cocoa soils and beans. Cocoa farms aged between 10 and 20years and have received agrochemicals for at least 5years were selected, and their adjacent forests were used as controls. Soil samples and cocoa pods were collected randomly and processed for laboratory analysis. The study discovered that across the sampling sites, heavy metal concentrations in soil were below permissible limits. However, contamination factor and geo-accumulation index results revealed that Tontro cocoa farms were considerably contaminated and moderately polluted respectively with Cu, probably due to long-term fungicide application. The concentrations of Fe (39.3-47.1mgkg-1) and Zn (54.8-70.0mgkg-1) in the cocoa beans across the communities were below the FAO/WHO's safe limit of 99.4mgkg-1 and 73.0mgkg-1 respectively. Lead (0.03-0.06mgkg-1) and Cd (0.12-0.20mgkg-1) concentrations were below the EU's maximum allowable level and Codex Alimentarius Commission's limit of 1.0mgkg-1 respectively. However, bean Cu content in the Tontro farm (51.5mgkg-1) was slightly above the EU's recommended maximum limit of 50.0mgkg-1, possibly due to the continuous application of copper-based fungicides. The study showed that, overall, agrochemicals used for cocoa farming in Abuakwa North Municipality of Ghana did not cause serious harm to the soils and beans, since the mean metal concentrations were not alarming and guarantee public health safety.

Chelated- and nano iron fertilization affects nutrient uptake and translocation in fresh market tomatoes

Our study aimed to evaluate the effects of nano and chelated Fe fertilization on nutrient and heavy metals uptake and their translocation from soil to root, stem, leaf, and fruits of fresh market tomatoes (Solanum Lycopersicon, cv. Sunbrite). A completely randomized design in a factorial experiment, with two Fe sources (nano vs. chelated) applied at 0 (control), 10, 20, and 40 mg/L via drip-irrigated plasticulture, was conducted. Nutrient and heavy metals concentrations in soil, water, tomato roots, stems, leaves, and fruits were determined. Soil had higher K, Fe, Ca, Mg, P, and Mn but lower Ni, Cd, and Cr concentrations. The P, K, Fe, Cu, Mn, and Na concentrations in the roots were significantly affected by both nano and chelated Fe fertilization; however, without following a consistent pattern. Averaged across the Fe treatments, K, Ca, Mg, Fe, and P that translocated from the soil were dominated in the roots, stems, and leaves. The nutrient translocation order, from roots to fruits in response to Fe fertilization, was nano Fe (40%) > chelated Fe (37%) > control (23%). Based on the treatment index values calculated across treatments, it is evident that Fe fertilization plays a pivotal role in nutrient uptake and translocation. The use of chelated Fe at a concentration of 20 mg/L had a pronounced effect on improving the nutritional quality of the tomatoes and, by extension, their associated public health benefits. In conclusion, the chelated Fe (20 mg/L) stands out as the most preferable and effective Fe fertilization for tomatoes.

Plant Growth-Promoting Bacteria (PGPB): A Potent Source of Heavy Metal Stress Management in Plants

Heavy metals or metalloids are toxic elements found throughout the crust of the earth’s surface. The gradual increase of heavy metal concentration in soil and water due to some natural and anthropogenic activities like application of agrochemicals, waste disposal, industrial activities, mining, smelting, lead-based paints, etc cause stress to the local vegetation. Soil microorganisms play a critical part in the remediation of heavy metal contaminated soil and thereby exert direct or indirect promotion to plant growth. Plant growth-promoting bacteria (PGPB), specially the Plant growth-promoting rhizobacteria (PGPR), are a natural, sustainable, and eco-friendly solution for mitigating stress challenges. They can boost plant growth by alleviating heavy metal toxicity through various mechanisms such as metal sequestration, metal immobilization, and production of metal chelating compounds, which reduce metal toxicity and enhance plant growth. This review summarizes the effect of heavy metal stress on plants, the response of plants to heavy metal stress, mechanisms involved in metal stress tolerance by soil bacteria, and their application in managing heavy metal stress in plants.

[Prediction of Spatial Distribution of Heavy Metals in Cultivated Soil Based on Multi-source Auxiliary Variables and Random Forest Model].

Spatial prediction of the concentrations of soil heavy metals (HMs) in cultivated land is critical for monitoring cultivated land contamination and ensuring sustainable eco-agriculture. In this study, 32 environmental variables from terrain, climate, soil attributes, remote-sensing information, vegetation indices, and anthropogenic activities were used as auxiliary variables, and random forest (RF), regression Kriging (RK), ordinary Kriging (OK), and multiple linear regression (MLR) models were proposed to predict the concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn in cultivated soils. In comparison to those of RK, OK, and MLR, the RF model had the best prediction performance for As, Cd, Cr, Hg, Pb, and Zn, whereas the OK and RK models had highest prediction performance for Cu and Ni, respectively, showing that R2 was the highest, and mean absolute error (MAE) and root mean square error (RMSE) were the lowest. The prediction performance of the spatial distribution of soil HMs under different prediction methods was basically consistent. The high value areas of eight HMs concentrations were all distributed in the southern plain area. However, the RF model depicted the details of spatial prediction more prominently. Moreover, the importance ranking of influencing factors derived from the RF model indicated that the spatial variation in concentrations of the eight HMs in Lanxi City were mainly affected by the combined effects of Se, TN, pH, elevation, annual average temperature, annual average rainfall, distance from rivers, and distance from factories. Given the above, random forest models could be used as an effective method for the spatial prediction of soil heavy metals, providing scientific reference for regional soil pollution investigation, assessment, and management.

Integrated application of metal tolerant P. fluorescens and press mud for conferring heavy metal tolerance to aloe vera (Aloe barbadensis)

Soil pollution due to heavy metal (HM) contamination has emerged as a global issue worldwide owing to their adverse impacts on plant growth. Bioremediation approaches employing living organisms for HM alleviation have gained considerable attention among scientific community. Biological agents such as plant growth-promoting rhizobacteria (PGPR) offer a sustainable way to restore soil health, and their combination with different organic amendments such as press mud (PM) can serve as potential approach for immobilizing HMs in soil. We performed a pot experiment to evaluate the role of individual and combined application of press mud and PGPR strain ‘FQ6’ (identified as Pseudomonas fluorescens) in the phytoremediation of different HMs (Pb, Ni and Cd) and growth promotion of aloe vera. Combined application of FQ6 strain and PM yielded more significant outcomes in terms of all the growth and yield attributes such as leaf length (123 %), plant height (57 %), number of leaves (115 %), fresh and dry weights of gel (246 and 280 %), gel contents (96 %), root length (164 %), root diameter (220 %), no. of root tips (138 %) and root area (315 %), as compared to control. Combined application of FQ6 and PM also led to a significant improvement in different antioxidant activities i.e., CAT (129 %), SOD (48 %), APX (17 %) and POD (83 %) as compared to control. Contrastingly, mobility of these HMs was reduced under combined application of Pseudomonas strain ‘FQ6’ and PM, as there existed a considerable difference between HMs concentrations in soil and plant body. We concluded that joint application of HM-tolerant Pseudomonas FQ6 strain and PM could be an ideal option to alleviate the HM induced adverse impacts on aloe vera by immobilizing them in soil, and subsequently, improving plant growth.

Exploring the accumulation capacity of dominant plants based on soil heavy metals forms and assessing heavy metals contamination characteristics near gold tailings ponds

Heavy metal contamination of soil commonly accompanies problems around gold mine tailings ponds. Fully investigating the distribution characteristics of heavy metals and the survival strategies of dominant plants in contaminated soils is crucial for effective pollution management and remediation. This study aims to investigate the contamination characteristics, sources of heavy metals (As, Cd, Pb, Hg, Cu, Zn, Cr, and Ni) in soils around gold mine tailings ponds areas (JHH and WZ) and to clarify the form distribution of heavy metals (As, Cd, Pb, Hg) in contaminated plots as well as their accumulation and translocation in native dominant plants. The results of the study showed that the concentrations of As, Pb, Cd, Cu, and Zn in soil exceeded the national limits at parts of the sampling sites in both study areas. The Nemerow pollution index showed that both study areas reached extreme high pollution levels. Spatial analysis showed that the main areas of contamination were concentrated around metallurgical plants and tailings ponds, with Cd exhibiting the most extensive area of contamination. In the JHH, As (74%), Cd (66%), Pb (77%), Zn (47%) were mainly from tailings releases, and Cu (52%) and Hg (51%) were mainly from gold ore smelting. In the WZ, As (42%), Cd (41%), Pb (73%), Cu (47%), and Zn (41%) were mainly from tailings releases. As, Cd, Pb, and Hg were mostly present in the residue state, and the proportion of water-soluble, ion-exchangeable, and carbonate-bound forms of Cd (19.93%) was significantly higher than that of other heavy metals. Artemisia L. and Amaranthus L. are the primary dominating plants, which exhibited superior accumulation of Cd compared to As, Pb, and Hg, and Artemisia L. demonstrated a robust translocation capacity for As, Pb, and Hg. Compared to the concentrations of other forms of soil heavy metals, the heavy metal content in Artemisia L correlates significantly better with the total soil heavy metal concentration. These results offer additional systematic data support and a deeper theoretical foundation to bolster pollution-control and ecological remediation efforts in mining areas.

Determination of Heavy Metals Concentrations in Soil and Tomato Plant (Solanum lycopersicom) from Ajiwa Fadama farms, Katsina State.

The study aimed to determine heavy metals Cu, Zn, Ni, and Pb concentration from soil and tomato plants (root stem and fruits) produced from Ajiwa Fadama farm.  Tomato (Solanum lycopersicum L.) is a vegetable cultivated in Ajiwa fadama sites and is also commonly consumed in Katsina and Nigeria at large.  Heavy metal concentrations from soil and tomato plants were determined using Atomic Absorption Spectroscopy (AAS).  The results of heavy metal revealed that lead (Pb) has the highest concentration, followed by Zn, Cu and Ni as the lowest in both soil and plant parts.  However, the concentration of all examined heavy metals in the soils and tomato plants was lower than the permissible values approved by FAO/WHO except for lead (Pb), which has a higher concentration above the permissible limits of 0.3 ppm.  The findings of this study indicate possible exposure to lead by the consumers in the sampling area.  Further studies are recommended to analyze the heavy metals of irrigation water and organic and inorganic fertilizers used