Soil Pb Concentrations Research Articles

Determination of Cu, Pb, and Zn Contents of Soils Formed on Different Parent Materials (Çanakkale, Türkiye)

This study was carried out to determine the heavy metal (Cu, Pb, and Zn) contents and their differences in soils formed on three different parent materials (basalt, clastic and carbonate rocks and granite) in the Ezine district of Çanakkale province. For this purpose, surface sampling was conducted in soils formed under natural conditions on different geological materials. Heavy metal contents of the soil samples were determined using flame atomic absorption spectrometry using the wet digestion Aqua regia method. Analysis of variance (ANOVA) was used to determine the elemental differences in soil samples. Pearson correlation analysis was applied to reveal the relationship between heavy metals and the physicochemical properties of soils taken from different parent materials. As a result of the evaluation, the mean Cu concentrations in the samples ranged from soils formed on basalt (40.8 ± 3.8 μg/g)> soils formed on clastic and carbonate rocks (24.8 ± 1.5 μg/g)> soils formed on granite (22.5 ± 2. 8 μg/g); mean Pb concentrations followed the order of soils formed on granite (37.50 ± 3.30 μg/g)> soils formed on clastic and carbonate rocks (35.67 ± 2.74 μg/g)> soils formed on basalt (21.6 ± 6.7 μg/g). The mean Zn concentrations of the soil samples were as follows: soils formed on basalt (80.9 ± 8.7 μg/g)> soils formed on clastic and carbonate rocks (44.1 ± 3.7 μg/g)> soils formed on granite (37.0 ± 9.4 μg/g). Pb concentrations of all soil samples were found to be higher than the average of the earth's crust.

Farmers' Bussiness as Usual Increase Lead (Pb) Level in the Soil: a Case Study Horticulture Land in Batu, Indonesia

Lead (Pb) is a toxic heavy metal for humans. Agrochemical applications contribute to Pb contamination and accumulation in plants which is harmful to humans. This study aims to investigate the habits of horticultural farmers in applying inorganic fertilizers and pesticides intensively on Pb concentration in the soil and cultivated plants, and its effect on plant growth. The measured parameters consisted of soil and plant Pb content, soil pH, organic C, total N, available P, and exchangeable K. This study used a randomized block design consisting of 4 treatments (Control (KT); (PR); (TN); and (TP)). The results showed that horticultural farmers' dosage of inorganic fertilizers exceeded Indonesia Ministry of Agriculture recommendation. The application of inorganic fertilizers and pesticides by farmers (business as usual) increased Pb content in plants up to 31.46 mg/kg. Besides the application of inorganic fertilizers increases fertilizer residues in the soil by up to 65%, which increases environmental pollution even though the application of inorganic fertilizers increases plant growth and production (shoot length, the number of leaves, and plant dry weight). Thus, wisely applying agrochemicals is required in order to support plant growth and production without further environmental problems.

Cadmium and lead accumulation in important food crops due to wastewater irrigation: Pollution index and health risks assessment

The contamination of farm soils with heavy metals (HMs) has raised significant concerns due to the increased bioavailability and accumulation of HMs in agricultural food crops. To address this issue, a survey experiment was conducted in the suburbs of Multan and Faisalabad to investigate the spatial distribution, bioaccumulation, translocation, and health risks of cadmium (Cd) and lead (Pb) in agricultural crops. The results show a considerable concentration of Cd and Pb in soils irrigated with wastewater, even though these levels were below the permissible limits in water and soil matrices. The pollution index for Cd was mostly greater than 1 at the selected sites, indicating its accumulation in soil over time due to wastewater irrigation. Conversely, the pollution index for Pb was below 1 at all sites. Among the plants, Zea mays accumulated the highest concentration of Cd and Pb. The translocation factor from soil to root was highest for Brassica olearecea (7.037 for Cd) and Zea mays (6.383 for Pb). The target hazard quotient (THQ) value of Cd exceeded the non-carcinogenic limit for most vegetables. The highest value was found in Allium cepa (5.256) and the lowest in Allium sativum (0.040). In contrast, the THQ level of Pb was below the non-carcinogenic limit for most vegetables, except for Allium cepa (1.479), Solanum lycopersicum (1.367), and Solanum tuberosum (1.326). The study highlights that Allium cepa poses the highest health risk for humans, while Medicago sativa poses the highest risk for animals due to Cd and Pb contamination. These results underscore the urgent need for effective measures to mitigate the health risks associated with HM contamination in crops and soils.

Pitfalls of distinguishing anthropogenic and geogenic reasons for risk elements in soils around coal-fired power plants: from a case study in the Northwestern Czech Republic to general recommendations

PurposeMany historical industrial activities, including coal mining and burning, have started near geogenic anomalies. It resulted in spatial overlap of anthropogenic and natural causes of elevated soil risk element contents. Here, distinguishing between anthropogenic and geogenic contributions cannot be achieved by conventional geochemical soil mapping, in particular, when only pseudo-total contents of risk elements were obtained, soil depth profiles were not acquired, and geological maps were not implemented.MethodsThe local geology, topography and anthropogenic activities were taken into account when planning the soil sampling. Soil profiles were obtained using an auger sampler. Total contents of risk and lithogenic elements were obtained by X-ray fluorescence. Aqua regia extraction and inductively coupled plasma mass spectrometry (ICP-MS) were also used for analyses.ResultsCoal use in the study area increased the soil contents of Cd, Hg, and Zn in 7 km circle east of the major power plant, typically to 2 × local background in topsoils. In the profiles closest to that plant, Cd emissions have already been translocated to soils below ploughed horizons that weaken the contamination signal in topsoils. The highest As and Pb contents in the Most Basin soils originated from a local geogenic anomaly and not coal burning.ConclusionCommon soil mapping projects and data mining routines cannot decipher anthropogenic contribution to the soil risk elements unequivocally, as it is demonstrated in this paper. When working in geogenically anomalous areas, a fundamental knowledge of the mechanisms controlling the content of risk elements in soils is required.

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.

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.

Phytoremediation of Toxic Lead from Contaminated Soil Using Neyraudia reynaudiana: Soil of Xuzhou as a Case Study

Lead (Pb), as one of the main pollution elements, has resulted in large-scale soil pollution around the world. Even if phytoremediation can solve this problem, the selection of restoration potential plants has always been a scientific problem. As a multifunctional repair plant, Neyraudia reynaudiana can rehabilitate both polluted soils and slopes. N. reynaudiana has been widely used in terrain restoration in southern China before. This study was the first to study the growth and Pb absorption and enrichment capacity of N. reynaudiana in Xuzhou, north of the Yangtze River. In this study, N. reynaudiana was planted in soils with different lead concentrations, and the change of lead content in roots, shoots, and soils, as well as the redox enzyme, was tested and analyzed during each growth stage. The results showed that the roots could absorb Pb and transfer 79.45% to the shoots at most. With the growth of the plant, the ability to accumulate and transfer gradually increased. Moreover, when the soil Pb concentration was above 800 mg kg−1, the ability to accumulate by N. reynaudiana was significantly restrained. Furthermore, superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), and peroxidase (POD) first acted on the redox response in the initial phase, while increasing the pollutant concentration or the growth of N. reynaudiana in the later stage, and the glutathione reductase (GR) redox system continued to feed back on the lead stress. This study proved that N. reynaudiana is a kind remediation plant for lead pollution soil and could repair soil with a lead pollution concentration lower than 800 mg kg−1. The results provide a theoretical reference for clarifying the action mechanism and threshold value of N. reynaudiana in rehabilitating soil lead pollution and provide practical guidance for the planting proportion of N. reynaudiana.

Study on improvement of copper sulfide acid soil properties and mechanism of metal ion fixation based on Fe-biochar composite

In this study, Fe modification of bamboo biochar (BC) with ferrate was used to construct a composite soil amendment based on K2FeO4-biochar (Fe-BC) system. Based on soil culture experiments, Fe-BC combined with organic–inorganic materials at the application levels of 3%, 5% and 10% to copper sulfide contaminated acid soil was studied. Adsorption kinetics experiment was used to investigate the adsorption capacity of Fe-modified biochar to heavy metal Cu. The results showed that the pH value of bamboo biochar could be increased by 1.12 units after K2FeO4 modification. Compared with the BC, the adsorption capacity of Cu2+ increased from 190.48 to 276.12 mg/g, which was mainly reflected in single-layer surface adsorption and chemisorption. Pore diffusion, electrostatic interaction and surface interaction are the possible mechanisms of Fe-BC interaction with Cu2+ ions. And the contents of Pb, Cu and Zn in soil leaching state decreased by 59.20%, 65.88% and 57.88%, respectively, at the 10% application level of Fe-BC. In general, the composite modifier based on ferrate and biochar has a positive effect on improving the characteristics of acidic soil in copper mining area.

Alternating Direct Current Field Assistant Promoting Chemical Stabilization of Combined Heavy Metals Contaminated Soil

To enhance the remediation efficiency and reduce the chemicals consumption, the combined heavy metals Lead (Pb), Copper (Cu) and Cadmium (Cd) contaminated soil were stabilized by chemical passivators with alternating direct current field (ADC) assistant. The results showed that ADC treatment activated initial heavy metal compounds in the soil. The leaching concentrations of Pb, Cu and Cd in soil under ADC condition increased by 19.61%, 38.77% and 49.10% in comparison with no electric field, respectively. The exchangeable fractions of Pb, Cu and Cd increased while the reducible, oxidizable and residual fractions decreased substantially. Thus, the chemical passivators addition companied with ADC treatment in the beginning increased stabilization efficiency of heavy metals in the soil and reduced over 30% amount of chemical agent consumption, comparing with only chemical passivator stabilization. The mechanism can be speculated that ADC assistant facilitated more heavy metal ions in the soil reacting with phosphorus- containing passivator to formed more complex and stable metal phosphates. It was confirmed by XRD measurement that silicon element in the crystal structure of metallic compound was replaced by phosphorus element.

Prediction of lead in agricultural soils: An integrated approach using machine learning, terrain attributes and reflectance spectra

The study aimed to predict lead (Pb) concentrations in agricultural soils by analyzing visible, near-infrared, and shortwave infrared (VNIR-SWIR) spectra and terrain attributes under three distinct approaches. Approach 1 relied solely on the VNIR-SWIR data, with the cubist_FD (cubist_first derivative) modeling technique as the most effective, achieving a coefficient of determination (R2) of 0.63, a concordance correlation coefficient (CCC) of 0.51, a mean absolute error (MAE) of 6.87 mg kg-1, and a root mean square error (RMSE) of 8.66 mg kg-1. Approach 2 combined VNIR-SWIR spectra and terrain attributes, where SNV (standard normal variate) preprocessing technique and extreme gradient boosting (EGB) yielded superior results with R2 of 0.75, CCC of 0.65, MAE of 5.48 mg kg-1, and minimal RMSE of 7.34 mg kg-1. Approach 3 solely employed terrain attributes, and the combination with cubist modeling demonstrated the best prediction results, with R2 of 0.75, CCC of 0.66, MAE of 6.18 mg kg-1, and RMSE of 7.71 mg kg-1. The cumulative assessment identified that the fusion of terrain properties, SNV-transformed VNIR-SWIR spectra, and EGB as the optimal method for estimating Pb concentrations in agricultural soil yielded the highest R2 value and minimal error. Comparatively, Gaussian process regression (GPR), artificial neural network (ANN), and support vector machine (SVM) techniques achieved high R2 values in approaches 2 and 3 but also exhibited higher estimation errors. In conclusion, the study underscores the significance of using relevant auxiliary datasets and appropriate modeling techniques for accurate Pb concentration predictions with minimal error in agricultural soils. The findings contribute valuable insights for developing successful soil management strategies based on predictive modeling.

Risk assessment of soil and wheat pollution by heavy metals around Zanjan cement factory, Northwest Iran

ABSTRACT This study aimed to evaluate the extent of the risk of lead (Pb) and cadmium (Cd) in the soil and wheat plants grown around the Zanjan cement factory in Northwest Iran and to assess the accuracy of soil regulatory standards in reflecting the potential risk of these pollutants. A total of 73 surface (0–20 cm) soil samples and 31 wheat plant samples were collected and analyzed for their Pb and Cd contents. Statistical analyses revealed that the soil Pb and Cd concentrations varied from 1.2 to 240.0 milligram per kilogram (mg kg−1) and below the detection limit to 27.5 mg kg−1, respectively. Additionally, the wheat grain content of these metallic pollutants ranged from below the detection limit to 3.5 and 1.6 mg kg−1, respectively, indicating that certain wheat samples were polluted with Pb, Cd, or both. Spatial analyses highlighted the significant role of the cement factory in spreading substantial amounts of HMs into the soil. By comparing the soil Pb and Cd total concentrations to their respective national regulatory standards, it was observed that 26.3% and 37.2% of the study area, respectively, were classified as polluted. Integrating the interpolated maps of soil Pb and Cd concentrations and identifying sites in which at least one pollutant exceeded its regulatory standard showed that a greater proportion of the area (42.3%) should be considered polluted. Finally, after accounting for sites in which either the soil was polluted and/or wheat grain content of Pb or Cd exceeded the threshold value of 0.2 mg kg−1, 67.4% of the area was classified as polluted. The findings suggest that thematic maps prepared for individual pollutants are inadequate for pinpointing the potential risk of soil pollution with HMs. Therefore, it is crucial to consider both the soil and plant aspects when assessing the risk of HM pollution in agricultural lands.

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.

Source apportionment and risk assessment of heavy metals in typical greenhouse vegetable soils in Shenyang, China.

In this study, the concentrations of Cr, Cu, Ni, Pb, Zn, Cd, As, and Hg in the typical greenhouse vegetable soils in Shenyang, Northeast of China, were determined, and the pollution characteristics and primary sources of heavy mental pollution in soil were analyzed. Results showed that the sum of the mean values of eight typical heavy metals in the soil of the greenhouse soils was 219.79 mg/kg. According to the "Chinese Environmental Quality Evaluation Standard for Farmland of Greenhouse Vegetables Production" (HJ/T 333-2006), the concentrations of Cu (33.50 ± 11.99 mg/kg), Cd (0.246 ± 0.156 mg/kg), and Hg (0.214 ± 0.177 mg/kg) exceeded the limit values in 14.29%, 39.29%, and 39.29% of sampling points, respectively. The single factor pollution index and the Nemerow comprehensive pollution index of heavy metal elements showed that most greenhouse soils were at safety, alert, or light pollution levels. The potential ecological risk index (RI = 505.19) showed that 42.86% of the samples were at high or very high risk and Cd and Hg were the main ecological risk factors. Based on the result of correlation analysis, the Positive Matrix Factorization (PMF) differentiated sources of heavy metal pollution in the study area into four components, including fertilizer input, soil parent material, pesticide spraying and raw coal combustion, and plastic film mulching, which accounted for 36.76%, 22.64%, 20.89%, and 19.71%, respectively, of the total sources of heavy metals.

Characterization of Microplastics and Associated Heavy Metals in Urban Soils Affected by Anthropogenic Littering: Distribution, Spatial Variation, and Influence of Soil Properties

ABSTRACT Microplastics (MPs) are ubiquitous in terrestrial and marine environments. This research work studied the occurrence, distribution of MPs and heavy metals associated with these MPs in soils of different land- use types viz. residential, commercial, and industrial areas. We identified 15 types of MP polymers, of which polypropylene (15.51%), polyethylene (12.28%), polyamide (9.57%) were dominant. MPs abundance ranged from 8.54 × 104 to 8.51 × 105 particles/kg of soil. Average abundance of MPs in the three fractions viz. < 1 mm, 1–2 mm, and 2–5 mm were 1.85 × 105, 1.31 × 105, and 697.5 particles/kg of soil, respectively. Among the three land use types, MPs abundance in < 1 mm and 1–2 mm soil fractions were significantly higher in residential areas than that in commercial and industrial areas, i.e. <1 mm: 3.4 × 105, 1.26 × 104 and 9.0 × 104, and 1–2 mm: 2.35 × 105, 8.36 × 104, and 7.50 × 104 particles/kg, respectively, for residential, commercial and industrial areas respectively. The average concentration of Cr, Fe, Mn, Cd, Co, Ni, Cu and Pb in soils were 31.79, 11047.29, 423.92, 0.05, 11.86, 17.53, 125.95 and 28.29 mg/kg, respectively, whereas that in MPs were 6.27, 2181.64, 277.36, 0.24, 6.43, 8.29, 47.05 and 9.24 mg/kg, respectively. Concentration of Cd sorbed on MPs was higher than those found in soils. Also, Pb and Cd in MPs were positively correlated to MPs abundance, indicating MPs can be vectors of heavy metals in soils. The synergistic effect of MPs and heavy metals might put soil organisms at risk.

Effect of Bio-Organomineral Fertilizer on Soil Chemical Properties and PB Content of Industrial Waste Polluted Soil

Rancaekek Sub-district is one of the agricultural centers in Bandung Regency, Indonesia, which is currently experiencing land conversion into an industrial area. The industrial activities produce waste containing heavy metal lead (Pb), which if not treated can pollute the soil and surrounding waters. Efforts to overcome soil pollution are through fertilization. The purpose of this study was to determine the effect of the dose and incubation time of Bio-organomineral fertilizer with Bacillus subtilis biological agent on improving soil chemical properties and reducing soil Pb levels. This study used a two-factor Factorial Randomized Block Design, namely the dose and incubation time of Bio-organomineral fertilizer. The results showed that the combination of Bio-organomineral fertilizer dose of 225 kg.ha-1 and an incubation time of 30 days was able to increase soil K-potential. The independent factor of Bio-organomineral fertilizer dose had a significant effect on increasing soil pH. The independent factor of incubation time had a significant effect on reducing soil Pb levels. In particular, there was an interaction between the dose and incubation time of Bio-organomineral fertilizer on soil K-potential.

Elevated lead (Pb) in urban European starling (Sturnus vulgaris) feathers is not correlated to physiology or behavior

Urbanization is rapidly changing the environment and creating new challenges in the lives of animals across the globe. Anthropogenic contaminants—like heavy metals—can persist within the environment for prolonged periods of time and present a widespread problem for those living near contaminated areas. Lead (Pb) was a commonly used heavy metal that continues to threaten the health of all organisms despite being phased out, especially in urban areas where historical use was more common. In this study, a common urban-adapter, the European starling (Sturnus vulgaris), was trapped to explore whether feather Pb burden is greater in birds from urban habitats than rural habitats, as well as whether Pb burdens were correlated with behavior, physiology, and feather development. Across four sites (two rural and two urban), soil Pb concentrations were measured and 197 free-living starlings were captured to measure feather Pb concentrations. Using linear mixed models, this study found that urban starling nestlings had elevated feather Pb burdens compared to rural nestlings. In contrast, there was no correlation between Pb and urbanization in adult birds whose exposure to Pb may reflect a larger spatial range compared to nestlings. For both nestlings and adults, feather Pb was uncorrelated to corticosterone, testosterone, aggressive behavior, or feather growth rates. These findings suggest that starlings may be a useful biomonitoring tool to detect Pb in the local environment, however, the age and spatial range of birds is a critical consideration in applying this tool. Further work is needed to understand the intricate relationship between heavy metals, behavior, morphological development, and physiology in free-living organisms.

Phytoremediation of Zinc and Lead from Polluted Soils Using Sunflower

Phytoremediation is a new, cost-effective remediation technique. Sunflower, a plant grown in Pb- and Zn-contaminated soils, underwent pot trials to examine its biomass and determine how well may remove the metals. According to the findings, the fresh and dry weights of the developing plants gradually reduced, as the amount of heavy metals in the soil increased. The fresh weights of the shoot and root were decreased by the application of 835 mg/kg soil of Pb and 598 mg/kg Zn. The maximum concentration of lead and zinc in the shoot (146.27and 277.83 mg/kg) and roots (589.77 and 287.97 mg/kg, respectively) was recorded in the plant cultivated in contaminated soil. The root growing on soil treated with 835 mg/kg had the maximum bioaccumulation of lead. The control soil had the longest plant shoot and root lengths, averaging 75.67 cm and 9.5 cm, respectively. Under a soil Pb concentration of 835 mg/kg, the shortest plants had shoots that measured 58.5 cm and roots that measured 6 cm. Similarly, when the soil was treated with 598 mg/kg soil zinc, the plant?s shoot and root decreased dramatically by 55.67 cm and 7 cm, respectively. As a result, the study shows that sunflower plants were better at absorbing lead and zinc, and we recommend using them to clean up soil that has been contaminated with these metals. . KEYWORDS :Sunflower, Heavy Metals, Lead, Phytoremediation, Zinc

Spatial distribution and migration of lead and zinc and the influence of parent materials in typical paddy soils of Hunan Province, China.

This study aimed to investigate the distribution and migration characteristics of lead (Pb) and zinc (Zn) in paddy soils in Hunan Province, China. A total of 343 soil samples from 63 profiles were collected from typical regions. The concentration, spatial distribution, and migration behaviors of Pb and Zn in the paddy soils were examined. The results showed that (1) the concentration ranges of Pb and Zn in the surface layer were 17.62-114.07 mg/kg and 44.98-146.84 mg/kg, respectively. (2) The content was higher in the middle and lower reaches of the Xiangjiang River basin horizontally and exhibited shallow enrichment characteristics vertically. (3) Pb migration was weaker than Zn migration, and the parent material had the most significant influence on Pb and Zn content in the bottom soil layer. The research results will clarify the characteristics of Pb and Zn contents in paddy soils in Hunan Province, further understand the horizontal distribution and vertical migration and transformation characteristics of Pb and Zn contents in paddy soils, and provide basic data for scientific rice cultivation and safe food production.

Would it be better for partition prediction of heavy metal concentration in soils based on the fusion of XRF and Vis-NIR data?

Heavy metal (HM) contamination in soil necessitates effective methods to diagnose suspected contaminated areas and control rehabilitation processes. The synergistic use of proximal sensors demonstrates significant potential for rapid detection via accurate surveys of soil HM pollution at large scales and high sampling densities, and necessitates the selection of appropriate data mining and modeling methods for early diagnosis of soil pollution. The aim of this study is to evaluate the performance of a subarea model based on geographically partitioned and global models based on high-precision energy dispersive X-ray fluorescence (HD-XRF) and visible near-infrared (vis-NIR) spectra using a random forest model for predicting soil Cu and Pb concentrations. A total of 166 soil samples are acquired from a contaminated plot in Baiyin, Gansu Province, China. The soil samples are subjected to HM analysis and proximal sensor scanning in a laboratory. Vis-NIR spectral data are preprocessed using the Savitzky Golay (SG) and first-order derivative with Savitzky Golay (SGFD) methods. The results show that for predicting Cu and Pb concentrations in soil, the subarea models performs better than the global models in terms of quantitative prediction, based solely on individual HD-XRF data. For the subarea and global models, the R2 values are 0.961 and 0.981, respectively; the RMSE values are 27.8 and 79.6, respectively; and the RPD values are 4.96 and 7.38, respectively. However, making use of the random forest algorithm trained with data fusion obtained from the HD-XRF and vis-NIR sensors, the global model achieves the best predictions for Cu and Pb concentrations via HD-XRF + vis-NIR (SGFD) and HD-XRF + vis-NIR (SG), respectively. The results will provide a new perspective for modeling approaches to rapidly invert HM concentrations based on proximal sensor data fusion within a large scope of the study area.

A remote sensing-based strategy for mapping potentially toxic elements of soils: Temporal-spatial-spectral covariates combined with random forest

The selection of predictor variables is a crucial issue in building a digital mapping model of potentially toxic elements (PTEs) in soil. Traditionally, the predictor variables for mapping models of soil PTEs have been chosen from sets of spatial parameters or spectral parameters derived from geographical environmental data. However, the enrichment of soil PTEs exhibits significant variations in both spatial and temporal dimensions, with the temporal dimension often being overlooked in the selection of predictor variables for digital mapping models. This limitation hampers the robustness and generalizability of the models. Therefore, multi-source geographical data were used in this study to determine three temporal indices for characterizing the enrichment process of soil PTEs in temporal dimensions, and additionally to construct the temporal-spatial-spectral (TSS) covariate combinations. The random forest (RF) algorithm was used to map soil PTEs at a regional scale. Results showed that: (1) When using spatial parameters or spectral parameters as predictor variables and measured Pb content as the dependent variable, the values of the model performance indicator RPIQ (ratio of performance to inter-quartile range) were 2.66 and 2.27, respectively. After incorporating the temporal parameters into the model input, values of RPIQ for the RF model reached 3.55 (using spatial-temporal covariates) and 3.21 (using spectral-temporal covariates), representing performance improvements of 33.46% and 41.41%, respectively. (2) The RF model constructed with the temporal-spatial-spectral covariates achieved satisfactory mapping accuracy (R2 = 0.85; RMSE = 0.80 mg kg−1; RPIQ = 4.09). (3) The soil Pb content in the western and northeastern regions was relatively high, while the remaining areas exhibited lower Pb levels, mainly due to industrial activities. (4) The mapping results of Pb obtained in this study were superior to other mapping methods, such as ordinary kriging, artificial neural networks, and multivariate linear regression methods. The soil PTE mapping technique employed in this study that combined TSS covariates with the RF provided an effective methodological approach for preventing soil pollution, controlling environmental risk, and improving soil management.