Soil Pb Concentrations Research Articles

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.

Assessment of trace elements in the long-term banana cultivation field’s soil

This work assesses the contamination of trace elements (Cr, Cu, Ni, As, Zn Cd, Mn, Fe, and Pb) in soil and different tissues of the banana plant (Musa spp.), the ecological risks of trace elements using various indices, and the probable health risks using a chemometric approach. Soil and different banana plant tissues were collected from banana fields around the industrial area of the capital of old Pundranagar (the earliest urban archaeological location), Bangladesh. Samples were digested by acid digestion, and trace elements were measured by inductively coupled plasma spectrophotometer (ICP-MS). The concentrations of Cr, Ni, Cu, As, Cd, Pb, Fe, Mn, and Zn in soil ranged from 1.50–61.7, 2.42–87.4, 2.00–100.8, 0.25–31.2, 0.10–12.7, 0.60–91.8, 11330–23782, 8.69–105.9, and 7.50–125.9 mg/kg, respectively. The mean concentrations of trace elements in four tissues descend in order of roots &amp;gt; leaves &amp;gt; stems &amp;gt; fruits. The abundances of trace elements varied in both soil and plant samples, which apparently occurred due to the variations of soil parent materials and the excessive use of agrochemicals for long-term banana cultivation. The soil exhibited a moderate to high degree of contamination with trace elements, and Cr, Pb, Zn, Cd, and As mainly originated from anthropogenic sources. Both non-cancer and cancer risks were perceived due to Cr and As exposure from the fruit tissue of banana plants in the study area.

Environmental assessment of heavy metal contamination at Chekfa aggregate quarry (Jijel, North‐East Algeria)

AbstractIt is fascinating to observe the potential hazards associated with mining and quarrying activities, despite the limited number of comprehensive studies conducted in Algeria to evaluate their environmental impact. This particular investigation focuses on El M'cif quarry in Chekfa, located within the Nil watershed in Jijel, a region situated in the North‐East of Algeria. The primary objective is to assess the concentrations of lead (Pb), zinc (Zn), copper (Cu), and cadmium (Cd) in surface water, groundwater, stream sediments, and soil. Consequently, a suite of petrographic and geochemical analyses is undertaken to discern the origins of these heavy metals. Employing statistical methodologies, notably the Geo‐accumulation Index (Igeo), Pollution Index (PI+PI), and Ecological Risk Assessment (ER), this study is further fortified to quantify the levels of hazardous contaminants. The findings reveal that the concentrations of Cd and Pb in surface water are recorded at 22 and 654.3 μg/L, respectively, surpassing both Algerian and WHO standards. Meanwhile, the heavy metal concentrations in stream sediments are ranked in the order of Zn &gt; Cu &gt; Cd, with respectively mean concentrations of 1085.30, 14.60, and 1.49 (mg/kg). The mean concentrations of Zn, Pb, Cd, and Cu in soil are measured respectively at 219.78, 128.86, 20.76, and 2.42 (mg/kg). Based on the statistical methods such as I‐geo, PI, IPI, and RI, the research reveals that the sediments of the Aftis River are uncontaminated by Cu, moderately to heavily contaminated with Zn and heavily contaminated by Cd. Besides the soil samples which are found to be extremely contaminated with Cd, moderately contaminated with Pb, and uncontaminated with Cu and Zn. Additionally, the X‐ray fluorescence analysis reveals that the limestone extracted from the quarry contains 0.7% cadmium, suggesting that the quarry could potentially contribute to cadmium contamination. The surrounding rocks exhibit significant concentrations of iron, lead, and zinc. Therefore, the elevated levels of these heavy metals can primarily be attributed to geological factors rather than human activities, highlighting the combined influence of the extractive industry and the geological environment.

Distribution characteristics, risk assessment, and relevance with surrounding soil of heavy metals in coking solid wastes from coking plants in Shanxi, China.

Heavy metal concentrations represent important pollution evaluation indices, and it is necessary to assess the potential environmental and health risks from heavy metals associated with coking wastes from coking plants. In this study, coking sludge (CS), tar residue (TR), coke powder (CP), and sulfur paste (SP) from three coking plants (Plant A, Plant B, and Plant C) in central, western, and southern Shanxi Province and from soils surrounding Plant A were selected as the research objects, and the distributions of Cu, Ni, Pb, Zn, Mn, Cd, and Cr were determined. The results showed that Cd in the four solid wastes far exceeded the soil background value by a factor of 16~195, and the contents of Pb in TR (three plants) and CS (Plant C) exceeded the soil background values 19.70-, 23.57-, 14.46-, and 12.56-fold, respectively. Similarly, the concentrations of Cu, Ni, Pb, Zn, and Cd in soils were higher than the background values by factors of 31.18, 8.35, 34.79, 29.48, and 3.43, respectively. In addition, the Cu, Ni, Pb, and Cr in the four solid wastes and soils mainly existed in the residual state. As depth increased, the overall Ni, Pb, Mn, and Cd concentrations in soils increased. The high ecological risks associated with the four solid wastes were mainly due to the enrichment of Cd. Workers in coking plants face certain Cr health risks. This study provides theoretical support for the coking industry with respect to the treatment, disposal, and management of solid wastes.

Combination of intercropping maize and soybean with root exudate additions reduces metal mobility in soil-plant system under wastewater irrigation

The effects of root exudates and irrigation with treated wastewater on heavy metal mobility and soil bacterial composition under intercropping remain poorly understood. We conducted a pot experiment with maize and soybean grown in monocultures or intercultures, irrigated with either groundwater or treated wastewater. In addition, the pre-collected root exudates from hydroponic culture with mono- or inter-cropped maize and soybean were applied to the soil at four levels (0 %, 16 %, 32 % and 64 %). The results showed that application of root exudates increased plant growth and soil nutrient content. The analysis of “Technique for Order of Preference by Similarity to Ideal Solution” for higher plant biomass and lower soil Cd and Pb concentrations indicated that the best performance of soybean under treated wastewater irrigation was recorded under intercropping applied with 64 % of exudates, with a performance score of 0.926 and 0.953 for Cd and Pb, respectively. The second-best performance of maize under treated wastewater irrigation was also observed under intercropping applied with 64 % of exudates. Root exudate application reduced heavy metals migration in the soil-plant system, with a greater impact in intercropping than in monocropping. In addition, certain soil microorganisms were also increased with root exudate application, regardless of irrigation water. This study suggests that appropriate application of root exudates could potentially improve plant growth and soil health, and reduce toxic heavy metal concentrations in soils and plants irrigated with treated wastewater.

Distribution of cadmium and lead in soil–rice systems and their environmental driving factors at the island scale

Toxic elements, such as Cd and Pb are of primary concern for soil quality and food security owing to their high toxicity and potential for bioaccumulation. Knowledge of the spatial variability of Cd and Pb in soil–rice systems across the landscape and identification of their driving factors are prerequisites for developing appropriate management strategies to remediate or regulate these hazardous contaminants. Considering the role of rice (Oryza sativa) as a dietary staple in China, this study aimed to examine the distribution patterns and drivers of Cd and Pb in tropical soil–rice systems across Hainan Island. To achieve this goal, 229 pairs of representative paddy soil and rice samples combined with a set of environmental covariates at the island scale were systematically analyzed. Arithmetic mean values (AMs) of Cd and Pb in rice were 0.080 and 0.199 mg kg−1, and exceeded the standard limits by 27.1% and 22.7%, respectively. We found that the AMs of Cd and Pb concentrations in paddy soil were 0.294 and 43.0 mg kg−1. Additionally, Cd in 29.26% of soil samples and Pb in 11.35% of soil samples exceeded the risk screening value for toxic elements. The enrichment factor generally showed that soil Cd and Pb on Hainan Island were both moderately enriched. Results obtained from both Spearman's correlation and stepwise regression analyses suggest that the concentrations of soil Cd and Pb are significantly influenced by the soil Na and Fe concentrations. Specifically, an increment of 1 g kg−1 in soil Na caused a rise of soil Cd and Pb by 57.1 mg kg−1 and 34.4 mg kg−1, respectively, while an increase of 1 g kg−1 in soil Fe resulted in a rise by 25.0 mg kg−1 and 14.5 mg kg−1. Similarly for rice grains, an increment of 1 g kg−1 in soil Ca resulted in a rise of rice Pb by 30.8 mg kg−1, whereas an increase of 1 g kg−1 in soil Mg led to a decrease in rice Pb by 14.8 mg kg−1. However, no significant correlation between soil Se and rice Cd concentrations was found. Furthermore, the result of geographically weighted regression revealed that the impacts of soil Na, Ca, Fe, and Mg on rice Cd were more significant in the western region, whereas the effects of soil Na and Fe on rice Pb were stronger in the northeastern region. This study provides new insights for the identification of factors influencing the distribution and accumulation of Cd and Pb in tropical island agroecosystems.

Children's lead exposure in the U.S.: Application of a national-scale, probabilistic aggregate model with a focus on residential soil and dust lead (Pb) scenarios

Lead (Pb) exposures from soil and dust ingestion contribute to children's blood lead levels (BLLs) in the United States. The U.S. Environmental Protection Agency (EPA)'s Strategy to Reduce Lead Exposures and Disparities in U.S. Communities and the Federal Action Plan to Reduce Childhood Lead Exposure describe multi-pronged collaborative approaches. These include reducing multi-media lead exposures nationally using analytical tools such as EPA's Stochastic Human Exposure and Dose Simulation model for lead [SHEDS-Pb; formerly known as SHEDS-IEUBK (Integrated Exposure Uptake Biokinetic model)], which was initially developed and applied with a focus on children's drinking water exposures. In this study we applied SHEDS-Pb to determine what residential soil Pb and dust Pb concentrations (individually and their sum) can keep BLLs of potentially exposed young children in the general U.S. population below specified values, considering aggregate exposures from water, soil, dust, food, and air. We considered two age groups (1 to <2 years and 2 to <6 years), two BLL values (5 μg/dL and 3.5 μg/dL), and two population percentiles (95th and 97.5th). Sensitivity analyses were conducted using several alternative model inputs and data sets, yielding 15 scenarios summarized in the paper. Of those scenarios, we focused on ones with the most recent science and available data. Modeled soil Pb concentrations by age group, population percentile and reference BLL scenarios for the focus scenarios ranged from 70 ppm to 220 ppm; and modeled dust Pb concentrations ranged from 110 ppm to 240 ppm. These results are consistent with current soil and dust Pb concentrations in the U.S. general population and are lower than most of the current U.S. Federal standards. Estimated BLLs compared well with measured BLLs from CDC's NHANES 2009–2016 (0–27 % relative error for focus scenarios). This analysis can be used to inform EPA and other federal Pb efforts.

Deep Learning Based Spatial Distribution Estimation of Soil Pb Using Multi-Phase Multispectral Remote Sensing Images in a Mining Area

Extensive investigation and monitoring of lead (Pb) content of soil is significant for ensuring hazard-free agricultural production, protecting human health, and ecosystem security, especially in a mining area. One temporal period of a hyperspectral image is usually used to estimate the spatial distribution of Pb and other heavy metals, but hyperspectral images are usually difficult to obtain. Multispectral remote-sensing images are more accessible than hyperspectral images. In this study, a deep learning-based model using 3D convolution is proposed to estimate the Pb content from the constructed multi-phase, multispectral remote-sensing images. Multi-phase multispectral remote-sensing images were stacked to generate a data set with more spectral bands to reduce the atmospheric absorptive aerosol effect. At the same time, a neural network based on 3D convolution (3D-ConvNet) was proposed to estimate Pb content based on the constructed data set. Compared with partial least-squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVMR), and gradient-boosting regression (GBR), experimental results showed the proposed 3D-ConvNet has obvious superiority and generates more accurate estimation results, with the prediction dataset coefficient of determination (R2) and the mean normalized bias (MNB) values being 0.90 and 2.63%, respectively. Therefore, it is possible to effectively estimate heavy metal content from multi-phase, multispectral remote-sensing images, and this study provides a new approach to heavy metal pollution monitoring.