Content Of Heavy Metals In Soil Research Articles

Heavy Metal Accumulation in Soil and Plants Using Municipal Solid Waste Compost in Variable pH Conditions

ABSTRACT Municipal solid waste composts (MSWC) are extensively utilized as organic soil conditioners and fertilizers in agricultural fields across many countries. However, the use of MSWC can lead to heavy metal pollution in soil and plants, which may adversely affect plant growth. This study examined the impact of MSWC on the heavy metal content in plants and soil across various pH levels. The experiment was conducted in a pot setting with three replications. The research findings indicated that MSWC application increased the levels of copper (Cu) and zinc (Zn) in soils but had no significant effect on lead (Pb), nickel (Ni), manganese (Mn), and iron (Fe) levels. Additionally, increases were observed in the Cu, Zn, and Fe content in plants, while Pb and Ni content remained unchanged. Cu, Zn, and Fe are essential for plant growth, and their enhancement in both soil and plants is a direct result of their relatively high presence in the MSWC used. The soil-plant transfer coefficients show that the transfer of heavy metals from soil to plants is highest under acidic conditions, indicating that the heavy metal content in MSWC can vary depending on soil pH and application rate. Therefore, it is crucial to carefully analyze and evaluate the heavy metal contents of soil and compost samples before employing MSWC in agricultural fields.

Accumulation of heavy metals from single and combined olive mill wastewater and pomace in soil and bioaccumulation in tissues of two earthworm species: Endogeic (Aporrectodea trapezoides) and Epigeic (Eisenia fetida).

Soil and earthworms are threatened by anthropogenic contamination resulting from olive mill waste dumping on the soil due to their pollutant properties. While several studies have explored the effects of olive mill waste on soil properties and the accumulation of heavy metals in soil, there is currently a gap in the literature regarding the potential bioaccumulation of heavy metals from olive mill waste in earthworms. In this study, soil with earthworms from two ecological categories (endogeic: Aporrectodea trapezoides and epigeic: Eisenia fetida) was treated with increasing doses of olive mill wastewater (OMWW) and olive mill pomace (OMP), applied individually or combined, in an indoor experiment in plastic containers, under laboratory conditions. The results revealed the presence of significant concentrations of heavy metals in the two types of wastes ranging as follows: Fe˃ Zn˃ Cu˃ Cd˃ Cr for OMWW, and Fe˃ Zn˃ Cu˃ Cr for OMP (with Cd below the detection limit). The study demonstrated distinct effects of OMWW and OMP, both individually and in combination, on soil heavy metal content, ranging as follows: soil OMWW > soil Combination > soil OMP for Cd; soil Combination > soil OMWW > soil OMP for Cr and Fe; and soil Combination > soil OMP > soil OMWW for Cu and Zn. Additionally, our investigation showed that both earthworm species exhibited significant uptake of these metals into their tissues, particularly the endogeic species. Interestingly, the most significant difference between species was in the accumulation of Cu, with the epigeic species accumulating significantly lower amounts.

Improving the enrichment of Cd and Zn in leaf mustard (Brassica juncea) by using metal-activating probiotics

Microbial-assisted phytoremediation is of great significance for the remediation of soil contaminated with heavy metals (HMs). Probiotics are beneficial microorganisms that can improve soil structure and fertility and promote plant growth. This study aimed to investigate the effect of two kinds of probiotics, including Lactobacillus casei (L) and Bacillus licheniformis (B), on activating the remediation potentiality of leaf mustard [Brassica juncea (L.) Czerniak.] for mining soil contaminated by Cd and Zn. The results showed that addition of two probiotics significantly reduced soil pH (with 0.05–0.32 units) and improved the available contents of soil HMs (16.0%–59.9% for Cd and 7.1%–23.9% for Zn) in the indoor-incubation experiment. After probiotic treatments, available Cd and Zn in potting soil treated with 1×109 cfu mL-1 of B were 1.65-fold and 1.66-fold of the control, respectively. Meanwhile, soil alkaline phosphatase, urease and sucrose activities were increased, indicating that soil microbial metabolic activities were also motivated. Addition of L and B significantly improved the biomass and chlorophyll contents of leaf mustard. The contents of Cd, Zn in shoot and root were significantly increased with 1×105 cfu mL-1 of L treatment (shoot: 75.2%, 71.9%; root: 36.8%, 61.2%). Furthermore, the activity of antioxidant enzymes such as superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT) and the level of malondialdehyde (MDA) were increased or inhibited, indicating that the resistance of plants to HMs was enhanced. These results indicated that two kinds of probiotics could improve the activities of Cd and Zn directly in mining soil and promote the growth of leaf mustard, thus increasing the efficiency of phytoremediation for HMs. The study provides a reference value for probiotic-assisted phytoremediation of soil contaminated with HMs.

Exploring Phytoremediation Potential: A Comprehensive Study of Flora Inventory and Soil Heavy Metal Contents in the Northeastern Mining Districts of Morocco.

Mining activities produce waste materials and effluents with very high metal concentrations that can negatively impact ecosystems and human health. Consequently, data on soil and plant metal levels are crucial for evaluating pollution severity and formulating soil reclamation strategies, such as phytoremediation. Our research focused on soils and vegetation of a highly contaminated site with potentially toxic metals (Pb, Zn, and Cu) in the Touissit mining districts of eastern Morocco. Vegetation inventory was carried out in three mine tailings of the Touissit mine fields using the "field tower" technique. Here, 91 species belonging to 23 families were inventoried: the most represented families were Poaceae and Asteraceae, and the biological spectrum indicated a predominance of Therophytes (55.12%). From the studied areas, 15 species were selected and collected in triplicate on the tailings and sampled with their corresponding rhizospheric soils, and analyzed for Pb, Zn, and Cu concentrations. Reseda lutea, lotus marocanus, and lotus corniculatus can be considered as hyperaccumulators of Pb, as these plants accumulated more than 1000 mg·kg-1 in their aerial parts. According to TF, these plant species could serve as effective plants for Pb phytoextraction.

Characterization of Physicochemical Properties and and Heavy Metals Content of Soils under Kratom (Mitragyna speciosa) Cultivation, Kapuas Hulu District, Indonesia

Background: Kapuas Hulu is a district in West Kalimantan Province, Indonesia, which is famous for its production of kratom (Mitragyna speciosa), a traditional medicinal plant. Kratom is the main cultivated plant carried out by farmers. However, data regarding the physicochemical properties of kratom plantation soil are still limited, especially regarding the heavy metal content. This research aimed to determine the physicochemical properties and heavy metal content of soil planted with kratom. Methods: This research was conducted on alluvial soil and peat planted with kratom. This research is survey of 2 fields where sampling from each land was carried out using the diagonal method, where 5 sub-samples were taken per land and composited, except for volume Weight. Result: Mineral land and peat land can be used as land for Kratom cultivation because the physicochemical properties of both lands support the growth of Kratom plus the heavy metal content is below the critical threshold.

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, 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 2–5 m distance) from a depth of 0–15 cm 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 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.

Spatial distribution characteristics and pollution evaluation of soil heavy metals in Wulongdong National Forest Park

To scrutinize the spatial distribution attributes of soil heavy metal content and discern its pollution status within the expanse of Wulongdong National Forest Park, a meticulous investigation is imperative. Three altitude gradients of 900, 1000, and 1069 m were selected on the shady and sunny slopes of Wulongdong National Forest Park, and a total of 300 soil sample points were collected. Soil samples were collected in layers, and the contents of seven soil heavy metal elements, Cr, Cd, Hg, Ni, Se, As, and Pb, were measured. With regard to the national soil element background values, the single factor index method, Nemerow index method, and pollution load index were employed to undertake a thorough assessment of soil heavy metal pollution. (1) The contents of heavy metal elements Cr, Se, As, and Pb in the 0–20 cm soil layer of Wulongdong National Forest Park are lower than the national soil element background value and the Henan soil element background value; the Cd and Hg contents exceed the national soil element background value. The value and Henan soil element background value are 2.2 times and 2.92 times the national soil element background value, and 2.75 times and 9.5 times the Henan soil element background value respectively; Ni content is lower than the Henan soil element background value, but higher than the national soil element background value. The background value is 1.03 times its content. The coefficients of variation of the contents of seven heavy metal elements are all greater than 50%, among which Hg shows extreme variation, and the remaining six are highly variable. (2) In the same soil layer, the Cr and As contents are lower on sunny slopes than on shady slopes, and the contents of Pb, Ni, and Hg are generally higher on sunny slopes than on shady slopes. On sunny slopes, the contents of As, Cd, and Hg decrease with increasing altitude, and the Se content increases with increasing altitude; while on shady slopes, the contents of Cr, Se, and As decrease with increasing altitude, and Pb and Hg content increase with the increase of altitude; the content of heavy metal element As increases with the deepening of the soil layer on shady slopes, and the Hg content decreases with the deepening of the soil layer on sunny slopes. The contents of other heavy metal elements have no obvious regularity among different slope directions, altitudes and soil layers. (3) The single factor index evaluation results show that in the 0 ~ 20c soil layer and on the sunny slope, Hg is heavily polluted, Cd is moderately polluted, Ni is lightly polluted, and Cr, Se, As, and Pb are all non-polluted; On the shady slope, Cd and Hg are moderately polluted, and the other five heavy metal elements are in a non-polluting state. (4) The Nemerow index method evaluation results show that in the 0 ~ 20 cm soil layer, the soil on sunny slopes is significantly more polluted by heavy metals than on shady slopes, and the main pollutants are Ni, Cd and Hg. (5) In the 0 ~ 20 cm soil layer of Wulongdong National Forest Park, the three heavy metal elements Ni, Cd and Hg have reached pollution levels, of which Ni is slightly polluted, Cd and Hg are moderately or above polluted; the sunny slope soil is slightly polluted. Heavy metal pollution, no heavy metal pollution on shady slopes.

Evaluation of Acacia karroo’s Potential Aspect in the Phytoremediation of Soil Pollution

The rise in contaminated sites presents a significant issue for the environment and human health, necessitating the decontamination of the surroundings and the adoption of effective decontamination strategies. This investigation was initiated to assess the potential aspects of Acacia karroo in conjunction with enzyme activity, a method that shows promise for mitigating soil contamination. Acacia karroo, with its hyperaccumulator traits, demonstrates great capacity. Enzymes significantly efficiently convert and detoxify harmful substances to a non-toxic level. ICP-MS quantified the concentrations of trace elements in Acacia karroo, while colorimetric assays were used to determine the activity levels of the enzymes. Ten toxic elements were identified in leaf samples of Acacia karroo in the following sequence: Sr > Zn > Cr > V > Rb > Cu > Ni > Y > Sc > Co; concentrations ranged between 203.86 ± 4.48 ppm (Zn) and 10.12 ± 0.09 ppm (Sc). The concentration of these metals was very high, posing a potential risk of harming the environment. Meanwhile, the three identified enzymes, invertase (INV), phosphatase (PHO), and catalase (CAT), have high and average activity levels, respectively. PHO and CAT showed a positive correlation with Zn, Rb, Sr, and Y, while INV correlated positively with Sc, V, Cr, Co, Cu, and Ni content. The principal component analysis (PCA) findings in this study demonstrated an inconclusive correlation between soil enzyme activity and soil heavy metal content. Both positive and negative correlations between soil enzyme activity and heavy metals were observed. This investigation revealed Acacia karroo as an optimal botanical species for phytoremediation. Consequently, a correlation analysis demonstrated that incorporating the Acacia karroo species along with enzyme activity seems to be a highly promising environmentally friendly technique for remediating soil pollution. The Acacia species can also be used in phytoremediation efforts to help conserve biodiversity. Subsequent investigations should focus on the operational mechanisms of different plant parts used as herbal remedies, isolated compounds, their efficacy, adverse effects, and practical implications.

Quantifying the mitigating effect of organic matter on heavy metal availability in soils with different manure applications: A geochemical modelling study

Manure is one of the main sources of heavy metal (HM) pollution on farmlands. It has become the focus of global ecological research because of its potential threat to human health and the sustainability of food systems. Soil pH and organic matter are improved by manure and play pivotal roles in determining soil HM behavior. Geochemical modeling has been widely used to assess and predict the behavior of soil HMs; however, there remains a research gap in manure applications. In this study, a geochemical model (LeachXS) coupled with a pH-dependent leaching test with continuously simulations over a broad pH range was used to determine the effects and pollution risks of pig or cattle manure separate application on soil HMs distribution. Both pig and cattle manure applications led to soil pH reduction in alkaline soils and increased organic matter content. Pig manure application resulted in a potential 90.5–156.0 % increase in soil HM content. Cattle manure did not cause significant HM contamination. The leaching trend of soil HMs across treatments exhibited a V-shaped change, with the lowest concentration at pH = 7, gradually increasing toward strong acids and bases. The dissolved organic matter-bound HM content directly increased the HM availability, especially for Cu (up to 8.4 %) after pig manure application. However, more HMs (Cr, Cu, Zn, Ni) were in the particulate organic matter-bound state than in other solid phases (e.g., Fe-Al(hydr) oxides, clay minerals), which inhibited the HMs leaching by more than 19.3 % after cattle manure application. Despite these variations, high HM concentrations introduced by pig manure raised the soil contamination risk, potentially exceeding 40 times at pH ±1. When manure is returned to the field, reducing its HM content and mitigating possible pollution is necessary to realize the healthy and sustainable development of circular agriculture.

Selenium and Bacillus proteolyticus SES increased Cu-Cd-Cr uptake by ryegrass: highlighting the significance of key taxa and soil enzyme activity.

Many studies have focused their attention on strategies to improve soil phytoremediation efficiency. In this study, a pot experiment was carried out to investigate whether Se and Bacillus proteolyticus SES promote Cu-Cd-Cr uptake by ryegrass. To explore the effect mechanism of Se and Bacillus proteolyticus SES, rhizosphere soil physiochemical properties and rhizosphere soil bacterial properties were determined further. The findings showed that Se and Bacillus proteolyticus SES reduced 23.04% Cu, 36.85% Cd, and 9.85% Cr from the rhizosphere soil of ryegrass. Further analysis revealed that soil pH, organic matter, soil enzyme activities, and soil microbial properties were changed with Se and Bacillus proteolyticus SES application. Notably, rhizosphere key taxa (Bacteroidetes, Actinobacteria, Firmicutes, Patescibacteria, Verrucomicrobia, Chloroflexi, etc.) were significantly enriched in rhizosphere soil of ryegrass, and those taxa abundance were positively correlated with soil heavy metal contents (P < 0.01). Our study also demonstrated that in terms of explaining variations of soil Cu-Cd-Cr content under Se and Bacillus proteolyticus SES treatment, soil enzyme activities (catalase and acid phosphatase) and soil microbe properties showed 42.5% and 12.2% contributions value, respectively. Overall, our study provided solid evidence again that Se and Bacillus proteolyticus SES facilitated phytoextraction of soil Cu-Cd-Cr, and elucidated the effect of soil key microorganism and chemical factor.

Health and ecological risk of heavy metals in agricultural soils related to Tungsten mining in Southern Jiangxi Province, China.

Dayu County, a major tungsten producer in China, experiences severe heavy metal pollution. This study evaluated the pollution status, the accumulation characteristics in paddy rice, and the potential ecological risks of heavy metals in agricutural soils near tungsten mining areas of Dayu County. Furthermore, the impacts of soil properties on the accumulation of heavy metals in soil were explored. The geo-accumulation index (Igeo), the contamination factor (CF), and the pollution load index (PLI) were used to evaluate the pollution status of metals (As, Cd, Cu, Cr, Pb, Mo, W, and Zn) in soils. The ecological risk factor (RI) was used to assess the potential ecological risks of heavy metals in soil. The health risks and accumulation of heavy metals in paddy rice were evaluated using the health risk index and the translocation factor (TF), respectively. Pearson's correlation coefficient was used to discuss the influence of soil factors on heavy metal contents in soil. The concentrations of metals exceeded the respective average background values for soils (As: 10.4, Cd: 0.10, Cu: 20.8, Cr: 48.0, Pb: 32.1, Mo: 0.30, W: 4.93, Zn: 69.0, mg/kg). The levels of As, Cd, Mo, and tungsten(W) exceeded the risk screening values for Chinese agricultural soil contamination and the Dutch standard. The mean concentrations of the eight tested heavy metals followed the order FJ-S > QL > FJ-N > HL > CJ-E > CJ-W, with a significant distribution throughout the Zhangjiang River basin. Heavy metals, especially Cd, were enriched in paddy rice. The Igeo and CF assessment indicated that the soil was moderately to heavily polluted by Mo, W and Cd, and the PLI assessment indicated the the sites of FJ-S and QL were extremely severely polluted due to the contribution of Cd, Mo and W. The RI results indicated that Cd posed the highest risk near tungsten mining areas. The non-carcinogenic and total carcinogenic risks were above the threshold values (non-carcinogenic risk by HQ > 1, carcinogenic risks by CR > 1 × 10-4 a-1) for As and Cd. Correlation analysis indicated that K2O, Na2O, and CaO are main factors affecting the accumulation and migration of heavy metals in soils and plants. Our findings reveal significant contamination of soils and crops with heavy metals, especially Cd, Mo, and W, near mining areas, highlighting serious health risks. This emphasizes the need for immediate remedial actions and the implementation of stringent environmental policies to safeguard health and the environment.

Indirect Estimation of Heavy Metal Contamination in Rice Soil Using Spectral Techniques.

The rapid growth of industrialization and urbanization in China has led to an increase in soil heavy metal pollution, which poses a serious threat to ecosystem safety and human health. The advancement of spectral technology offers a way to rapidly and non-destructively monitor soil heavy metal content. In order to explore the potential of rice leaf spectra to indirectly estimate soil heavy metal content. We collected farmland soil samples and measured rice leaf spectra in Xushe Town, Yixing City, Jiangsu Province, China. In the laboratory, the heavy metals Cd and As were determined. In order to establish an estimation model between the pre-processed spectra and the soil heavy metals Cd and As content, a genetic algorithm (GA) was used to optimise the partial least squares regression (PLSR). The model's accuracy was evaluated and the best estimation model was obtained. The results showed that spectral pre-processing techniques can extract hidden information from the spectra. The first-order derivative of absorbance was more effective in extracting spectral sensitive information from rice leaf spectra. The GA-PLSR model selects only about 10% of the bands and has better accuracy in spectral modeling than the PLSR model. The spectral reflectance of rice leaves has the capacity to estimate Cd content in the soil (relative percent difference [RPD] = 2.09) and a good capacity to estimate As content in the soil (RPD = 2.97). Therefore, the content of the heavy metals Cd and As in the soil can be estimated indirectly from the spectral data of rice leaves. This study provides a reference for future remote sensing monitoring of soil heavy metal pollution in farmland that is quantitative, dynamic, and non-destructive over a large area.

Different “nongrain” activities affect the accumulation of heavy metals and their source-oriented health risks on cultivated lands

“Nongrain” production on cultivated land is one of the primary environmental issues in China. Different “nongrain” activities may introduce different pollution sources to the local environment, leading to variations in heavy metal contents in soil, which can profoundly impact national food security. In this study, three typical “nongrain” regions (Nanxun (NX), Xiaoshan (XS) and Lin'an (LA)) with intensive aquaculture, tea planting and flower (seedling) growth on cultivated land around the Hangzhou metropolitan area were selected to address the spatial heterogeneity of accumulation levels, sources and source-oriented health risks of heavy metals in soil. The results showed that Hg was the main pollutant in NX and XS, while Cd and As were the major contaminants in LA. Aquiculture and sericultural industries (37.43%), natural sources (23.59%) and industrial activities (38.99%) were the major sources in NX; atmospheric deposition (37.73%), flower and seedling planting (23.49%) and metal-related industries (35.16%) were the major sources in XS; and atmospheric deposition (28.06%), excessive application of fertilizers and pesticides during tea planting (43.47%) and natural sources (28.47%) were the major sources in LA. The major risk population, area, exposure route and hazardous elements were children, LA, ingestion and As and Cr, respectively. From the perspective of source-based health risk assessment, in addition to natural sources that are difficult to intervene in, industrial activities, especially leather and wood process industries, metal-related industries and excessive fertilizer and pesticide application during tea planting contributed the most to the total health risk, which explained 67%, 41% and 42%, respectively, of the total risk in NX, XS and LA. High health risks are present in sources with heavy loadings of hazardous heavy metals (As and Cr); thus, to protect human health, the corresponding high-risk anthropogenic pollution sources in different “nongrain” areas need to be controlled.

Effects of land use/cover change on heavy metal distribution of soils in wetlands and ecological risk assessment

This study aimed to determine the impact of land use/cover changes on the heavy metal content in the Sultan Marshland and surrounding area and assess the pollution status. 54 topsoil samples (0–20 cm) were collected from the Rangeland, Farmland, Scrubland, Southern Marshland, Northern Marshland, and Dry Lake areas. The heavy metal contents of the soil samples (Cr, Pb, Fe, Zn, Cu, Co, Mn, Cd, Mo, As, and Ni) were determined using ICP-MS and ICP-OES devices. The impact of land use/cover change on soil heavy metal content was evaluated using variance analysis, while differences between groups were identified using the Duncan test. Principal Component Analysis (PCA) was conducted to identify potential sources of heavy metals. The contamination status of the soils was evaluated based on land use/cover using the Contamination Factor (Cf), Pollution Load Index (PLI), Ecological Risk Factor (Er), and Potential Ecological Risk Index (PERI).Changes in land use/cover around the Sultan Marshlands affected heavy metal distribution of the soils except for Cd. Among all land use/cover types, Fe concentration was the highest in the soils, while Cd concentration was the lowest. Soils in Southern Marshland exhibited higher average concentrations of Cr, Fe, Zn, Co, Cu, and Ni compared to other land uses/covers. Farmlands and rangelands had higher concentrations of Cd, As and Pb. Land use/cover was ranked based on the total heavy metal load in the following order in terms of average values: Southern Marshland > Scrubland > Farmland > Rangeland > Northern Marshland > Dry Lake. According to Cf, the soils in the Dry Lake were exposed to considerable levels of As contamination. Based on PLI, half of the soil sampling points in the Southern Marshland soils showed a degradation in environmental quality. Er indicated that all land uses moderately polluted with Cd. According to the average PERI, all soils under different land use/cover types were categorized as having a low ecological risk. It was believed that heavy metals originated from both natural and human activities. To ensure the sustainability of the ecosystem and to mitigate the risk of heavy metal pollution entering the food chain, it is recommended to manage farming and mining activities and land use habits.

Distribution Prediction of Soil Heavy Metals Based on Remote Sensing Temporal-Spatial-Spectral Features and Random Forest Model

Obtaining soil heavy metal content characteristics and spatial distribution is crucial for preventing soil pollution and formulating environmental protection policies. We collected 304 surface soil samples (0-20 cm) in the Changqing district. At the same time, the spectral, temporal, and spatial features of soil heavy metals were derived from multi-remote sensing data; the temporal-spatial-spectral features closely related to soil heavy metals were selected via correlation analysis and used as input independent variables. The measured soil arsenic (As) content was used as the dependent variable to establish a spatial prediction model based on the random forest (RF) algorithm. The results showed the following:the As content in the soils exceeded the background value by 43.17% but did not exceed the risk screening values and intervention values, indicating slight heavy metal pollution in the soil. The accuracy ranking of the spatial prediction models with one feature type from high to low was spatial features (ratio of performance to inter-quartile range (RPIQ)=3.87)>temporal features (RPIQ=2.57)>spectral features (RPIQ=2.50). The spatial features were the most informative for predicting soil heavy metals. The models using temporal-spatial, temporal-spectral, and spatial-spectral features were superior to those using only one feature type, and the RPIQ values were 4.81, 4.21, and 4.70, respectively. The RF model with temporal-spatial-spectral features achieved the highest spatial prediction accuracy (R2=0.90; root mean square error (RMSE)=0.77; RPIQ=5.68). The As content decreased from the northwest to the southeast due to Yellow River erosion and industrial activities. The spatial prediction of soil heavy metals incorporating remote sensing temporal-spatial-spectral features and the random forest model provides effective support for soil pollution prevention and environmental risk control.

Urbanization-driven forest soil greenhouse gas emissions: Insights from the role of soil bacteria in carbon and nitrogen cycling using a metagenomic approach

Increasing population densities and urban sprawl have induced greenhouse gas (GHG) emissions from the soil, and the soil microbiota of urban forests play a critical role in the production and consumption of GHGs, supporting green development. However, the function and potential mechanism of soil bacteria in GHG emissions from forests during urbanization processes need to be better understood. Here, we measured the fluxes of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) in Cinnamomum camphora forest soils along an urbanization gradient. 16S amplicon and metagenomic sequencing approaches were employed to examine the structure and potential functions of the soil bacterial community involved in carbon (C) and nitrogen (N) cycling. In this study, the CH4 and CO2 emissions from urban forest soils (sites U and G) were significantly greater than those from suburban soils (sites S and M). The N2O emissions in the urban center (site U) were 24.0 % (G), 13.8 % (S), and 13.5 % (M) greater than those at the other three sites. These results were related to the increasing bacterial alpha diversity, interactions, and C and N cycling gene abundances (especially those involved in denitrification) in urban forest soils. Additionally, the soil pH and metal contents (K, Ca, Mg) affected key bacterial populations (such as Methylomirabilota, Acidobacteriota, and Proteobacteria) and indicators (napA, nosZ, nrfA, nifH) involved in reducing N2O emissions. The soil heavy metal contents (Fe, Cr, Pb) were the main contributors to CH4 emissions, possibly by affecting methanogens (Desulfobacterota) and methanotrophic bacteria (Proteobacteria, Actinobacteriota, and Patescibacteria). Our study provides new insights into the benefits of conservation-minded urban planning and close-to-nature urban forest management and construction, which are conducive to mitigating GHG emissions and supporting urban sustainable development by mediating the core bacterial population.

Revealing soil microbial ecophysiological indicators in acidic environments laden with heavy metals via predictive modeling: Understanding the impacts of black diamond excavation

Appraising the activity of soil microbial community in relation to soil acidity and heavy metal (HM) content can help evaluate it's quality and health. Coal mining has been reported to mobilize locked HM in soil and induce acid mine drainage. In this study, agricultural soils around coal mining areas were studied and compared to baseline soils in order to comprehend the former's effect in downgrading soil quality. Acidity as well as HM fractions were significantly higher in the two contaminated zones as compared to baseline soils (p < 0.01). Moreover, self-organizing and geostatistical maps show a similar pattern of localization in metal availability and soil acidity thereby indicating a causal relationship. Sobol sensitivity, cluster, and principal component analyses were employed to enunciate the relationship between the various metal and acidity fractions with that of soil microbial properties. The results indicate a significant negative impact of metal bioavailability, and acidity on soil microbial activity. Lastly, Taylor diagrams were employed to predict soil microbial quality and health based on soil physicochemical inputs. The efficiency of several machine learning algorithms was tested to identify Random Forrest as the best model for prediction. Thus, the study imparts knowledge about soil pollution parameters, and acidity status thereby projecting soil quality which can be a pioneer in sustainable agricultural practices.

Inversion of heavy metal content in soil using hyperspectral characteristic bands-based machine learning method

The global concern regarding the adverse effects of heavy metal pollution in soil has grown significantly. Accurate prediction of heavy metal content in soil is crucial for environmental protection. This study proposes an inversion analysis method for heavy metals (As, Cd, Cr, Cu, Ni, Pb) in soil based on hyperspectral and machine learning algorithms for 21 soil reference materials from multiple provinces in China. On this basis, an integrated learning model called Stacked RF (the base model is XGBoost, LightGBM, CatBoost, and the meta-model is RF) was established to perform soil heavy metal inversion. Specifically, three popular algorithms were initially employed to preprocess the spectral data, then Random Forest (RF) was used to select the best feature bands to reduce the impact of noise, finally Stacking and four basic machine learning algorithms were used to establish comparisons and analysis of inversion model. Compared with traditional machine learning methods, the stacking model showcases enhanced stability and superior accuracy. Research results indicate that machine learning algorithms, especially ensemble learning models, have better inversion effects on heavy metals in soil. Overall, the MF-RF-Stacking model performed best in the inversion of the six heavy metals. The research results will provide a new perspective on the ensemble learning model method for soil heavy metal content inversion using data of hyperspectral characteristic bands collected from soil reference materials.

Ecological risk assessment of heavy metals in tea plantation soil around Tai Lake region in Suzhou, China

Tea plant [Camellia sinensis (L.) O. Kuntze] is one of the important foliar cash crops in China, and its root system absorbs heavy metal (HM) elements enriched in the soil and transports them to the over ground part. In order to ensure the safety of the soil ecological environment and tea raw materials in the tea production area, the HM contents of soil and tea plant leaves in Suzhou tea plantations were detected, the relationship between HMs and soil physicochemical properties was analyzed, and the ecological risk of HMs in tea plantation soils was evaluated by using relevant detection techniques and evaluation models. The results showed that the average pH of tea plantation soils around Tai Lake in Suzhou was within the range suitable for the growth of tea plants. The pH, soil organic matter, total nitrogen, available phosphorus and available potassium of tea plantation soil satisfying the requirements of high quality, high efficiency and high yield (‘3H’) tea plantation accounted for 47.06%, 26.47%, 8.82%, 79.41% and 67.65%, respectively. Site 2 fully met the requirements of ‘3H’ tea plantation. In addition, the contents of cadmium (Cd) and mercury (Hg) were extremely variable, and the average contents exceeded the background value of soil in Jiangsu Province, but the HM contents of tea leaves all met the pollution-free standard, and the HM contents of tea leaves around Tai Lake in Suzhou were generally at a safe level. The composite ecological risk index ranged from 0.05 to 0.60, and 32 of the 34 sample sites (except site 21 and site 23) are the most suitable agricultural land for tea plantations.

Methods for controlling heavy metals in environmental soils based on artificial neural networks

The problem of heavy metal pollution in soil has become a global environmental problem, and it is very important to predict and manage the heavy metals in the environmental soil in a timely manner. The changes in heavy metal content in soil have characteristics such as nonlinearity and large delay, making it difficult to predict heavy metals in soil using traditional methods. Traditional prediction methods are complex and cumbersome, which can lead to longer treatment time and easy secondary pollution. This article analyzed the Back Propagation neural network (BPNN) in artificial neural networks (ANN) and applied it to the prediction of heavy metals in environmental soils. BPNN has good nonlinear function approximation ability, so it can be well applied to complex problems such as soil heavy metal prediction. The methods of treating soil heavy metals include physical repair method, chemical repair method, microbial repair method, plant repair method, plant microbial combined repair method and so on. The use of BPNN can predict heavy metals in environmental soils through adaptive dynamic learning. However, the training time of the BPNN is relatively long and the convergence speed is relatively slow. Therefore, additional momentum terms were added to adjust the weights and thresholds of the network to improve the BPNN. In the experiment, the prediction performance of the improved BPNN was compared before and after the improvement. This article took 50 monitoring data of heavy metals in the same soil in a certain region in 2021 as sample data and predicted the content of heavy metals in the soil using improved and improved BPNN. Due to time constraints, this article only conducted experimental analysis on heavy metals such as lead and cadmium. In the first experiment, when the soil sample data was 50, the prediction accuracy of the BPNN for cadmium before and after improvement was 75.95% and 89.56%, respectively. In the second experiment, when the soil sample data was 50, the prediction accuracy of the BPNN for cadmium before and after improvement was 77.99% and 89.85%, respectively. The improved BPNN has good prediction accuracy and can effectively predict the status of heavy metals in soil. The analysis in this article can provide scientific basis for the comprehensive prevention and control of heavy metals in regional soil, and also provide reference for the development of pollution-free agriculture and ensuring food safety.