Search Results (284)

Soil contamination by potentially toxic elements (PTEs) is a growing environmental concern, particularly in agricultural regions where soil quality directly affects crop safety and human health. This study evaluates PTE concentrations and ecological risks in agricultural soils of Hautat Sudair, central Saudi Arabia, using contamination indices, multivariate statistics, and GIS-based spatial modeling supported by RS-derived land use/land cover (LULC) mapping. The results show that the mean concentrations of Ni (35.97 mg/kg) and Mn (1230 mg/kg) exceed international thresholds in several locations, while Pb (8.34 mg/kg), Cr (33.00 mg/kg), Zn (60.09 mg/kg), and As (4.25 mg/kg) remain within permissible limits in most samples. Contamination indices, including the Enrichment Factor (EF), Contamination Factor (CF), and Geo-Accumulation Index (Igeo), highlight hotspot behavior, with isolated sites showing elevated concentrations approaching screening levels (e.g., Pb up to 32.0 mg/kg and Cr up to 52.0 mg/kg), whereas Ni and Mn exhibit the most pronounced local enrichment. The Pollution Load Index (PLI) varies from 0.24 to 0.80, indicating low to moderate contamination levels, while the Risk Index (RI) ranges from 10.43 to 41.38, signifying low ecological risk. Multivariate statistical analyses, including correlation matrices and principal component analysis (PCA), reveal that Ni, Cr, and Mn share a common source, possibly linked to anthropogenic inputs and natural geological background. Kaiser–Meyer–Olkin (KMO) and Bartlett’s test confirm the adequacy of the dataset for PCA (KMO = 0.797; χ² = 563.845, p < 0.001). Spatial distribution maps generated using GIS and RS highlight contamination hotspots, reinforcing the necessity for periodic monitoring. By integrating indices, multivariate patterns, and spatial context, this study provides a replicable, research-driven framework for interpreting PTE controls in arid agricultural soils. Full article

Soil heavy metal contamination in agricultural land has emerged as a critical environmental issue, threatening both food security and ecological sustainability. However, the contamination characteristics and associated potential ecological risks under different land use types remain poorly understood. This study presents a systematic comparison of heavy-metal pollution between three distinct agricultural land use systems (suburban vegetable fields, paddy fields, and maize fields) using an integrated approach that combines spatial analysis, pollution indices, and receptor modeling. Dehui City, a major grain-producing region in Northeast China, was selected as the study region, in which 73 topsoil samples were systematically collected. The concentrations and spatial distributions of heavy metals (Cd, Cr, Cu, Hg, Ni, Pb, Zn, and As) were analyzed. Source apportionment of soil heavy metals was performed using principal component analysis (PCA) and positive matrix factorization (PMF), while pollution assessment employed the geo-accumulation index (I_geo), Nemerow integrated pollution index (NIPI), and potential ecological risk index (PERI). The results showed that the mean concentrations of all heavy metals exceeded the soil background values for Jilin Province. The enrichment factors for Hg, Pb, and Cu were 3.51, 1.32, and 1.31, respectively, while all metals remained below the risk screening values (GB 15618-2018, China) for agricultural soils. Land use-specific patterns in heavy-metal accumulation were evident. Suburban vegetable fields showed elevated levels of Ni, As, and Cr, paddy fields showed elevated levels of Cd, Hg, and As, and maize fields showed elevated levels of Hg and Pb. Source apportionment revealed that agricultural fertilization, traffic emissions, industrial and coal-combustion activities, and natural sources were the main contributors. Notably, industrial and coal-combustion sources accounted for 77.7% of Hg in maize fields, while agricultural fertilization contributed 67.7% of Cd in suburban vegetable fields. The I_geo results indicated that 65.75% of the sampling sites exhibited slight or higher pollution levels for Hg. However, the NIPI results showed that 97.26% of the sampling sites remained at a safe level (NIPI < 0.7). The PERI results revealed a moderate ecological risk across the study area, with the risk levels following the order: maize fields > paddy fields > vegetable fields. Although agricultural soils generally met the safety standards, Hg-dominated ecological risks warrant priority attention and mitigation measures. Full article

Mangrove sediments in the South China Sea, particularly in the Hainan Island region, play a crucial role in regulating heavy metal migration and sequestration. However, the impact of converting mangrove areas to fish and shrimp culture ponds on heavy metal pollution in the Bamen Bay Mangrove Reserve is unclear. This study evaluates the pollution levels and ecological risks of Cr, Zn, Pb, Cu, and As in sediments from three land-use types using pollution indices (CF, PLI, RI) and the geo-accumulation index (I_geo). Multivariate analysis explores the relationships between metals and their potential sources. The results show significant differences in pollution levels (p < 0.05), with culture ponds having the highest pollution and ecological risk (RI = 73). As is the primary ecological risk factor (Er = 129). Zn and Cr are positively correlated with organic matter, while As and Pb show negative correlations with pH and salinity. Culture ponds increase heavy metal load and ecological risk, adversely impacting the mangrove ecosystem. These findings provide scientific support for land-use management and pollution control in mangrove wetlands. Full article

Building upon a theoretical framework, this study utilized 126 field survey questionnaires from farmers in Shouyang County, Shanxi Province, China, coupled with corresponding farmland soil heavy metal monitoring data, to investigate the extent of heavy metal pollution and its mechanistic relationship with farmers’ behavior. The single-factor pollution index (P_i), Nemerow composite pollution index (P_N), and geographical detector were employed to assess pollution levels and elucidate the underlying mechanisms linking farmer practices to soil heavy metal accumulation. Analysis revealed that the mean concentrations of Cu, Ni, Cr, Pb, Cd, and Zn (25.54, 31.47, 98.50, 16.63, 0.16 and 76.92 mg/kg, respectively) in the farmland soil exceeded the background values for soil elements in Shanxi Province, whereas As (1.92 mg/kg) levels were lower. Assessment using P_i indicated that Cr, Pb, Cd, Ni, Cu, and Zn (1.78, 1.13, 1.55, 1.05, 1.07 and 1.21, respectively) were predominantly in a state of mild pollution. Similarly, the P_N (1.50) suggested an overall mild level of composite heavy metal pollution in the soil. Geographical detector(Geo-Detector) analysis demonstrated that the explanatory power (q-value) of interactions among factors-including agricultural film and fertilizer application intensity, farmland fragmentation degree, per capita annual household income, farmland area, and years engaged in farming-on soil heavy metal accumulation was significantly enhanced compared to that of individual behavioral factors. While individual farmers’ behaviors are associated with heavy metal accumulation, the interaction effects among multiple behaviors constitute the dominant factor influencing localized accumulation in farmland soil. Consequently, local authorities should enhance farmers’ requisite knowledge, skills, and practices for mitigating soil heavy metal accumulation through strategies such as promoting large-scale farming, implementing agricultural input reduction initiatives, and intensifying technical and environmental protection training. The Geo-Detector exhibits significant advantages in identifying nonlinear influencing factors and analyzing factor interactions, yielding more comprehensive insights compared to conventional linear models. Full article

Copper anode slime (CAS), obtained from non-standard anodes by pyro-hydrometallurgical electronic waste (e-waste) processing, contains high concentrations of lead, tin (as metastannic acid), and base (Cu, Fe, Zn), precious (Au, Ag), and technological metals (In, Ga, Ge), which limit the efficiency of conventional valorization methods. In this study, an integrated alkali fusion–leaching process was applied to non-standard CAS. Thermodynamic modeling defined the key parameters for selective phase transformations and efficient metal separation. These parameters were experimentally investigated, and the optimized fusion conditions (CAS:NaOH = 40:60, 600 °C, 60 min), followed by water leaching (200 g/dm³, 80 °C, 60 min, 250 rpm), resulted in >97% Sn removal efficiency. Simultaneously, Au and Ag losses were negligible, resulting in solid residue enrichment. Oxidant addition (NaNO₃) did not improve Sn removal but increased Fe, Pb, and Ag solubility, reducing selectivity. The scaled-up test confirmed process reproducibility, achieving 97.75% Sn dissolution and retention of precious metals in the PbO-based residue (99.99% Au, 99.78% Ag). Application of an integrated thermodynamic modeling, laboratory optimization, and scaled-up validation approach to non-standard CAS provides a relevant framework for a selective, efficient, and scalable method addressing industrial needs driven by increased e-waste co-processing, contributing to sustainable metal recovery. Full article

In order to investigate the current status of soil heavy metal pollution, ecological risk, and risk sources in the black soil area of the Eastern Inner Mongolia Province, topsoil (0–20 cm) samples from farmland in the black soil area (N = 163) were collected to determine the contents of seven heavy metals. The levels of soil heavy metal pollution and ecological risk in the study area were evaluated by combining the geo-accumulation index, potential ecological risk index, and static environmental carrying capacity; the positive matrix factorization (PMF) model was used to identify the pollution sources and contributions of heavy metals in the soil and analyze the risk levels to adults and children. The soil was predominantly weakly acidic, with mean values of Cr, Ni, Cu, As, Cd, Pb, and Zn of 61.77, 26.77, 17.07, 12.11, 0.08, 12.61, and 85.71 mg·kg⁻¹. The mean concentrations of heavy metals exceeded the background values, except for Pb, the mean concentration of which was lower than the soil background. Ni concentrations of 6.21% at the sampling sites exceeded the risk screening value for agricultural soils. The geo-accumulation index showed that Cr (55.15%) and As (54.00%) were mainly mild pollutants; the static environmental carrying capacity indicated that the soils were slightly polluted by Ni, As, and Zn; and the potential ecological risk indices of Cd, Ni, and As were at moderate levels. The PMF model analyzed three pollution sources: mixed agricultural practice–transportation sources (39.46%), mineral-related activity sources (27.01%), and pesticide–fertilizer agricultural practices (33.53%). The human health risk assessment indicated that 46.58% of sampling sites posed a carcinogenic risk to children, with Ni as the main carcinogenic element. In conclusion, the potential contamination of As, Cd, Ni, Cr, and Zn in the Eastern Inner Mongolia farmland black soil area should be further studied. Full article

Establishing robust geochemical baselines in the hyper-arid Atacama Desert remains challenging because of extreme climatic gradients, polymetallic mineralisation, and decades of intensive mining. To disentangle natural lithogeochemical signals from anthropogenic inputs, a region-wide, multi-institutional soil dataset (1404 samples; 32 elements) was compiled. The analytical workflow integrated compositional data analysis (CoDA) with isometric log-ratio transformation (ILR), principal component analysis (PCA), robust principal component analysis (RPCA), and consensus anomaly detection via hierarchical (HC) and spectral clustering (SC), applied both with and without spatial coordinates to capture compositional structure and geographic autocorrelation. Optimal cluster solutions differed among laboratory subsets (k = 2–17), reflecting instrument-specific biases. The dual workflows flagged 76 (geochemical-only) and 83 (geo-spatial) anomalies, of which 33 were jointly identified, yielding high-confidence exclusions. Regional baselines for 13 priority elements were subsequently computed, producing thresholds such as As = 66.9 mg · kg⁻¹, Pb = 53.6 mg · kg⁻¹, and Zn = 166.8 mg · kg⁻¹. Incorporating spatial variables generated more coherent, lithology-aligned clusters without sacrificing sensitivity to geochemical extremes (Jaccard index = 0.26). These findings demonstrate that a reproducible, compositional-aware machine learning workflow can separate overlapping geogenic and anthropogenic signatures in heterogeneous terrains. The resulting baselines provide an operational reference for environmental monitoring in northern Chile and a transferable template for other arid mining locations. Full article

Heavy metals constitute a group of toxic environmental contaminants with complex and varied origins. This study provides a comprehensive framework for deciphering soil heavy metal pollution in rapidly developing regions. The geochemical baselines, pollution levels, and sources of ten heavy metals (V, Cr, Mn, Co, Ni, As, Cd, Pb, Cu, and Zn) were analyzed in topsoil from the industrial–agricultural–transportation hub of Eastern Yongqiao District, Suzhou City, Anhui Province, China. Overall, 48 topsoil samples were analyzed using geochemical baseline determination, the geo-accumulation index (I_geo), the Nemerow comprehensive index, and a multiple linear regression model based on absolute principal component scores (APCS-MLR). The geochemical baseline determination indicates that the elevated mean concentrations of Cr (218.51 mg/kg) and Ni (103.19 mg/kg) are significantly associated with anthropogenic activities. Three samples were identified with moderate-to-strong Cr and Ni pollution by the I_geo method, while all other samples had slight-to-moderate pollution levels. The Nemerow comprehensive index showed heavy metal pollution above the moderate level in five samples. The APCS-MLR model identified four pollution sources for heavy metals: industrial sources (40.5%, dominated by Cr, Co, and Ni), traffic-related sources (23.7%, dominated by V, As, Pb, Cu, and Zn), natural sources (12.6%, dominated by Mn), and agricultural sources (9.4%, dominated by Cd). This research provides a scientific basis for the management of heavy metal pollution derived from industrial production, agricultural activities, and transportation. Full article

The Three Gorges Reservoir, serving as a crucial ecological barrier for the middle-lower Yangtze River basin, faces substantial threats to watershed ecosystems from sediment-associated heavy metal, threatening aquatic ecosystems and human health via bioaccumulation. Leveraging the legislative framework of the Yangtze River Protection Law, this study analyzed sediment cores (0–65 cm) collected from 12 representative sites in the Three Gorges Reservoir using 2020 Air–Space–Ground integrated monitoring data from the Changjiang Water Resources Commission. Concentrations of nine heavy metals (Cd, Cu, Pb, Fe, Mn, Cr, As, Hg, and Zn) were quantified to characterize spatial and vertical distribution patterns. Source apportionment was conducted through correlation analysis and principal component analysis (PCA). Contamination severity and ecological risks were assessed via geo-accumulation index (I_geo), potential ecological risk index (RI), and acute toxicity metrics. The findings indicated substantial spatial heterogeneity in sediment heavy-metal concentrations, with the coefficients of variation (CV) for Hg and Cd reaching 214.46% and 116.76%, respectively. Cu and Pb showed surface enrichment, while Cd exhibited distinct vertical accumulation. Source apportionment indicated geogenic dominance for most metals, with anthropogenic contributions specifically linked to Cd and Hg enrichment. Among the metals assessed, Cd emerged as the primary ecological risk driver, with localized strong risk levels (E_i > 320), particularly at FP and SS sites. These findings establish a scientific foundation for precision pollution control and ecological restoration strategies targeting reservoir sediments. Full article

This study focused on the Wugong coal fire area in the Zhunnan coalfield of Xinjiang, analyzing 41 soil samples extending from the fire center outward. The key parameters included pH, soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), available potassium (AK), various ions (Ca²⁺, Na⁺, Mg²⁺, SO₄²⁻, CO₃²⁻, HCO₃⁻, and Cl⁻), and heavy metal concentrations (As, Cr, Hg, Ni, Cd, Cu, Zn, and Pb). The primary objectives were to evaluate heavy metal pollution levels and potential ecological risks using the single factor pollution index (P_i), the Geo-accumulation index (I_Geo), Nemero’s pollution index (P_n), the pollution load index (PLI), and the ecological risk factor (E_rⁱ) and risk index (RI). Spatial distribution analysis indicated higher heavy metal concentrations in the southwestern and central regions. The heavy metals Cr, Ni, Cd, Cu, and Zn reached mild pollution levels, while Hg exhibited high pollution, with P_i, I_Geo, and P_n values of 3.27, 0.61, and 9.68, respectively. Hg (E_rⁱ = 111.07) and Cd (E_rⁱ = 45.91) emerged as the primary ecological risk factors. The overall ecological risk index (RI) of 184.98 indicated a moderate ecological risk. The results demonstrate that soils surrounding the coal fire zone are significantly impacted by coal fire, characterized by severe heavy metal contamination and nutrient deficiency. Full article

Developing detectors to enhance the timing resolution of positron emission tomography scanners can help reduce radioactive doses absorbed by patients and improve spatial resolution in medical imaging. Time resolution may be enhanced in heterostructures comprising a heavy scintillator for attenuation of 511 keV γ-quanta, as well as a fast scintillator converting recoiled electrons from the heavy scintillator to prompt light photons. In this study, ZnO films as fast scintillators with different thicknesses were obtained on substrates of a heavy bismuth germanate (Bi₄Ge₃O₁₂, BGO) scintillator using several film preparation techniques, such as spray-coating, drop-casting, and spin-coating. The design of heterostructures combined the key advantage of a low-cost film preparation technique with environmentally friendly and available precursors. This work proposes synthesis methods of highly nanocrystalline ZnO films on BGO, where a film thickness ranges from 6 to 18 μm. All ZnO studied films exhibit exciton luminescence peaked in UV (353 nm) and defect luminescence in the green (657 nm) range under 325 nm excitation. The best coincidence time resolution of 158 ± 8 ps was obtained with BGO@ZnO heterostructures fabricated by the spray-coating. The proposed approach allowed obtaining BGO@ZnO heterostructures for potential use as ultrafast scintillation detectors. Full article

Mining activities caused heavy metal enrichment in mine soils. Sixty-six soil samplings of 26 mines in the central Tianshan Mountains of China were conducted to reveal heavy metal pollution for the single-factor (P_i), Nemerow comprehensive pollution (P_N), geo-accumulation (I_geo), potential ecological risk (E_i), and health risks. The results indicate that mines in Bayingolin and Aksu exhibit the most severe pollution (P_N = 26.64 and 25.28), characterized by Cd (P_i = 115.18) and As (P_i = 67.20), forming a Cd-As compound pattern. While Ili mines show Ni-Cu co-exceedance, and Turpan mines have lower overall pollution but localized Cd enrichment. Additionally, Cd is identified as the most severe by I_geo, with moderate or higher pollution levels observed in 61.00% of samplings. The E_i assessment revealed that Cd posed the greatest threat, with 100%, 53.80%, and 30.70% of samplings indicating slight, high, and extremely high ecological risk levels, respectively. Health risk assessment indicated that non-carcinogenic risks were dominated by Cr (affecting 19.20% of samplings), while carcinogenic risks were primarily from As (7.70%) and Cd (11.50% of samplings), with Cr exhibiting the highest carcinogenic risk. Furthermore, comparative analysis showed that underground mines led to higher pollution levels (I_geo) for Cd, Cu, Mn, Pb, and Zn compared to open-pit mines, and metal mines incurred greater heavy metal(loid) contamination than non-metal mines. These findings could provide data for mine soil pollution remediation in the central Tianshan Mountains. Full article

Acid mine drainage (AMD) poses significant environmental and health risks due to its high acidity and elevated metal and sulfate contents. Previous studies have primarily focused on short-term AMD monitoring, with limited attention paid to long-term, spatially resolved datasets and predictive modeling. In this 3.5-year study, six wells down-stream of a mine waste rock pile were monitored, and 132 sets of associated water quality (AWQ), geological (GEO), and climate history (CH) parameters were compiled to develop predictive models for Fe, Cu, and Zn concentrations. Random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) algorithms were applied using different combinations of input variables. The combined AWQ-GEO-CH dataset achieved the best overall performance, with XGBoost yielding the highest R² values for Fe (0.81) and Cu (0.77), and SVM performing best for Zn (0.94). CH variables, particularly precipitation and evaporation over 60-day periods, strongly influenced metal concentrations by driving hydrological and solute redistribution processes. AWQ parameters, especially F⁻ and S²⁻, were key predictors for Fe and Zn and ranked second for Cu, likely due to shared upstream sources and coupled geochemical processes such as FeF₃ dissolution. The most impactful GEO factor was the installation of a vertical barrier, which reduced metal concentrations by 73–80%. These findings highlight the value of integrating multi-source datasets with ML for long-term AMD prediction and management. Full article

Exploring the pollution characteristics and ecological risks of urbanization on lakes in urban fringe areas has guiding significance for the control and scientific management of heavy metal pollution in lakes in urban fringe areas. Taking the West Lake in Kaifeng city as an example, the samples of the sediments and surface water of the lake were collected, and the contents of heavy metals (As, Cd, Cr, Cu, Ni, Pb, and Zn) were measured, assessing the degree and ecological risk of heavy metal pollution using the Geo-Accumulation Index (I_geo) and Potential Ecological Risk Index methods (RI); and the sources of pollution were identified. The results show that the heavy metal concentrations in the surface water of the West Lake in Kaifeng city are generally low; average concentrations of Cd, Cu, Zn, Cr, Ni, Pb, and As in sediments are 3.120, 1.810, 1.700, 1.540, 1.000, 0.990, and 0.430 times higher than the background value of fluvo-aquic soil, respectively. The sequence of the average I_geo from high to low is Cd (1.020) > Cu (0.220) > Zn (0.160) > Cr (0.000) > Pb (−0.610) > Ni (−0.640) > As (−1.850). Among them, contaminations with Pb are classed as moderately polluted; As pollution is relatively light, while other heavy metals are unpolluted. The average Potential Ecological Risk Coefficient (E) values for seven heavy metals are Cd (93.500) > Cu (9.040) > Ni (4.990) > Pb (4.950) > As (4.290) > Cr (3.080) > Zn (1.700). Cd is at a considerable potential ecological risk, while other heavy metals are at low ecological risks. Heavy metal pollution in sediment of West Lake in Kaifeng mainly comes from traffic activities such as yacht machinery wear and gasoline burning. The research findings provide a scientific foundation for developing effective mitigation strategies against heavy metal contamination in peri-urban lacustrine ecosystems. Full article

Municipal sewage sludge is a potential soil amendment rich in organic matter and nutrients, yet its reuse is often constrained by heavy metal contamination. This study evaluated six heavy metals (Cd, Cr, Cu, Ni, Pb, and Zn) in sludge collected from seven centralized wastewater treatment plants in Bangkok, Thailand, by analyzing physicochemical properties, total metal concentrations, and chemical speciation. Three ecological risk indices, the geo-accumulation index (I_geo), risk assessment code (RAC), and potential ecological risk index (PERI), were applied to assess contamination status, mobility, and ecological threat. The sludge exhibited high levels of organic matter and essential nutrients, indicating potential for agricultural reuse; however, elevated electrical conductivity at some sites may pose salinity risks if unmanaged. Speciation analysis revealed that Cd and Zn were largely present in mobile and redox-sensitive fractions, Cr and Pb were primarily in stable residual forms, and Cu and Ni occurred in moderately mobile forms influenced by environmental conditions. Across all indices, Cd consistently posed the highest ecological risk, followed by Zn, in a site-dependent manner, while Cr and Pb represented low risk. These findings provide a clearer understanding of metal behavior in sewage sludge and underscore the importance of integrating chemical speciation with multi-index risk assessment in sludge management. Incorporating such approaches into national guidelines, particularly in countries lacking established heavy metal limits, can strengthen monitoring frameworks, guide safe and sustainable reuse, and support regulatory development in contexts with limited monitoring data. Full article