Search Results (1,830)

Against the challenge of extreme lead (Pb) contamination (>15,000 ppm) in industrial polluted soils, where conventional agents fail to disrupt stable Pb–soil complexes—this study extends our prior cadmium (Cd) remediation research to validate amino acid ionic liquids (AAILs) for highly recalcitrant metals. Fifteen AAILs were screened via batch washing, with [Met][NO₃] (methionine-based) demonstrating the highest Pb removal efficiency. Single-factor optimization revealed that under the conditions of 0.8 mol/L, 6:1 liquid–soil ratio, 60 min, 85.4% Pb was removed from severely contaminated soil by [Met][NO₃]. Kinetic analysis using four common models showed that the second-order kinetic equation provided the best fit, indicating that Pb removal was predominantly driven by chemical reactions such as complexation or ion exchange. After washing, the contents of various Pb species were significantly reduced, thereby mitigating environmental risks. Notably, no substantial changes in soil texture were observed. However, a marked increase in organic matter content was detected, accompanied by decreases in soil pH and mineral element concentrations. Analysis of soil mineral composition, functional groups, and chemical speciation revealed that [Met][NO₃] primarily facilitated Pb removal through ion-exchange and coordination reactions. This study establishes [Met][NO₃] as a green agent with dual efficacy: it achieves high-efficiency remediation of severely Pb-contaminated soil while ensuring environmental sustainability, thus highlighting its potential for practical application. Full article

The aim of this study was to explore the decomposition characteristics of Tamarix chinensis litter and its soil-improving capacity under different salinities. Four treatments were designed: a control (CK) treatment without saline water injection and three treatments encompassing slightly (SS, 0.4% soil salinity), moderately (SM, 0.8%), and highly saline (SH, 1.2%) conditions. T. chinensis litter at three degrees of decomposition (undecomposed, semidecomposed, and already decomposed) was studied. After 180 days, the litter substrate quality, 0–10 cm soil physicochemical properties, and enzyme activities were measured. Correlation analysis and structural equation modeling were employed to elucidate the interactions and response patterns among soil salinity, the decomposition characteristics of T. chinensis litter, and the physicochemical properties and enzyme activities of surface soil. The results revealed the following: (1) With increasing soil salinity, the contents of litter lignin, cellulose, total carbon and nitrogen residues first decreased but then increased, reaching minima under SM, whereas the content of hemicellulose residue exhibited the opposite trend. With increasing degree of litter decomposition, the contents of lignin and cellulose residues decreased, whereas the contents of hemicellulose, total nitrogen and phosphorus residues increased. (2) With increasing soil salinity, the soil water content, organic matter content, total nitrogen content, and activity of several enzymes increased, peaking under SH. The pH performance followed the order of SS > SM > CK > SH. The total carbon and phosphorus contents first increased but then decreased, with a maximum under SS. The activity of N-acetylamino glucosidase first decreased but then increased and was greatest at moderate and high salinities. (3) The soil water content and level of enzyme activity were significantly correlated with the litter substrate quality. Salinity negatively affected litter substrate residues but positively affected soil physicochemical properties. Litter decomposition under different soil salinities indirectly influenced soil enzymes by affecting soil properties, whereas salinity modulated soil properties directly or through litter decomposition. T. chinensis litter decomposition notably increased enzyme activity in moderate- to high-salinity alkali coastal soils, offering insights for low-efficiency T. chinensis forest management and saline–alkali soil remediation in the Yellow River Delta. Full article

Decreasing summer precipitation is negatively affecting global productivity of grassland plant species. This study evaluated the effect of three levels of soil plant available water [80–90% PAW-H (high), 50–60% PAW-M (medium), and 20–30% PAW-L (low), which were soil water restriction (SWR) equivalent to (v/v%) 10–20%, 40–50%, and 70–80%, respectively] and nitrogen (N 0 and 110 kg ha⁻¹) on growth and nutritional quality of Bromus valdivianus Phil. (Bv) mini-swards (MS; 125 L containers), arranged in three blocks. Total lamina length (TLL), leaf expansion rate (LER; cm d⁻¹), phyllochron (Phy) expressed as “days” and “°C day”, tiller mass (TM, g tiller⁻¹), number of live leaves (NLL), number of dead leaves (NDL), and accumulated herbage mass [AHM, g DM (dry mass) m²] were measured. Defoliation events, leaving 5 cm residual height, were carried out every 320 GDD (using a base growth temperature of 5 °C), and foliage samples for nutritive quality [DM, crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF), water-soluble carbohydrates (WSC), and metabolic energy (ME)] were collected. Reducing PAW to 20–30% decreased the AHM by 60.7%, TLL by 52.7%, LER by 50%, and TM by 50%, with significant interaction between the main effects for AHM, TLL, and LER. The addition of N increased the AHM by 31.6%, LER by 21.6%, and TLL by 19.6%. The Phy remained undisturbed by decreasing PAW and increasing the N rate. Nutritive quality was generally not statistically different for the interaction or between N levels. However, low PAW levels resulted in statistically (p < 0.05) lower ME and higher concentrations of NDF. In general, growth, AHM, and nutritional quality of Bv during the summer period were driven by PAW levels and by the availability of N. Plant available water levels of 50% to 60% at 20 cm soil depth, with the addition of N, allowed Bv to reach its highest production. Full article

The interaction between microplastics (MPs) and heavy metals and their ecological risks to the soil–plant system has attracted widespread attention. This study explored the effects of polypropylene (PP) alone or combined with cadmium (Cd) pollution on wheat seed germination, plant growth, and the soil environment from multiple perspectives through seed germination experiments and pot experiments. The results of the seed germination experiment showed that the addition of 50 mg L⁻¹ PP could promote the growth of seeds. However, medium and high concentrations of PP had significant inhibitory effects on seeds. For PP + Cd co-pollution, the addition of 50 mg L⁻¹ PP could partially alleviate the stress of Cd alone. However, with the increase in PP concentration, the co-pollution showed stronger toxicity to seeds. Moreover, the synergistic effect of PP and Cd was greater than the antagonistic effect; both of them aggravated the stress on wheat. The results of the pot experiment showed that the soil microenvironment was significantly affected by PP alone or combined with Cd pollution. It was manifested as reducing soil moisture and pH, affecting soil nutrient cycling, and inhibiting the activities of soil enzymes (except for catalase). In addition, the MPs and Cd significantly affected the physiological characteristics of plants. Specifically, the addition of 50 mg L⁻¹ PP alone promoted or had no significant effect on wheat growth. However, with the increase in PP concentration, the biomass and chlorophyll content of plants decreased significantly, while carotenoids, oxidative damage, and antioxidant enzyme activities increased significantly. Moreover, PP + Cd co-pollution led to stronger phytotoxicity. Moreover, PP exposure caused an increase in plant shoot and root Cd concentrations, promoting Cd transport from roots to shoots. Correlation heat maps and RDA analysis revealed that plant Cd concentration was significantly correlated with soil environmental factors and plant physiological indicators. Finally, the results of the linear model (%) of relative importance indicated that pH and MDA content were important soil and plant variables affecting the increase in Cd concentration in plant tissues. This study is of great significance for evaluating the ecological risks of MPs-Cd composite pollution. Full article

The accurate assessment of soil fertility is critical for guiding nutrient management and promoting sustainable agriculture in semi-arid agroecosystems. In this study, a machine learning-based Soil Fertility Index (SFI) model was developed using regularized regression techniques to evaluate fertility across a dryland maize-growing region in southeastern Türkiye. A total of 64 composite soil samples were collected from the Batman Plain, characterized by alkaline and salinity-prone conditions. Five soil chemical indicators, electrical conductivity (EC), pH, organic matter (OM), zinc (Zn), and iron (Fe), were selected for SFI estimation using a standardized rating approach. The dataset was randomly split into training (80%) and test (20%) subsets to calibrate and validate the models. Ridge, Lasso, and Elastic Net regression models were employed to predict SFI and assess variable importance. Among these, the Lasso model achieved the highest predictive accuracy on test data (R² = 0.746, RMSE = 0.060), retaining only EC and Zn as significant predictors. Ridge and Elastic Net captured OM and pH, though their contributions were minimal (|β| < 0.01). Spatial predictions showed moderate alignment with observed SFI values (range: 0.48–0.76), but all models underestimated high-fertility zones (>0.69), likely due to coefficient shrinkage. Despite its simplicity, the Lasso model offered superior interpretability and spatial resolution. The results reveal the potential of interpretable machine learning for supporting sustainable, site-specific fertility assessment and informed nutrient management in data-scarce and environmentally vulnerable regions. Full article

The development and use of urban spaces for food production is increasing in response to the search for healthier foods and contact with nature. These spaces can be created or built on materials of various types, which might contain potentially toxic elements (PTEs). This study focuses on the evaluation of soil fertility and contamination levels in urban and rural kitchen gardens in Lisbon, Portugal. Soils of twenty kitchen gardens (n_urban = 15; n_rural = 5) were sampled, and their physicochemical characteristics and the contents of PTEs in the total and available fractions were analyzed. The results were used to calculate contamination indices and associated ecological risk. The soils of the urban and rural kitchen gardens had a neutral pH, with the presence of carbonate forms, and moderate-to-high organic matter contents, although with a clear nutritional imbalance. Regarding PTEs, both urban and rural kitchen gardens soils showed elevated levels of certain elements (e.g., Cr, Ni, Cu), exceeding the maximum allowable values established by Portuguese regulations. However, the available fraction of these elements was generally low. Contamination indices ranged from mild to considerable in isolated cases, with no general multi-element contamination or ecological risk. This suggests that associated environmental and health risks are minimal, although periodic monitoring of kitchen gardens’ soil quality is necessary to ensure and maximize the health benefits. Full article

As a rare edible mushroom, Dictyophora rubrovolvata possesses remarkable anti-tumor, anti-inflammatory, and antioxidant properties. However, continuous-cropping obstacles during cultivation significantly reduce soil reuse efficiency and adversely affect yield. To reveal a potential mechanism of continuous-cropping soil obstacles and propose some green precision cultivation strategies, the soil samples throughout the five growth stages of D. rubrovolvata were collected and systematically analyzed, including soil nutrient contents, pH, and dynamic changes in soil microbial communities. The results showed that soil organic carbon consumption was relatively high during the whole growth cycle. The total nitrogen consumption was greater during mycelial and primordium stages. The total phosphorus content began to exceed control from the egg stage. The total potassium and pH levels were both higher than control, exhibiting an upward trend. The bacterial species in the soil gradually increased with the growth of fruiting bodies, while the fungal species showed a declining trend. Moreover, there were significant differences in dominant bacteria and fungi in the soil during different growth stages. Further analysis revealed a dynamic coupling relationship among the soil microbial community, soil nutrient content, and pH during whole life cycle. This research would provide theoretical and technical support for the sustainable development of the edible mushroom industry. Full article

Plant Growth Promoting Rhizobacteria, PGPR, can protect plants against soil-borne diseases and abiotic stress conditions. The primary objective of this study was to evaluate the effects of different PGPRs (TF1, TF2, TF3, and TF4) on the rhizosphere microbial community of silage maize in a saline–alkaline field via Illumina MiSeq high-throughput sequencing technology. Results demonstrated that different PGPRs significantly increased the harvest density (by 21.31–45.16%), plant height (by 9.12–19.98%), stem diameter (by 30.07–45.78%), and biomass (by 33.20–65.36%) of silage maize, TF3 treatment significantly increased the fresh weight (by 32.50%), while the other treatments could increase the fresh weight but not significantly. Four microbial agents significantly reduced the contents of soil available phosphorus (AP), electrical conductivity (EC), and neutral phosphatase activity (NPA), while significantly increasing the contents of available potassium (AK), ammonium nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), chitinase activity (ChtA), and urease activity (UA). Specifically, TF2 and TF3 treatments significantly decreased the soil pH value, while not for TF1 and TF4. Microbiome analysis showed that four microbial agents significantly increased the relative abundances of beneficial microorganisms, such as Arthrobacter, Blastococcus, MNDI, Chaetomidium, Alternaria, Sarocladium, Acremonium, and Clonostachys, and significantly decreased the relative abundances of Gibberella and Fusarium. Mental analysis showed that the soil bacterial community structure did not significantly correlate with soil biochemical properties, while the soil fungal community structure significantly and positively correlated with pH. Maize yield significantly and positively correlated with NH₄⁺-N, OM, AP, EC, UA, ChtA, and NPA. Full article

(1) Background: Tolypocladium spp. are fungi known for producing cyclosporin A and their ability to infect insects. However, their pathogenicity against the lepidopteran pest Plutella xylostella has not been previously reported. (2) Methods: Four Tolypocladium strains were isolated from soil and identified through morphological and phylogenetic analyses (ITS, gene sequencing). Growth rates, sporulation capacity, and stress tolerance (45 °C heat, UV) were evaluated. Pathogenicity was assessed via larval bioassays, and immune responses were analyzed by quantifying Toll pathway gene expression and enzyme activities (PO, CAT, POD, GSTs, CarE, AChE) from 24 to 96 h post-inoculation. (3) Results: Strains N8-SF-04092 and O1/O2/O3-SF-04630/04927/04931 were identified as Tolypocladium cylindrosporum and Tolypocladium inflatum, respectively. Strain O2 showed the highest growth rate (p < 0.05), while O3 and N8 exhibited superior sporulation (>7 × 10⁵ spores/mm²). N8 also demonstrated notable thermotolerance. In pathogenicity assays, O1, O3, and N8 caused 98.3%, 93.3%, and 96.7% larval mortality, respectively, with LT₅₀ values (3.89–4.45 days) significantly lower than O2 (p < 0.05). Immune gene expression in P. xylostella was transiently activated at 24 h but suppressed from 48 to 96 h by N8 (p < 0.05), while O1 induced partial activation at 24 h and 96 h but suppression at 48 h and 72 h. Protective enzymes (PO, CAT) were initially upregulated (24–48 h) but inhibited after 72 h (p < 0.01). POD activity showed opposing trends between O1 (initially activated then suppressed) and N8 (initially suppressed then activated). Detoxification enzymes (GSTs, CarE, AchE) were predominantly suppressed, except for GSTs, which increased at 72–96 h. (4) Conclusions: Strains O1 and N8 exhibit high virulence against P. xylostella by disrupting immune responses through dynamic modulation of Toll pathway genes and enzyme activities. The thermotolerance of strain N8 further enhances its promising biocontrol agent for field application. Full article

Hemp stalk, a widely available agricultural waste, is an ideal eco-friendly raw material for biochar production. Carbonization experiments were conducted as a novel approach for the scalable and value-added utilization of hemp stalk under oxygen-exclusion conditions. The effects of feedstock types—Hibiscus cannabinus (KS), Corchorus spp. (JS), and Boehmeria spp. (RS)—and pyrolysis temperatures on biochar properties were analyzed through the measurements of X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy. The pH and electrical conductivity (EC) of biochars increased with increasing pyrolysis temperature. Notably, EC was significantly higher for RS (940–2278 μS/cm) than for KS (517–879 μS/cm) and JS (583–863 μS/cm). The C content in these three biochars increased as the temperature increased, whereas the H/C atomic ratio decreased, most notably in JS (by 0.33%). According to FTIR and XRD data, with the pyrolysis temperature increasing, the acidic oxygen-containing groups on biochar surfaces reduced. KS700, with superior aromatic structure and stability, may be able to effectively adsorb heavy metal ions. RS700, with relatively high pH and EC, was suitable for alleviating soil acidification and nutrient deficiency. The feedstock and pyrolysis temperature significantly affected the element content, pore structure, and stability of biochars derived from hemp stalk. Full article

The presence of understory vegetation not only influences slope-scale soil and water conservation but also exerts a profound effect on hydrodynamic characteristics and the processes of runoff and sediment production. Therefore, in this study, different vegetation types and vegetation coverages (bare land, 30%, 60%, and 90%) were set up by simulating rainfall (45, 60, 90, and 120 mm·h⁻¹) to evaluate the runoff-sediment process and the response characteristics of hydrodynamic parameters. The results showed that increasing vegetation cover significantly reduced soil erosion on forest slopes (p < 0.05). When the vegetation cover ranged from 60% to 90%, vegetation pattern C and pattern D were the most effective in suppressing erosion, where increased cover improved runoff stability. Under low-cover conditions, overland flow tended toward turbulent and rapid regimes, whereas under high cover conditions, flow was primarily laminar and slow. Patterns C and D significantly reduced flow velocity and water depth (p < 0.05). Structural equation patterning revealed that, under different vegetation patterns, the runoff power (ω), Reynolds number (Re), and resistance coefficient (f) more effectively characterized the erosion process. Among these, the Reynolds number and runoff power were the dominant factors driving erosion on red soil slopes. By contrast, runoff shear stress was significantly reduced under high-cover conditions and showed weak correlation with sediment yield, suggesting that it was unsuitable as an indicator of slope erosion. Segmental vegetation arrangements and increasing vegetation cover near runoff outlets—especially at 60–90% coverage—effectively reduced soil erosion. These findings provide scientific insight into the hydrodynamic mechanisms of vegetation cover on slopes and offer theoretical support for optimizing soil and water conservation strategies on hilly terrain. Full article

Accurate and spatially detailed soil information is essential for supporting sustainable land use planning, particularly in data-scarce regions such as Cyprus, where soil degradation risks are intensified by land fragmentation, water scarcity, and climate change pressure. This study aimed to generate national-scale predictive maps of key soil health descriptors by integrating satellite-based indicators with a recently released geo-referenced soil dataset. A machine learning model was applied to estimate a suite of soil properties, including organic carbon, pH, texture fractions, macronutrients, and electrical conductivity. The resulting maps reflect spatial patterns consistent with previous studies focused on Cyprus and provide high resolution insights into degradation processes, such as organic carbon loss, and salinization risk. These outputs provide added value for identifying priority zones for soil conservation and evidence-based land management planning. While predictive uncertainty is greater in areas lacking ground reference data, particularly in the northeastern part of the island, the modeling framework demonstrates strong potential for a national-scale soil health assessment. The outcomes are directly relevant to ongoing soil policy developments, including the forthcoming Soil Monitoring Law, and provide spatial prediction models and indicator maps that support the assessment and mitigation of soil degradation. Full article

Cocoa represents a crucial source of income in coastal regions of Ecuador, where the product is exported for the production of high-value chocolates. However, elevated levels of cadmium (Cd) in cocoa beans, attributable to volcanic soils, have the potential to impede international trade, particularly in accordance with European Union regulations. The main objective of this study was to reduce Cd concentrations in cocoa beans of two genotypes, Nacional and CCN-51, by applying different doses of pectin trans-eliminase (PTE) enzyme during the fermentation process in conjunction with mucilage washing techniques, pre-drying resting periods, and various drying methods. To this end, a Taguchi orthogonal design (L9) was employed to evaluate nine treatments per genotype, complemented with two controls. The most efficacious treatment for Nacional was identified as T7, involving a 0.30 mL·kg⁻¹ PTE dose, the absence of mucilage washing, a 48 h resting period, and drying in a marquee. This treatment resulted in a 68.6% reduction in Cd concentration (from 0.28 to 0.09 mg·kg⁻¹). For CCN-51, T3 (0.10 mL·kg⁻¹ PTE, complete washing, 48 h resting, and splint drying) yielded a 26.4% reduction in Cd (from 0.42 to 0.31 mg·kg⁻¹). It is noteworthy that none of the treatments exceeded the EU regulatory threshold of 0.8 mg·kg⁻¹. A physico-chemical analysis was conducted, which revealed significant treatment effects on pH (ranging from 5.63 to 6.85) and acidity (0.02% to 0.03%). Sensory evaluation indicated enhancements in cocoa and nutty flavors, along with a reduction in undesirable astringency and bitterness, particularly in Nacional samples. The findings of this study demonstrate that the combination of enzyme-assisted fermentation and optimized postharvest techniques represents a pragmatic approach to the mitigation of cadmium in cocoa, while simultaneously preserving or enhancing product quality. Full article

In order to precisely improve the quality of major tree species in northern China, near-natural differentiated management has been gradually introduced into forestry practice, aiming to optimize forest structure, enhance forest quality, and promote nutrient cycling and water conservation. As an essential element of forest ecosystems, soil microbes contribute to biodiversity preservation and nutrient turnover in soils. This study selected three typical forest types (Quercus acutissima forest, Pinus tabulaeformis forest, and Pinus tabulaeformis × Quercus mixed forest) that have been managed with target trees on Zhongtiao Mountain. Using 16S/ITS rRNA high-throughput sequencing, this study systematically assessed the influences of forest type and soil depth (0–60 cm) on the soil properties and microbial communities. The results showed that the fungal alpha diversity indices were the highest in Pinus tabulaeformis forest, which decreased with soil depth. Actinobacteriota exhibited the greatest relative abundance in mixed forest, whereas Ascomycota predominated in the Pinus tabulaeformis forest. The microbial co-occurrence network exhibited greater complexity compared to the pure forest. Microbial carbon and nitrogen cycling functions showed strong correlation with soil pH and nutrient levels. Symbiotrophs dominated the fungal community, and ectomycorrhizae were significantly abundant in mixed forests. pH is the dominant factor driving changes in microbial communities. In summary, the mixed forest improved soil nutrients, enhanced the complexity of microbial networks, and supported higher ectomycorrhizal abundance. These findings provide practical guidance for improving soil health and stability of forest ecosystems through near-natural management. Full article

Net ecosystem productivity (NEP) serves as a key indicator for assessing regional carbon sink potential, with its dynamics regulated by nonlinear interactions among multiple factors. However, its driving factors and their coupling processes remain insufficiently characterized. This study investigated terrestrial ecosystems in Yunnan Province, China, to elucidate the drivers of NEP using 14 environmental factors (including topography, meteorology, soil texture, and human activities) and 21 remote sensing features. We developed a research framework based on “Feature Selection–Machine Learning–Mechanism Interpretation.” The results demonstrated that the Variable Selection Using Random Forests (VSURF) feature selection method effectively reduced model complexity. The selected features achieved high estimation accuracy across three machine learning models, with the eXtreme Gradient Boosting Regression (XGBR) model performing optimally (R² = 0.94, RMSE = 76.82 gC/(m²·a), MAE = 55.11 gC/(m²·a)). Interpretation analysis using the SHAP (SHapley Additive exPlanations) method revealed the following: (1) The Enhanced Vegetation Index (EVI), soil pH, solar radiation, air temperature, clay content, precipitation, sand content, and vegetation type were the primary drivers of NEP in Yunnan. Notably, EVI’s importance exceeded that of other factors by approximately 3 to 10 times. (2) Significant interactions existed between soil texture and temperature: Under low-temperature conditions (−5 °C to 12.15 °C), moderate clay content (13–25%) combined with high sand content (40–55%) suppressed NEP. Conversely, within the medium to high temperature range (5 °C to 23.79 °C), high clay content (25–40%) coupled with low sand content (25–43%) enhanced NEP. These findings elucidate the complex driving mechanisms of NEP in subtropical ecosystems, confirming the dominant role of EVI in carbon sequestration and revealing nonlinear regulatory patterns in soil–temperature interactions. This study provides not only a robust “Feature Selection–Machine Learning–Mechanism Interpretation” modeling framework for assessing carbon budgets in mountainous regions but also a scientific basis for formulating regional carbon management policies. Full article