Search Results (1,914)

Petroleum contamination of soils remains a significant environmental problem in boreal regions, where low temperatures constrain natural attenuation processes and complicate bioremediation. Nitrogen biostimulation is widely used to enhance petroleum hydrocarbon degradation; however, the combined effects of temperature regime, nitrogen form, contamination level, and nitrogen dosage remain insufficiently resolved for sandy podzolic soils of northern regions. This study investigated nitrogen-assisted biostimulation of petroleum-contaminated sandy podzolic soil collected in the Khanty–Mansi Autonomous Okrug (Western Siberia, Russia) using a factorial experimental design. Soil samples were artificially contaminated with crude oil at concentrations of 25, 50, and 100 g kg⁻¹ and incubated under warm and cold temperature regimes. Two nitrogen sources, urea and ammonium nitrate, were applied at several dosages. Changes in residual petroleum hydrocarbon content were monitored together with the abundance of culturable microorganisms under the applied cultivation conditions at the intermediate contamination level on day 60. Nitrogen supplementation enhanced petroleum hydrocarbon removal relative to the untreated control, but the magnitude of the effect depended substantially on temperature, nitrogen form, and contamination level. Under the tested conditions, ammonium nitrate was generally associated with stronger hydrocarbon removal than urea, particularly at the intermediate contamination level (50 g kg⁻¹). The results indicate that the response to nitrogen biostimulation in sandy boreal soils is controlled by interacting experimental factors rather than by nitrogen addition alone. These findings improve the positioning of nutrient-assisted remediation in cold-region soils and provide a basis for future mechanistic and field-scale studies. Full article

In recent years, bacteria-based self-healing has emerged as a promising bioengineering strategy to address the self-repair of cracks in cement-based materials, which represent one of the persistent durability challenges. This approach relies on microbiologically induced calcium carbonate (CaCO₃) precipitation (MICP), in which metabolically active bacteria promote CaCO₃ formation of crystals that can heal cracks and restore material integrity. This study compares the self-healing potential of a natural (N-) alkaline soil Bacillus licheniformis strain with a UV-strain (phenotypic mutant) generated through controlled UV exposure followed by adaptive evolution. Both strains were evaluated under conditions relevant to cementitious environments. The UV-strain exhibited enhanced ureolytic performance, reaching urease activity of 0.32 U/mg compared to 0.24 U/mg in the N-strain. This translated into improved biomineralization, with CaCO₃ precipitation reaching 2.37 mg versus 2.23 mg/100 mL in the N-strain. Additionally, the UV-strain showed increased cell hydrophobicity and aggregation, indicating improved nucleation potential and surface-mediated mineral deposition. Multivariate analysis confirmed strong correlations between ureolytic metabolism, alkalization, and mineral formation, while artificial neural network (ANN) modeling (MLP 6-10-14) successfully predicted biomineralization-related parameters with high accuracy (R² > 0.90 for urease activity, NH₄⁺, ΔpH, and CaCO₃). The results demonstrate that UV-induced phenotypic adaptation can enhance biomineralization efficiency with minor trade-offs in physiological robustness. For the first time, that controlled UV-induced phenotypic adaptation can be used as a targeted strategy to enhance biomineralization efficiency in B. licheniformis, while maintaining functional stability under cement-relevant conditions. These findings provide a novel framework for tailoring bacterial performance in self-healing systems for construction biotechnology. Full article

In the construction industry, it is necessary to know the soil type and its water content to ensure compliance with required specifications. Existing solutions involve expensive equipment and require significant time to deliver reliable results. This article focuses on the application of modular neural networks to automate the analysis of measurement data for five soil types. The data analyzed were obtained using resistive and capacitive sensors, as well as the bulk volume mass of materials. A modular architecture consisting of 14 neural networks was designed. One sequential network specialized in material classification with an Adam optimizer. The other 13 neural networks were trained using evolutionary strategies by material type and water content range. The results show that modular architecture improves response time and reliability for individual network models, achieving an accuracy of 94.69%. The modular system was validated using 20% of the database and 10-fold cross-validation. For water content determination, the accuracy in the material with the highest variability was −0.1770% with a standard deviation of 0.6239%. The use of this modular system reduces operating and analysis times in material classification and water content determination through its real-time application. It validates its use in soil analysis processes for construction and can be used in educational settings. Full article

The prized wild mushroom Tricholoma matsutake maintains distinctive microbiota within its dominant zone; however, the spatial and seasonal reorganization of microbiota from taxonomic and functional perspectives remain poorly understood. High-throughput amplicon sequencing was performed in warming (March–June) and cooling (September–December) seasons to compare microbial communities among T. matsutake-dominant (present, visible mycelium) and -nondominant soils (transition, adjacent with present; control, distant from fairy ring). Fungal and bacterial community structures in T. matsutake-dominant soils were obviously distinct (ANOSIM, R > 0.6, p = 0.001), and bacterial communities exhibited clear seasonal separation. The relative abundances of Ascomycota and Mortierellomycota significantly reduced, whereas mycorrhiza-helper bacteria, including Paenibacillus, Bacillus, and Cohnella, were enriched. Functional predictions suggested that the potential expression of cofactor and vitamin biosynthesis, nutrient degradation, and inorganic nutrient metabolism pathways may be enriched in T. matsutake-dominant soil. During the fruiting period, the expression of the predicted amino acid biosynthesis pathway may be reduced, whereas that of the cofactor/carrier/vitamin biosynthesis pathway may be enriched. Our findings suggest that T. matsutake dominance could be associated with the spatial and seasonal restructuring of soil microbial communities, potentially leading to the formation of functionally interconnected microbial networks. Therefore, this study predicts hidden ecological insights that, once biochemically validated, may be used to develop important strategies for the sustainable conservation and artificial cultivation of T. matsutake. Full article

This study addresses the challenge of thermal accumulation and low efficiency in conventional ground heat exchangers for building heating and cooling applications. A novel direct-expansion CO₂ borehole heat exchanger (BHE) backfilled with well water is proposed to enhance heat transfer and mitigate soil thermal imbalance. A dynamic thermal resistance-capacity model (TRCM) coupling CO₂ phase change with natural convection in well water is developed and validated against full-scale field experiments (135 m depth), with prediction errors below 5% under cooling conditions (MAPE 2.29%, RMSE 2.49%). Quantitative analysis reveals that natural convection in well water enhances overall heat transfer by 14.9% compared to soil-backfilled systems, despite intensifying thermal short-circuiting. Two practical enhancement strategies for building energy efficiency are proposed: (1) adding insulation to the rising pipe, which increases the heat transfer rate by up to 35.1%; and (2) implementing artificial well-water circulation, which achieves up to 50.5% enhancement, with an equivalent coefficient of performance (COP) reaching 52.5 under intermittent operation. The proposed system and the parametric analysis of these strategies offer effective solutions for improving the energy performance of ground-source heat pumps in buildings, contributing to reduced operational energy consumption and enhanced system reliability. Full article

Soil electrical resistivity (SER) is widely used as an indirect indicator of soil physical, chemical, and hydrological properties and plays an important role in applications such as grounding system design, geotechnical site characterization, agricultural soil monitoring, and environmental contamination assessment. However, SER is strongly influenced by environmental variables including soil moisture content, temperature, salinity, and soil texture, which makes accurate prediction challenging under heterogeneous field conditions. A systematic review was conducted following the PRISMA 2020 protocol using the Scopus database to identify peer-reviewed studies published between 2018 and 2026 related to predictive models of soil electrical resistivity based on environmental parameters. After applying defined inclusion and exclusion criteria, a set of relevant studies was selected for qualitative and comparative analysis. The reviewed studies consistently identify soil moisture content as the most frequently reported influential factor affecting SER, followed by temperature, salinity, and soil texture. This observation reflects the predominant focus of the analyzed literature within the selected time frame rather than a definitive representation of all controlling physical processes. Similarly, the reviewed literature suggests that empirical and statistical models remain valuable due to their simplicity and interpretability, whereas machine learning approaches such as artificial neural networks, support vector regression, and ensemble methods are often reported to achieve higher predictive accuracy in complex soil environments. The predictive SER modeling represents a rapidly evolving research field, and future work should focus on hybrid physics-informed machine learning models, the development of standardized datasets, and the integration of predictive algorithms with emerging sensing technologies and IoT-based monitoring systems. Full article

Fuzzy logic-based mineral potential mapping was applied to the Tisová–Klingenthal Cu–Co VMS deposit (Erzgebirge) in the Czech–German border region. The study area is characterized by heterogeneous geological and geochemical datasets derived from differing national surveys and historical mining. Using the Exploration Information System (EIS) toolkit, a knowledge-driven fuzzy logic approach integrated key spatial datasets, including copper and zinc soil and stream sediment anomalies and metabasalt lithology, relevant to Besshi-type VMS deposits. Three prospective anomalies were identified: the historic Tisová mine and two additional targets aligned along the same stratigraphic horizon. Artificial Neural Network (ANN) modelling was limited by insufficient training data, resulting in overfitting and reduced predictive reliability. Follow-up soil geochemical surveys conducted over the largest anomaly returned locally elevated copper values but did not conclusively confirm mineralisation. The results demonstrate that fuzzy logic provides a flexible and interpretable framework for mineral potential mapping in complex, data-scarce environments and highlight the need for iterative modelling and targeted exploration. Full article

Revitalisation of contaminated brownfield sites is essential for sustainable development, particularly near sensitive ecological areas like Natura 2000 sites. The lagoon in Slovenia’s Regional Park Šturmovci, an artificial wastewater convergence point created during hydroelectric construction, is a highly relevant example. This study integrates geochemical, mineralogical and isotopic analyses to identify sources and controlling mechanisms of contaminant distribution in lagoon sediments and assess their transfer to nearby agricultural soils during flooding events. Results indicate anaerobic conditions, with depth-related shifts in phosphorus, sulphur and redox-sensitive elements, such as rare earth elements (REE), arsenic (As), barium (Ba), cobalt (Co), chromium (Cr), lead (Pb) and vanadium (V), as well as fluctuations in pyrite-rich laminated layers, suggesting potential flood-driven remobilisation of trace elements. Lagoon sediments are highly contaminated with As (73 mg kg⁻¹), Ba (247 mg kg⁻¹), Pb (97 mg kg⁻¹) and Zn (1118 mg kg⁻¹), with elevated concentrations also observed in agricultural soil, all exceeding respective limit values of 20, 160, 85 and 200 mg kg⁻¹. Pollutant concentrations were highest near wastewater inflows and decreased with distance, with nitrogen isotopic patterns indicating partial nitrification and surface ammonium accumulation, reflecting intensive agricultural inputs in the area. High enrichment factor (EF > 20) and geoaccumulation index (I_geo > 3) values, in particular for As, Cd and Zn, indicated severe contamination and highlighted the urgent need for effective remediation strategies, including immobilisation using biochar or cement-based binders, as well as phytoremediation approaches. Full article

Climate change exerts growing pressure on the built environment, intensifying urban heat stress, altering microclimatic conditions, and increasing energy demand and health risks. Urban areas, characterized by dense construction and extensive soil sealing, are particularly susceptible to thermal anomalies such as Urban Heat Islands (UHIs), making thermal assessment a crucial element in adaptation and mitigation strategies. This research provides an updated and critical review of methodologies for the thermal evaluation of the built environment, with a focus on remote sensing as an emerging and integrative measurement paradigm. The study presents a comprehensive framework of detection systems, including satellite and aerial remote sensing, ground-based monitoring, and hybrid approaches, complemented by analytical and modeling techniques that combine physical and data-driven methods. A comparative assessment of open-access satellite sensors is carried out, analyzing spatial, spectral, and temporal resolutions and their relevance to urban-scale applications. The integration of remote sensing data with artificial intelligence, machine learning, and cloud-based processing is highlighted as a key advancement for improving interpretative, predictive, and decision-support capabilities. The findings indicate that such integration represents a new frontier for multiscale thermal analysis, supporting resilient urban planning, enhanced energy efficiency, and effective climate change mitigation policies. Full article

The uptake and accumulation of mercury (Hg) in rice poses a serious threat to food safety. Pot experiments are widely used to screen for low-Hg-accumulating cultivars, yet their reliability in predicting field performance remains uncertain. This study evaluated pot-based screening by (1) comparing Hg uptake in rice grown in freshly processed versus aged soil; (2) contrasting Hg accumulation in the same cultivars grown in pots versus at two field sites; and (3) isolating micro-environmental effects by burying pots in situ. A total of 22 rice cultivars were used during 2021–2023 in this study. Pot systems, regardless of soil treatment, failed to replicate field accumulation patterns, yielding significantly greater Hg concentrations in brown rice (up to 59.24 ng g⁻¹) than field conditions (maximum 32.33 ng g⁻¹). Cultivar rankings derived from pot experiments showed little or no correlation with field rankings, indicating that performance is not transferable across environments. Random forest analysis identified elevated soil temperature and reduced light intensity as key artificial factors driving overestimation in pots, explaining 15.68% (total Hg) and 21.65% (methylmercury) of the variation. We conclude that pot experiments—due to soil disturbance and altered microclimates—overestimate Hg accumulation potential and show limited predictive capacity under the tested conditions. Therefore, field validation across multiple sites and seasons is essential for accurate mercury risk assessment and region-specific cultivar recommendation. Full article

Hydroponic cultivation is expanding rapidly as a resource-efficient alternative to soil-based farming, but challenges related to nutrient management, abiotic or biotic stresses, and organic production still limit the system’s performance and efficiency. Biostimulants are increasingly being explored as a promising strategy to support productivity and sustainability in soilless systems. This review summarizes the current evidence on the use of plant biostimulants to support crop performance in hydroponic systems. Microbial biostimulants, such as plant growth promoting rhizobacteria, Arbuscular Mycorrhizal Fungi, and Trichoderma spp., have been reported to promote root growth by synthesizing phytohormones, enhance nutrient uptake, and reduce the impacts of salt and heat stress, with reported improvements in biomass and nutrient use efficiency. Seaweed extracts and protein hydrolysates modulate plant hormonal balance, improve antioxidant defense, and have been associated with improvements in yield and quality. Humic and fulvic acids increase micronutrient bioavailability through chelation and stimulate root activity through auxin-like effects. In organic hydroponics, biostimulants may help address the nutrient gap by accelerating organic matter mineralization. Existing key challenges include the lack of hydroponic-specific dosage guidelines and high commercialization costs. Future efforts should further evaluate system-specific strategies, including emerging tools such as artificial intelligence-optimized strategies and the use of clustered regularly interspaced short palindromic repeats-edited microbes to support the long-term sustainability of controlled environment agriculture. Full article

Industrialization has led to the substantial release of heavy metals and organic pollutants into soil and groundwater, resulting in severe contaminated site issues that pose significant threats to ecosystems and human health. This review aims to systematically review the current development status and challenges of contaminated site remediation technologies, and explore the potential of artificial intelligence (AI) applications in site remediation, to provide a theoretical reference for advancing intelligent remediation. Conventional remediation technologies mainly include physical methods (e.g., solidification/stabilization (S/S), soil vapor extraction (SVE), thermal desorption, pump and treat (P&T), groundwater circulation wells (GCWs)), chemical methods (e.g., chemical oxidation/reduction, electrokinetic remediation (EKR), soil washing), and biological methods (phytoremediation, microbial remediation), along with combined strategies that integrate multiple approaches. Although these technologies have achieved certain successes in engineering practice, they still face common challenges such as risks of secondary pollution, long remediation periods, high costs, poor adaptability to complex hydrogeological conditions, and insufficient long-term stability, making it difficult to fully meet the remediation demands of complex contaminated sites. Subsequently, the potential of emerging technologies—including nanomaterial-based remediation, bioelectrochemical systems, and molecular biology-assisted remediation—is introduced. On this basis, the forefront applications of AI in contaminated site remediation are discussed, covering site monitoring and characterization, risk assessment, remedial strategy selection, process prediction and parameter optimization, material design, and post-remediation intelligent stewardship. Machine learning (ML), explainable AI (XAI), and hybrid modeling approaches have markedly improved remediation efficiency and decision-making. Looking forward, with advancements in XAI, mechanism-data fusion models, and environmental foundation models, AI is poised to drive a paradigm shift toward intelligent and precision remediation. However, challenges related to data quality, model interpretability, and interdisciplinary expertise remain key barriers to overcome. Full article

Plants of the genus Opuntia are cacti that grow under natural conditions, with scarce humidity, drastic changes in daytime and nighttime temperatures, and poor soils. Their fruits are a food source in certain regions of the world, and their modified stems (cladodes) have diverse uses, including human consumption—especially when young, tender, and succulent (“nopalitos”) —livestock feed, and raw material for various products. There are approximately 300 species and dozens of variants of this genus, identified as wild, semi-domesticated, or domesticated. The physiological and biochemical responses to abiotic stress in these species are diverse but are related to their Crassulacean acid metabolism and the level of domestication. The morphological modifications in fruits, seeds, and cladodes of the genus Opuntia during domestication appear to be the sum of numerous significant biochemical-physiological changes, but generally of small magnitude. Thus, evaluating wild, semi-domesticated, and domesticated Opuntia species allows us to understand the physiological and biochemical processes along a natural gradient (original and modified by natural and artificial selection and by the cultivation environment) and their alteration by abiotic stress of any kind. This review summarizes our main advances in considering the genus Opuntia as a model for evaluating abiotic stress in the physiology and biochemistry of succulent plants. Furthermore, it shows high relevance, especially in the context of climate change, because Opuntia species are key to food security in arid zones. Full article

The increasing demand for innovative strategies in sustainable food production—driven by rapid global population growth, particularly in sub-Saharan Africa (SSA)—necessitates urgent attention to agricultural resilience. Recent technological advancements have enhanced crop productivity, post-harvest preservation, and environmentally sustainable farming practices. However, three critical bottlenecks remain: (i) the lack of accurate, maize-specific yield prediction methods tailored to SSA; (ii) limited multimodal modeling approaches capable of capturing complex, nonlinear interactions among heterogeneous data sources; and (iii) a lack of explainability mechanisms, which render high-performing models “black boxes” and hinder stakeholder trust. To address these gaps, this study presents an explainable machine learning framework for smart maize yield prediction. We integrate multimodal SSA-specific soil, crop, and weather data to capture the multi-dimensional drivers of maize productivity. Six diverse algorithms—including extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), categorical boosting (CatBoost), support vector machine (SVM), random forest (RF), and an artificial neural network (ANN) combined with a k-nearest neighbors (kNN)—were benchmarked to evaluate predictive performance. To ensure robustness against spatial heterogeneity, we employed a Leave-One-Plot-Out (LOPO) cross-validation strategy. Empirical results on unseen test data identify CatBoost as the best-performing model, achieving a coefficient of determination of ($R^2 =~76\%$), demonstrating its ability to capture complex, nonlinear relationships in agricultural data. To enhance transparency and stakeholder trust, we integrated Local Interpretable Model-agnostic Explanations (LIME), providing plot-level insights into the physiological and environmental drivers of maize yield. Together, these contributions establish a scalable and interpretable modeling framework capable of supporting data-driven agricultural decision-making in SSA. Full article

Artificial intelligence (AI)-assisted drone technology in agriculture has transformed productivity and pest control techniques, resulting in novel solutions to modern farming challenges. Drones utilizing sensors, cameras, and AI algorithms can precisely monitor crop health, soil conditions, and insect infestations. Using AI-assisted drones for precision irrigation and yield predictions further improves resource allocation, promotes sustainability, and reduces operating costs. This review examines recent advancements in AI and unmanned aerial vehicles (UAVs) in precision agriculture. Key trends include AI-driven crop disease detection, UAV-enabled multispectral imaging, precision pest management, smart tractors, variable-rate fertilization, and integration with IoT-based decision support systems. This study synthesizes current research to identify technological progress, implementation challenges, scalability barriers, and opportunities for sustainable agricultural transformation. This review of peer-reviewed studies published between 2013 and 2025 uses major scientific databases and predefined inclusion and exclusion criteria covering crop monitoring, precision input application, integrated pest management (IPM), and livestock (especially cattle) monitoring. We describe the platform and payload trade-offs that govern coverage, endurance, and spray quality; the dominant analytics trends, from classical machine learning to deep learning and embedded/edge inference; and the emerging shift from monitoring-only UAV use toward closed-loop decision-making (detection–prediction–intervention). Across the literature, the strongest opportunities lie in robust field validation, multi-modal data fusion (UAV + ground sensors + farm records), and interoperable standards that enable actionable IPM decisions. Key gaps include limited cross-site generalization, scarce reporting of economic indicators (ROI, payback period, and adoption rate), and regulatory and safety barriers for routine autonomous operations. Finally, we present some case studies to emphasize the feasibility and highlight future research directions of AI-assisted drone technology. Through this review, we aim to demonstrate technological advancements, challenges, and future opportunities in AI-assisted drone applications, ultimately advocating for more sustainable and cost-effective farming practices. Full article