Search Results (347)

Reliable monitoring of pesticide residues is essential for ensuring food safety. Conventional chromatographic and spectrometric techniques remain labor-intensive, time-consuming, and costly. Recent progress in Machine Learning (ML) provides computational tools that improve the precision and efficiency of pesticide residue detection in diverse food matrices. This review presents a comprehensive analysis of current ML-based approaches for pesticide analysis, with particular attention to supervised learning algorithms such as support vector machines, random forests, boosting methods, and deep neural networks. These models have been integrated with chromatographic, spectroscopic, and electrochemical platforms to achieve enhanced signal interpretation and more reliable prediction from existing analytical data, and more robust data processing in complex food systems. The review also discusses methodologies for feature extraction, model validation, and the management of heterogeneous datasets, while examining ongoing challenges that include limited training data, matrix variability, and regulatory constraints. Emerging advances in deep learning architectures, transfer learning strategies, and portable sensing technologies are expected to support the development of real-time, field-ready monitoring systems. The findings highlight the potential of ML to advance food quality assurance and strengthen public health protection through more efficient and accurate pesticide residue detection. Full article

Urban tree canopy is increasingly recognized as a health-protective form of green infrastructure, yet its distribution remains uneven across socioeconomically stratified neighborhoods. This study quantifies fine-scale tree-canopy inequity across Census Block Groups (CBGs) in Baltimore and examines associations with socioeconomic deprivation and modeled pollution-related cancer risk. We integrated (i) 2023 US Forest Service canopy estimates aggregated to CBGs, (ii) Area Deprivation Index (ADI) national and state ranks, (iii) American Community Survey 5-year population counts, and (iv) EPA NATA/HAPs cancer-risk estimates aggregated to CBGs using population-weighted means. Associations were assessed using Spearman correlations and visualized with LOESS smoothers. Canopy was negatively associated with ADI national and state ranks (ρ = −0.509 and −0.503), explaining 29–31% of canopy variation. Population-weighted canopy declined from 47–51% in the least deprived decile to 13–15% in the most deprived (3.4–4.1× disparity). Beyond socioeconomic gradients, overall distributional inequity was quantified using a population-weighted Tree Canopy Inequality Index (TCI; weighted Gini), yielding TCI = 0.312, indicating substantial inequality. The population-weighted Atkinson index rose sharply under increasing inequality aversion (A_0.5 = 0.084; A₂ = 0.402), revealing extreme canopy deficits concentrated among the most disadvantaged neighborhoods. Canopy was also negatively associated with modeled cancer risk (ρ = −0.363). We constructed a Triple Burden Index integrating canopy deficit, deprivation, and cancer risk, identifying spatially clustered high-burden neighborhoods that collectively house over 86,000 residents. These findings demonstrate that canopy inequity in Baltimore is structurally concentrated and support equity-targeted greening and sustained maintenance strategies guided by distributional justice metrics. Full article

The global literature underscores a set of human wellbeing challenges and opportunities for forestland exploitation, albeit the lack of region-specific evidence. This concerns the Congo Basin, the second-largest forest ecosystem in the world. This study uses the case of the Campo Ma’an Landscape to: (i) analyze the challenges linked to the exploitation of forestland resources, and (ii) explore forest resource exploitation opportunities in the landscape. The study employed a random sample of 200 natural resource-dependent households drawn from four study zones—Niete, Campo, Ma’an and Akom II. This was complemented by focus group discussions (n = 4), key informant (n = 6) and expert (n = 6) interviews. The descriptive and inferential analyses led to the following results: First, economic, technical, socio-cultural and institutional challenges affect the sustainable exploitation of forestland resources in the Campo Ma’an Landscape. The economic challenges of forest (B = −0.389, p = 0.01) and land resource exploitation (B = −0.423, p = 0.006) significantly affect sustainable exploitation compared to other challenges, leading to biodiversity loss and deforestation. These constitute a threat to planetary health systems. Almost all households rely on forestland resources for their livelihoods and development, with opportunities for land resource exploitation outweighing those in forest resource exploitation. Protected area management and agriculture are affected owing to competing interests among farmers, conservationists and other land users. Thus, short-term economic gains are prioritized over long-term sustainability, putting the resource landscape at risk of degradation and future uncertainties. Integrated stakeholder engagement, capacity building, and policy revision could enhance the planetary health approach by linking the social, economic and environmental dimensions of forestland resource management. Full article

Hericium erinaceus is a fungus that, in addition to its health-promoting properties (including regenerative properties for gastrointestinal membranes and support for neuronal regeneration in neurodegenerative diseases such as Parkinson’s disease), has the ability to synthesize valuable metabolites, such as flavonoids (polyphenols) and terpenoids. These compounds possess strong biocidal properties. These substances provide the growing H. erinaceus mycelium with protection against colonization by other species of rot fungi, such as Trametes versicolor. For these reasons, the biological compounds produced by H. erinaceus can be used to produce ecological fungicides, which will find innovative applications in protecting forest tree seedlings. It should also be emphasized that valuable fungal substances are synthesized primarily by the mycelium of H. erinaceus during the initial stages of its development. Therefore, we undertook to develop an updated and modernized methodology for cultivating H. erinaceus mycelium in the laboratory, with the goal of commercializing the production of this mycelium, which will be used to isolate fungicidal substances metabolized by the fungus cultures. The biocidal substances obtained will be used to produce innovative fungicides in order to protect forest tree seedlings. The studies were conducted using various types of nutrient media, including Potato Dextrose Agar (PDA), Malt Extract Agar (MEA), and wort medium, at various temperatures ranging from 15 °C to 25 °C. Simultaneously, experiments were conducted using solidified media with a pH ranging from 4.0 to 7.0. The research was also expanded to include the growth and execution of experiments using a processed wood substrate, namely, sawdust made from individual structural wood elements. The sawdust was prepared from the bark, sapwood, and heartwood of sessile oak. The PDA medium was more favourable to the mycelium growth of H. erinaceus at 25 °C. It was also found that an acidic pH in the range of 4.0–5.0 significantly influenced the changes in the growth rate of the mycelium species and their phenotype. It was observed that mycelial growth on a substrate of oak sawdust made from sapwood resulted in intensive mycelial growth and a significant reduction in the wood substrate compared to sawdust made from bark, heartwood, and a mixture of all types of sawdust. The reason for the low mycelial growth, low mass reduction and slight reduction in the mass of sawdust made from bark, heartwood, and a mixture of all types of sawdust was the presence of high levels of tannins, which inhibited the fungal growth. Full article

Accurate assessment of water quality is crucial for protecting public health and promoting environmental sustainability. Conventional laboratory-based methods for evaluating microbial contaminants are often time-consuming, resource-intensive, and reactive in nature, limiting their effectiveness for real-time water quality monitoring and management. This study examines the application of data-driven machine learning models to predict E. coli concentrations in Midmar Dam, utilizing readily available physicochemical parameters. A comparative analysis was conducted using five classical standalone ML algorithms: Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGBoost). These models were assessed based on their predictive performance using standard error metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Among the models evaluated, the kNN algorithm demonstrated superior performance, achieving the lowest MSE and RMSE values, thereby highlighting its effectiveness in capturing the complex relationships between physicochemical indicators and microbial contamination levels. The findings demonstrate the potential of ML-based approaches to serve as efficient, scalable, and proactive tools for sustainable water-quality monitoring and management in dams. Full article

Mountain forests play a key role in buffering local climate, yet their climate sensitivity is seldom mapped in a way that is directly usable for spatial planning. This study investigates how phenological thermal and vegetation variability are organized within the forested landscape of Namyangju, a mountainous region in central Korea, and derives spatial indicators of forest climate sensitivity. Using monthly, cloud-screened Landsat-8/9 land surface temperature (LST) and normalized difference vegetation index (NDVI) images over a recent multi-year period, we calculated phenological coefficients of variation for 34,123 forest grid cells and applied local clustering analysis to identify belts of high and low variability. Forest areas where LST and NDVI variability simultaneously occupied the upper tail of their distributions (top 5%/10%/20%) were interpreted as climate-sensitivity hotspots, whereas co-located coldspots were treated as microclimatic refugia. Across the mountainous terrain, sensitivity hotspots formed continuous belts along high-elevation ridges and steep, dissected slopes, while coldspots were concentrated in sheltered valley floors. Notably, the most sensitive belts were dominated by high-elevation conifer stands, despite the limited seasonal fluctuation typically expected in evergreen canopies. This pattern suggests that elevation strongly amplifies the coupling between thermal responsiveness and vegetation health, whereas valley-bottom forests act as stabilizers that maintain comparatively constant microclimatic and phenological conditions. We refer to these patterns as “forest climate-sensitivity belts,” which translate satellite observations into spatially explicit information on where climate-buffering functions are most vulnerable or resilient. Incorporating climate-sensitivity belts into forest plans and adaptation strategies can guide elevation-aware species selection in new afforestation, targeted restoration and fuel-load management in upland sensitivity zones, and the protection of valley refugia that support biodiversity, thermal buffering, and hydrological regulation. Because the framework relies on standard satellite products and transparent calculations, it can be updated as new imagery becomes available and transferred to other seasonal, mountainous regions, providing a practical basis for climate-resilient forest planning. Full article

Background: Chronic obstructive pulmonary disease (COPD) is a global public health issue and a major cause of morbidity and mortality. Meat consumption is considered one of the factors influencing the risk of COPD. This study aims to perform a systematic review and meta-analysis to synthesize evidence on meat consumption and COPD risk. Methods: A systematic review and meta-analysis were performed and reported through a comprehensive search in PubMed and Web of Science from inception to March 2025 (PROSPERO registration ID: CRD42024595137). This meta-analysis included fifteen observational studies. Forest plots were presented, statistical heterogeneity was quantified with the I² statistic and investigated through subgroup analyses. Funnel plots and Egger’s test were used to evaluate publication bias. Results: The results showed that the odds ratio (OR) for total meat consumption and the risk of COPD was 1.15 (95% confidence interval (CI): 1.01–1.31), suggesting that meat consumption was associated with a higher risk of COPD. Our analysis revealed that fish consumption (OR: 0.84; 95% CI: 0.72–0.97) had a protective effect on COPD risk while processed meat consumption (OR: 1.18; 95% CI: 1.02–1.37) and cured meat consumption (OR: 1.64; 95% CI: 1.41–1.90) was significantly associated with an increased risk of COPD. In addition, subgroup analysis suggested that higher meat consumption was associated with an elevated risk of COPD in cross-sectional study (OR = 1.78; 95% CI: 1.57–2.02), case–control study (OR = 1.52; 95% CI: 1.10–2.10) and in group with 1000 or more participants (OR = 1.16; 95% CI: 1.01–1.33). Conclusions: The pooled results of this meta-analysis suggest an association between total meat consumption (encompassing fish, processed meat, cured meat, and unprocessed meat) and COPD. However, the strength of this evidence is tempered by substantial between-study heterogeneity and inconsistent findings across study designs—notably, cohort data failed to support a significant association. Future research should standardize classifications and explore meat subtypes to address heterogeneity. Full article

This study investigated the diversity, distribution, and seasonal abundance of ambrosia and pine bark beetles (PBBs) in the Florida Panhandle, focusing on Leon and Gadsden Counties between July 2022 and October 2023. A total of 1657 specimens representing 24 species and 18 genera were captured using baited Lindgren funnel traps. Dominant species varied by location: Xyleborinus saxesenii, Cnestus mutilatus, and Xylosandrus crassiusculus were most abundant in Leon County, while Xylosandrus amputatus prevailed in Gadsden County. Three new county records were documented, including Xylosandrus amputatus and Ambrosiodmus lewisi for Leon County, and Cyclorhipidion distinguendum for Gadsden County. Additionally, three ambrosia beetle species within Platypodinae Euplatypus compositus, Myoplatypus flavicornis, and Euplatypus compositus were recorded across both counties. Seasonal patterns showed pronounced activity peaks during spring and early fall, corresponding with warmer and more humid conditions that support beetle reproduction and host colonization. Climatic analysis revealed that moisture-related variables, particularly relative humidity and precipitation, were the strongest predictors of beetle abundance, reflecting the ecological dependence of ambrosia beetles on fungal symbionts. Greater species richness observed in Leon County suggests that favorable microclimatic and habitat conditions enhance colonization dynamics. The documentation of new county records highlights the influence of shifting trade pathways, human movement, and environmental change on species introductions. The findings underscore the need for continuous surveillance and refined detection systems integrating ethanol-based lures and species-specific pheromones. As climate change continues to modify forest ecosystems, these results provide essential guidance for developing proactive monitoring and management strategies to protect forest health, biodiversity, and timber resources in the Florida Panhandle. Full article

The rhizosphere is a dynamic microenvironment where plants interact with diverse native microbial communities that significantly influence growth, health, and resilience. Among plant-growth-promoting rhizobacteria, Bacillus subtilis stands out as a multifunctional species with exceptional ability to colonize plant roots, form robust biofilm, and confer protection against diseases. Its resilience as a spore-former, genetic ability to produce active compounds such as antibiotics, and phytohormones make it a valuable species for agriculture and forest sustainability. This review reveals the molecular and physiological mechanisms underlying B. subtilis interactions with plants, focusing on biofilm formation, root colonization, biocontrol and disease suppression, and promotion of plant growth. We further examine its role in root colonization, which triggers extensive reprogramming of plant gene expression, thereby integrating growth promotion with enhanced immune competence through a network that regulates plant-beneficial traits. Its genomic regulation supports colonization, stress tolerance, and immune support, while synergistic interactions with other microbes highlight its adaptability. As a versatile bio-fertilizer and biocontrol agent, further study of its strain-specific traits and rhizosphere interactions is key to maximizing its role in sustainable agriculture and forest control under environmental changes. Full article

This study investigated the interrelationships among Lifestyles of Health and Sustainability (LOHASs), recreational benefits, and environmentally responsible behaviors (ERBs) of visitors to the Xitou Forest Recreation Area in Taiwan and estimated the conservation value of its forest recreation resources using the contingent valuation method. The structural equation modeling analysis supported six of eight hypotheses. Three LOHAS factors (environmental awareness, internal health, and external health) indirectly promoted ERB through recreational benefits, including environmental education, psychological, physiological, and social benefits. Higher income, stronger perceived recreational benefits, and recognition of ecological or facility value significantly increased visitors’ willingness to pay (WTP). Among the three identified lifestyle clusters, the health-conscious LOHAS group consistently exhibited the highest WTP at NTD$263, with a confidence interval of NTD$255–271, surpassing both the eco-friendly group (NTD$193–209) and socially engaged group (NTD$184–200), demonstrating a stronger commitment to ecological and environmental protection and recreational facility maintenance. Forest recreation managers can target different LOHAS segments and emphasize the holistic benefits of forest recreation. Implementing flexible pricing alongside environmental education can increase WTP, supporting sustainable conservation funding and improved visitor experiences. Full article

Water hardness, an important factor influencing both human health and aquatic ecosystems, is controlled by natural processes and human activities. This study examined spatiotemporal variations in water hardness in Jinshu Port (JP) and Yuyang Mountain (YM) water sources in Suzhou from 2011 to 2023. The JP source exhibited a higher total hardness (92–182 mg/L) than the YM source (87–179 mg/L), and both sites showed clear seasonal patterns. Long-term trends diverged: the JP source remained stable, while the YM source declined significantly. Carbonate hardness increased, whereas non-carbonate hardness decreased in both sites. These changes were associated with the acid rain frequency, which correlated positively with non-carbonate hardness but negatively with carbonate hardness. Land use also strongly affected hardness: farmland-dominated rivers in Huxi (90–210 mg/L) had higher levels than forest-dominated rivers in Zhexi (76–164 mg/L). Water-soluble calcium and magnesium in farmland soils were about 4.5 times higher than those in forest soils and roughly doubled with fertilization. Overall, human activities—including land use, fertilizer application, and acid rain—strongly influenced hardness patterns. Over the past decade, the hardness in both regions has generally remained stable with a slight decrease, suggesting that the strict environmental protection in the Taihu Lake Basin effectively mitigated anthropogenic impacts on water sources. Full article

This study examines Phase B of the GREENERGY project focusing on the seismic performance and structural health monitoring of a renovated single-story RC frame with brick masonry infills that received significant strategic structural interventions. The columns were confined with basalt fiber ropes (FR, 4 mm thickness, two layers) in critical regions, the vertical interfaces between infill and concrete were filled with polyurethane PM forming PUFJ (PolyUrethane Flexible Joints), and glass fiber mesh embedded in polyurethane PS was applied as FRPU (Fiber Reinforced PolyUrethane) jacket on the infills. Further, greenery renovations included the attachment of five double-stack concrete planters (each weighing 153 kg) with different support-anchoring configurations and of eight steel frame constructions (40 kg/m²) simulating vertical living walls (VLW) with eight different connection methods. The specimen was subjected to progressively increasing earthquake excitation based on the Thessaloniki 1978 earthquake record with peak ground acceleration ranging from EQ0.07 g to EQ1.40 g. Comprehensive instrumentation included twelve accelerometers, eight draw wire sensors, twenty-two strain gauges, and a network of sixty-one PZTs utilizing the EMI (Electromechanical Impedance) technique. Results demonstrated that the structure sustained extremely high displacement drift levels of 2.62% at EQ1.40 g while maintaining structural integrity and avoiding collapse. The PUFJ and FRPU systems maintained their integrity throughout all excitations, with limited FRPU fracture only locally at extreme crushing zones of two opposite bottom bricks. Columns’ longitudinal reinforcement entered yielding and strain hardening at top and bottom critical regions provided the FR confinement. VLW frames exhibited equally remarkably resilient performance, avoiding collapse despite local anchor degradation in some investigated cases. The planter performance varied significantly, yet avoiding overturning in all cases. Steel rod anchored planter demonstrated superior performance while simply supported configurations on polyurethane pads exhibited significant rocking and base sliding displacement of ±4 cm at maximum intensity. PZT structural health monitoring (SHM) sensors successfully tracked damage progression. RMSD indices of PZT recordings provided quantifiable damage assessment. Elevated RMSD values corresponded well to visually observed local damages while lower RMSD values in columns 1 and 2 compared with columns 3 and 4 suggested that basalt rope wrapping together with PUFJ and FRPU jacketed infills in two directions could restrict concrete core disintegration more effectively. The experiments validate the advanced structural interventions and vertical forest renovations, ensuring human life protection during successive extreme EQ excitations of deficient existing building stock. Full article

Bacterial diseases of forest trees represent an increasing threat to ecosystem health and the sustainability and resilience of forest management, particularly under changing climate conditions. One of the key yet still insufficiently understood adaptive mechanisms of pathogens is biofilm formation—a structured community of bacterial cells embedded in a matrix of extracellular polymeric substances (EPS), which provides protection against stress factors, biocides, and the host’s defensive responses such as antimicrobial compounds or immune reactions. This paper presents a comprehensive review of current knowledge on the role of biofilms in the bacterial pathogenesis of forest trees, covering their formation mechanisms, molecular regulation, and ecological significance. Four key stages of biofilm development are discussed—adhesion, microcolony formation, EPS production, and dispersion—along with the roles of quorum sensing systems and c-di-GMP-based signaling in regulating these processes. Examples of major tree pathogens are presented, including Pseudomonas syringae, Erwinia amylovora, Xylella fastidiosa, the Brenneria–Gibbsiella complex associated with Acute Oak Decline (AOD) and Lonsdalea populi. Biofilm formation is shown to play a crucial role in the colonization of xylem, leaf surfaces, and tissues undergoing necrosis, where biofilms may stabilize decomposition zones and support saprophytic–pathogenic transitions. In the applied section, the concept of “biofilm-targeted control” is discussed, encompassing both chemical and biological strategies for disrupting biofilm structure—from quorum-sensing inhibitors and EPS-degrading enzymes to the use of biosurfactants and antagonistic microorganisms. The need for in situ research in forest environments and the adaptation of advanced imaging (CLSM, micro-CT) and metagenomic analyses to tree systems is also emphasized. This review concludes that biofilms are not merely a physiological form of bacterial organization but a complex adaptive system essential for the survival and virulence of pathogens in forest ecosystems. Understanding their functions is fundamental for developing sustainable and ecologically safe phytosanitary strategies for forest protection. Full article

Forecasting influent flow in Wastewater Treatment Plants (WWTPs) is critical for managing operational risks during flash floods, especially in Spain’s Mediterranean coastal regions. These facilities, essential for public health and environmental protection, are vulnerable to abrupt inflow surges caused by heavy rainfall. This study proposes a data-driven approach combining historical flow and rainfall data to predict short-term inflow dynamics. Several models were evaluated, including Random Forest, XGBoost, CatBoost, and LSTM, using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²). XGBoost outperformed the others, particularly under severe class imbalance, with only 1% of the data representing rainfall events. Hyperparameter tuning and input window size analysis revealed that accurate predictions are achievable with just 14 days of training data from a 10-year (2012–2022) dataset sourced from a single WWTP and on-site weather station. The proposed framework supports proactive WWTP management during extreme weather events. Full article

The purpose of this study was to determine the motivation to buy eco-friendly products via machine learning techniques. With this in mind, a dataset was collected between November and December 2024 from 245 organic consumers in Maramureș County, Romania, via a questionnaire. Consumers’ main motivations to buy eco-friendly products were considered according to three categories: Health Care, Environmental Protection, and Superior Quality. In the analysis of the dataset, among the four feature selection techniques used, Random Forest was determined to be the best with the highest accuracy value. At the beginning of the study, 16 variables were thought to be important categorical factors for consumers’ eco-friendly product-buying motivations, with 5 of these being found to be the most effective with the Random Forest technique. Then, the SHAP method was applied to identify the contribution of driving factors to the buying motivation for eco-friendly products. All analyses were conducted with Python software. The results of the SHAP method indicated that while all factors perform well, consumers considering themselves as eco-friendly is the most important factor for the Environmental Protection category when buying eco-friendly products, while the most important criterion of the original certification category was found to be the Health Care category. The most effective factor for Superior Quality was determined as the high-price category, which is the main barrier to purchasing eco-friendly products. Full article