Asthma is a debilitating disease, and its diagnosis and disease management remain imprecise. It continues to impose a major global burden on public health, medicine, and the economy. Asthma exhibits marked heterogeneity in clinical phenotypes, and environmental and genetic risk factors remain incompletely defined. Moreover, its significant geographical and ethnic variation limits diagnostic precision. They also hinder effective risk stratification and accurate prediction of disease exacerbations. To date, most asthma research and therapeutic development have focused on allergen-mediated immune responses. Conversely, the adverse effects of environmental chemical pollutants have received less attention. This imbalance has limited the development of a comprehensive understanding of asthma pathogenesis. It has also slowed progress toward truly precision-based therapies. Simultaneously, growing experimental and clinical evidence highlights causal links between environmental exposures and disease. The concepts of the exposome and exposomics have also emerged. These provide useful frameworks to study disease development and progression. In this review, we summarize recent multicenter studies on asthma. These studies show that environmental determinants of asthma are not uniform, as different asthma phenotypic clusters have distinct environmental exposure profiles. Moreover, environmentally driven metabolic reprogramming plays an important role, resulting in bioactive metabolites that also deserve careful attention. These factors are crucial for advancing precision environmental medicine.
Effective management of soil heavy metal(loid)s (HMs) requires linking carrying limits to specific pollution sources for targeted regulation. However, traditional environmental capacity calculation often lacks integrated health risk and source apportionment models, thereby overestimating safety margins by ignoring critical toxicological constraints. Analyzing 430 soil samples from Shanghai green spaces, this study developed an environmental capacity model constrained by health risks and quantized source-specific capacity loads by coupling with Positive Matrix Factorization (PMF). Results indicate relatively low soil HM pollution (average PN = 3.05), yet both children and adults face non-negligible carcinogenic risks exceeding the negligible risk threshold (10-6). Upon introducing health risk constraints, the environmental carrying capacity significantly decreased, the comprehensive environmental capacity index decreased from 1.002 to 0.784, resulting in a downgrade of the regional capacity level from "high" to "medium", and Cr and As showed the most significant declines among all HMs, with their average capacity indices dropping by 77.14% and 41.01%, respectively. Source-capacity coupling analysis further revealed that traffic emissions exhibited the highest contribution to the capacity load at 42.68%. Our findings challenge conventional total-quantity control and provide a quantitative basis for shifting to a refined, source-oriented soil management framework.
Cyclospora cayetanensis is an important foodborne parasite worldwide, with fresh produce and contaminated irrigation water as major transmission vehicles. In South Asia, environmental surveillance data remain limited. We investigated the occurrence of C. cayetanensis DNA in fresh produce and irrigation water across peri-urban areas of Khyber Pakhtunkhwa, Pakistan, and assessed environmental and farm-level factors associated with contamination. A cross-sectional study was conducted in Peshawar and Kohat districts from April to September 2025. A total of 420 samples were collected, including 300 fresh produce samples (six commonly consumed vegetables and herbs) and 120 irrigation water samples from canal, tube-well, and mixed sources. Samples were processed using concentration techniques, and detection was performed by nested PCR targeting the 18 S rRNA gene. Structured field questionnaires were used to capture farm-level practices, and logistic regression was applied to identify risk factors. We detected C. cayetanensis DNA in 6.0% of produce (18/300) and 12.5% of irrigation water (15/120; p = 0.028). Canal water (20.0%) was more frequently contaminated than tube-well water (5.0%; OR 4.75; 95% CI: 1.01-22.3). Leafy vegetables and herbs had higher contamination than smooth-surfaced produce (8.0% vs. 2.0%; p = 0.009). In multivariable analysis, canal irrigation (aOR 3.41), proximity to drainage channels ≤ 50 m (aOR 3.98), and use of untreated rinsing water (aOR 2.91) remained independently associated with contamination (all p < 0.05). This study provides among the first molecular evidence of C. cayetanensis contamination at the produce-water interface in peri-urban Khyber Pakhtunkhwa, Pakistan, identifying surface irrigation and poor water management as key risk factors. However, because PCR detects DNA rather than viable organisms, these findings indicate environmental contamination and potential exposure pathways rather than direct infection risk. Sequencing confirmation is needed to exclude cross-amplification of related coccidia.
Identifying the optimal spatial scale for landscape management is crucial for effective water quality improvement, specifically regarding whether the watershed scale or the riparian buffer scale is more effective. To address this, we conducted a national-scale analysis of the relationships between water quality and landscape-geoclimatic metrics across 441 watersheds in China, utilizing a multi-scale framework encompassing eight distinct spatial scales. These included four watershed scales (circular areas with radii of 10, 25, 50 km, and the entire watershed) and four riparian buffer scales (1-km wide buffers extending 10, 25, 50 km upstream, and the entire reach). The results indicated that geoclimatic conditions explained 18%-23% of the water quality variation, while landscape metrics explained 17%-32%. Crucially, we found that the riparian buffer scales provided explanatory power (40%-50%) nearly equivalent to that of the watershed scales (40%-49%), despite occupying a significantly smaller area. A distinct scale-dependent trade-off was identified: landscape composition dominated water quality variation within riparian buffer scales, whereas landscape configuration was more influential at the watershed scales. Our findings suggest that prioritizing landscape management within riparian buffers offers an efficient pathway that yields water quality improvements comparable to whole-watershed interventions, providing a precise spatial strategy for environmental planning under resource constraints.
Artificial turf systems have become increasingly widespread in urban environments, providing durable and weather-independent surfaces for a wide range of sports. Yet, their multi-material composition poses critical challenges for sustainable end-of-life management. A growing number of industrial actors are developing recycling solutions, but these efforts remain fragmented and only partially documented. The present review examines the current state of artificial turf recycling through a systematic literature review complemented by expert interviews with industry stakeholders. The holistic examination of contemporary artificial turf recycling processes reveals that the separation of major components-namely turf carpet, performance infill, and stabilising infill-along with their diverse polymer fractions, is imperative to achieve high-quality secondary materials. Nevertheless, this process remains technologically and economically demanding due to the high complexity and variety of products. Current practices often face limitations in terms of recycling efficiency, transport logistics, and the lack of standardised data, which in turn affects recycling practice, life cycle assessments and policy evaluation. This review makes a significant contribution to the field by providing a detailed mapping of European practices at a level of technological detail not previously published. It highlights challenges and identifies research gaps concerning materials, products, processes and assessment methodologies. It provides a more profound understanding of the environmental implications of artificial turf recycling and outlines pathways for advancing sustainable waste management strategies in the artificial turf sector. Furthermore, it addresses the internal tensions within this highly competitive industry.
Agricultural systems vary worldwide due to location, socioeconomic conditions, and management practices. Understanding the patterns and key factors that lead to differences between farms is crucial to providing the best support for their development. In our study, we interviewed farmers who operate small-scale dairy farms (with 1-5 cows) in a major agricultural region of Poland specifically, the voivodships Małopolskie and Podkarpackie, to understand how socioeconomic factors and farm management practices shape the diversity of farm profiles, and we evaluated our results using cluster analysis. Our findings confirmed the diversity among small-scale farming systems, allowing the identification of three distinct farm profiles. One of these clusters reflects part-time farmers characterized by low agricultural incomes, another long-standing peasant or hobby farms, while the smallest cluster referred to more professionalized agricultural systems. The key factors that characterized the different farm profiles were related to animal production (i.e. grazing time), infrastructure (i.e. manure storage facilities), farm size (i.e. land area), farming practices (i.e. mineral fertiliser use) and farmer's experience. The analysis also identified various socioeconomic factors that influenced the prospects for the continuity of different farm profiles, pointing out the need to devise support policies very specifically addressing the respective needs.
This study explores the novel application of artificial neural networks to predict polycyclic aromatic hydrocarbons (PAHs) pollution in urban road dust by integrating magnetic properties, particle size distributions, and urban environmental features. A comprehensive dataset from 284 samples from Warsaw, Poland, included magnetic susceptibility (χ), saturation magnetization (Ms), remanent magnetization (Mrs), traffic intensity, granulometric fractions, and parameters such as building height, building layout, connection to the municipal central heating network, and geospatial coordinates. Principal component analysis (PCA) revealed that ∑PAH16 accumulation patterns are governed by the interplay between magnetic proxies (χ, Ms​, Mrs​), traffic intensity (T), and urban structural configurations, specifically heating grid status (C), building height (H), and building continuity (B; attached vs. detached structures), collectively accounting for 60.21% of the total variance. The predictive performance of the models was evaluated using 5-fold cross-validation. While the Linear Regression (LR) model showed low and unstable accuracy (R2 ranging from 0.05 to 0.32, mean 0.18), the Random Forest (RF) model provided a significantly more robust framework for capturing the nonlinear relationships between variables. SHAP (SHapley Additive exPlanations) analysis was employed to interpret the RF model, revealing that grain size fraction (F) and geospatial coordinates (LA, LO) were the primary drivers of PAH accumulation. In contrast, factors such as traffic intensity and building layout exhibited a marginal influence. The comparison of modeling approaches revealed a progressive increase in predictive performance as the ability to capture nonlinear and local relationships improved (R2 =≈0.18 for linear regression, ≈0.26 for ANN, and ≈0.40 for RF), indicating that PAH accumulation is governed by complex, context-dependent interactions rather than simple independent predictors. These findings demonstrate that integrating magnetic properties and urban features using machine learning provides a powerful tool for identifying pollution hotspots and understanding the complex mechanisms underlying the distribution of organic pollutants in urban environments.
Long-term intensive tillage has reduced soil organic carbon (SOC) and threatened sustainable maize production in Northeast China. Yet how tillage regulates SOC-yield relationship across climatic gradients remains unclear, limiting climate-smart cropland management. Here, we combined field observations with the DSSAT model to simulate long-term effects of conventional tillage (CT) and no-tillage (NT) on SOC and maize yield (MY) across the maize belt of Jilin Province, and qualified SOC-MY synergy/trade-off along aridity gradients. NT consistently increased SOC region-wide and improve MY on average, but yield benefits showed clear climatic thresholds. Yield gains were concentrated in moderately dry environments with intermediate water-heat availability (aridity index, AI ≈ 0.28-0.40). In humid zones (AI > 0.67), NT still promoted SOC accumulation but often failed to sustain MY, indicating a shift from co-benefit to trade-off. The area exhibiting simultaneous SOC and MY increases expanded rapidly during early adoption, stabilized in years 5-20, and gradually decline thereafter. These results demonstrate that NT co-benefit depend jointly on climate suitability and adoption stage, providing transferable thresholds for climate-informed promotion of conservation agriculture in temperate maize system.
Paddy soils derived from basalt weathering contain high levels of Fe-Mn oxides, along with elevated nickel (Ni) and chromium (Cr), posing threats to rice safety. Unlike Fe oxides, Mn oxides exhibit both adsorption and oxidation capabilities, creating complex regulatory mechanisms for Ni and Cr. The environmental impacts of these oxides depend on their spatial distribution, though the mechanisms remain unclear. This study investigates the synergistic regulation of δ-MnO2 on the speciation transformation and bioavailability of Ni and Cr. Pot experiments were setup using δ-MnO2 distributed either in the rhizosphere or sub-root layers, combined with continuous or intermittent flooding water management. Results show that δ-MnO2 spatial distribution critically influences the distinct environmental behaviors of Ni and Cr. For Ni, δ-MnO2 exhibits adsorption and immobilization effect, but these effects are strongly dependent on the position: distribution in the rhizosphere reduces the concentration of available forms and decreases Ni accumulation in rice grains, while distribution in the sub-root layer hinders downward Ni migration and increases grain Ni accumulation. For Cr, δ-MnO2 primarily converts inert Cr(III) into highly reactive Cr(VI) through oxidation, resulting in increased Cr accumulation in grains. Water management and the spatial distribution of δ-MnO2 show significant synergistic effects: continuous flooding promotes Ni release and Cr(VI) reduction, while intermittent flooding favors Ni adsorption and immobilization. This study challenges the conventional understanding that "metal oxides universally exhibit immobilization effects on heavy metals", clarifying the differential regulatory roles of Mn oxide spatial distribution in paddy soil profiles on the environmental behaviors of Ni and Cr. It reveals the "double-edged sword effect" of Mn oxides in adsorbing/immobilizing Ni while oxidizing/activating Cr, and elucidates the core principle that neglecting their vertical distribution would lead to counterproductive heavy metal control measures. The findings not only provide new insights into the mechanisms by which Mn oxides regulate Ni and Cr accumulation in rice within basalt weathering zones, but also offer scientific and theoretical support for precise management of rice safety production in geologically high-background regions based on the differential properties of heavy metals.
The co-incineration of municipal solid waste (MSW) with sewage sludge (SS) is pivotal for urban waste-to-energy strategies, yet its operational instability poses significant challenges for environmental management, leading to incomplete combustion and elevated pollutant emissions. This study investigates how to minimize its environmental footprint by optimizing key operational parameters. A validated three-dimensional full-scale unsteady-state model of a 500 t/d mechanical grate incinerator was developed to simulate the real incineration process. It systematically quantified the impact of fuel heterogeneity on combustion stability and pollutant generation under different sewage sludge blending ratios, sewage sludge moisture content, and primary air distribution ratio. Results demonstrate that exceeding a sewage sludge blending ratios of 7 % induces calorific value dilution and stratified combustion, shifting the drying zone outward by 0.2-0.6 m and increasing the risk of incomplete combustion. Similarly, sewage sludge moisture content above 40 % extends the main combustion zone by 0.8-2.5 m, substantially raising CO emissions. Critically, this study proposes and validates an optimized primary air distribution ratio scheme (1.2:1.5:2.5:2.5:1.2:1.1) as a process intensification strategy. This management lever effectively enhances fuel drying and reactor environment, achieving a carbon burnout rate of 99.4 % and reducing CO emissions to 0.006 % in the MSW/SS co-incineration process. This work translates complex combustion mechanisms into actionable operational thresholds and control strategies, providing a robust simulation-driven framework for plant managers and policymakers to optimize co-incineration performance, minimize environmental footprint, and advance sustainable waste management.
As a major sink of heavy metals, wastewater sludge requires management to avoid environmental and health risks, while conventional treatments (e.g. chemical leaching and precipitation) remain dependent on chemical additives. Leveraging the alkalinity variations inherent to nitrification and denitrification, we present a fully biological, chemical-free strategy for solubilizing metals from sludge to leachate, and subsequent elimination in the leachate together with nitrogen. In the first stage, nitrification, driven primarily by acid-tolerant ammonia-oxidizing bacteria (AOB), oxidized ammonium, consuming sludge alkalinity and reducing sludge pH to ∼2.0, enabling efficient metals solubilization (Cu 85.6%, Zn 95.2%, Mn 85.2%, Al 73.5%). In the second stage, the acidic leachate underwent further treatment in a continuous methane-based membrane biofilm reactor (MBfR) enriched with nitrite/nitrate-dependent anaerobic methane-oxidizing (n-DAMO) microorganisms ('Ca. Methylomirabilis': 1.41% and 'Ca. Methanoperedens': 2.34%). This process removed > 98.0% of total nitrogen at a rate of 753.3 ± 31.1 mg N/(L d). Due to the alkalinity produced during denitrification, the pH of MBfR was automatically raised to ∼8.0, facilitating > 95.0% precipitation of solubilized metals. This work demonstrates a novel technical pathway for sludge management utilizing new nitrogen-cycling microorganisms and abundant nitrogen embedded in sludge, offering an environmentally sustainable pathway for integrated nitrogen and metal management in wastewater sludge.
Heterogeneous agro-ecological factors, insect breeding, and climate change are serious challenges to sustainable agricultural management. The study proposes a graph-enhanced meta-adaptive federated learning framework (GNN-ML-FRL) to address the challenges in precision agriculture. The proposed framework integrates Federated Learning (FL) for collaborative training of models in a decentralized manner across geographically distributed farms, Meta-Learning (ML) for rapid adaptation to changing environmental factors, and Graph Neural Networks (GNNs) for capturing spatial dependencies among agricultural entities. A comprehensive multivariate IoT environmental dataset with 52.56 million time-series observations gathered from 500 dispersed sensors over a 12-month period, the IP102 insect pest recognition benchmark (75,222 images across 102 species), and curated genomic datasets from MaizeGDB and the Rice Annotation Project Database for genotype-informed modeling are the three standardized datasets used to assess the framework. Experimental results show statistically significant improvements (p < 0.01) over CNN and graph-based baselines, achieving 89.3% Top-1 accuracy, 7.8% higher generalization performance, and 12.4% reduction in prediction loss across geographically unseen farms. SHAP-based explainability further indicate that environmental accuracy-related features contributed nearly 63% positive influence, while loss-related factors contributed 37% negative influence, validating model robustness. Geographic generality is confirmed by site-out validation using IoT data, and resilience is improved under varied crop conditions by genotype-informed graph modeling. The findings show that a scalable and statistically sound framework for data-driven pest identification and environmental modeling in precision agriculture may be achieved by combining spatial graph reasoning, meta-adaptive learning, and decentralized training.
With coral reefs increasingly threatened by rapid environmental changes, understanding genetic diversity at microgeographic scale is critical for assessing their capacity to respond to local stress regimes. Theory for continuous populations predicts that brooding corals with restricted dispersal should exhibit fine-scale genetic structure and isolation-by-distance, yet such patterns remain poorly resolved in marginal and environmentally extreme reef ecosystems. Here, we investigated the genetic structure of the catch bowl coral, Isopora cf. palifera, across 11 sites within ~ 14 km in Kenting National Park (KNP), southern Taiwan, a reefscape characterized by strong small-scale environmental heterogeneity, including chronic thermal influence from a nuclear power plant and tidally driven upwelling. We genotyped 466 colonies (six microsatellite loci yielding 302 unique multilocus genotypes) and sequenced nuclear PaxC 46/47-intron from 322 colonies of I. cf. palifera. Microsatellite data revealed strong genetic structure (K = 2, K = 5): principal coordinate analyses identified four geographic groupings, and Bayesian clustering (STRUCTURE) supported two major clusters separating Nanwan (plus Tantzei Bay) from the remaining coastal sites, with one site (Shiaowan) showing admixture. The PaxC marker resolved ten haplotypes, with H1 widespread, H2 concentrated along Nanwan, and H3 dominant at thermally influenced sites near the nuclear power plant outfall. Overall, populations showed high site differentiation, significant isolation-by-distance, and high self-recruitment (68-92%), indicating limited effective dispersal. A temporal comparison (2000-2015) at Tantzei Bay indicated stable genetic structure through time despite repeated regional disturbances. Generalized estimating equation (GEE) models showed that site-level seawater temperature was positively associated with both host haplotype composition (GEE; coefficient = 0.0479, p < 0.001) and Symbiodiniaceae genera (GEE; coefficient = 0.0462, p < 0.001, symbiont data from a previous work in KNP), suggesting non-random host-symbiont-environment associations at microgeographic scale. Together, these results indicate that I. cf. palifera in KNP exhibits pronounced fine-scale genetic structure consistent with restricted dispersal and possible microgeographic adaptation of the holobiont to local thermal regimes. While such structuring may enhance local resilience by maintaining diverse, site-specific host-symbiont combinations, it also implies limited scope for rescue via gene flow if future warming pushes populations beyond their adapted tolerances. Our findings underscore the importance of accounting for microgeographic genetic structure and local adaptation when designing management and conservation strategies for reefscape such as those in KNP.
Global climate change is rapidly impacting biodiversity and threatening the sustainable use of medicinal plant species by reducing their availability and increasing harvest uncertainty. Understanding the adaptive genetic variation and genetic vulnerability of medicinal plants under climate change is crucial for effective germplasm management, cultivation, and breeding efforts. In this study, we assessed the genetic differentiation, local adaptation, and genomic vulnerability of the medicinal plant Isodon rubescens (Hemsl.) H. Hara, with the goals of elucidating the impacts of geographic and environmental factors on its genetic structure and identifying at-risk populations for informed conservation and breeding under climate change. We applied restriction site-associated DNA sequencing (RAD-seq) to 17 populations of I. rubescens spanning its central and peripheral ranges, including the Taihang and Qinling-Funiu Mountains. The analysis revealed two distinct genetic groups: one in the Taihang Mountains and the other in the Qinling-Funiu Mountains. Significant patterns of isolation by distance (IBD), environment (IBE), and resistance (IBR) were detected, alongside high niche differentiation. We identified 456 candidate adaptive SNPs, some linked to genes involved in stress responses and biosynthesis. Precipitation was a key environmental driver of local adaptation. Populations in the northern Taihang Mountains and southern Funiu Mountains showed higher genomic vulnerability, indicating a greater risk of maladaptation. Our findings demonstrate that geographic isolation and environmental factors, particularly precipitation, are key drivers of genetic differentiation and local adaptation in I. rubescens. The identified genomic vulnerability pinpoints specific populations at high risk under climate change. These insights provide a crucial genetic basis for formulating targeted conservation strategies and developing climate-resilient breeding programs for this medicinal species.
In the context of global digital transformation, digital sustainability is emerging as a crucial yet underexplored trend in the field of entrepreneurship. Based on the Stimulus-Organism-Response model, this study surveyed 1066 students to clarify the mechanism of forming digital-oriented sustainable entrepreneurial intention. The findings indicate that institutional support plays a more effective stimulating role than entrepreneurship education, significantly shaping both environmental values and digital competencies. The results indicate that the joint influence of digital competencies and environmental values plays a key role in fostering digital-oriented sustainable entrepreneurial intention. Moreover, knowledge of Sustainable Development Goals positively moderates the link between digital competence and entrepreneurial intentions but does not affect the relationship between environmental values and intentions, reflecting differences in the cognitive transformational role of information flows.
Hyperosmolar-hypernatremic dehydration (HHND) is a life-threatening yet preventable neonatal condition, often due to inadequate breastfeeding. The recent North Indian heat wave heightened dehydration risks, necessitating an evaluation of extreme temperatures' impact on neonatal hydration. This retrospective study analysed neonates admitted to a tertiary care level 3 neonatal intensive care unit (NICU) at AIIMS Jodhpur between April and June 2024. Case records were reviewed, and details on maternal age, feeding practices, presenting complaints, biochemical profile, and outcome were studied. The 2024 (April-May) heat wave led to a threefold increase in NICU admissions for HND compared to the previous 2 years, with cases rising from 2 to 3 per year to 10. Primigravida mothers accounted for 70% of the cases. The mean age of presentation was 6.7 days. Affected neonates experienced weight loss ranging from 11% to 33%, with serum sodium levels between 149 and 185 mEq/l and plasma osmolarity reaching 370-450 mOsm/l. Six neonates required peritoneal dialysis (PD) due to encephalopathy/anuria. One developed aortic thrombosis with lower limb gangrene, necessitating thrombolytic therapy. MRI abnormalities were observed in one case. Despite intensive management, one neonate succumbed to sepsis. Extreme environmental heat significantly heightens the risk of hyperosmolar-hypernatremic dehydration (HND) in neonates. Proactive neonatal monitoring, early breastfeeding support, and parental education are critical to preventing dehydration and its complications, especially in tropical and resource-limited settings, where extreme heat, early discharge, and limited lactation support increase neonatal vulnerability. Judicious fluid management targeting plasma osmolarity and timely intervention with PD in severe cases can optimize survival and neurological outcomes, underscoring the need for heightened vigilance during heat waves.
Coastal marshes, recognized as effective organic carbon (OC) sinks, have gained attention for their potential contribution to climate mitigation through protection and restoration. However, the climate mitigation potential of Nordic coastal marshes remains understudied, likely due to their heterogeneous and often non-tidal nature. To fill this gap, we examined soil OC storage and accumulation rates, and the effects of grazing, a common management practice, across eight Nordic coastal marsh areas spanning broad climate and environmental gradients. We also assessed soil methane emissions in selected areas. The Nordic marshes studied store a median of 7 kg OC m-2 (interquartile range, IQR: 6-8) in the top 15-35 cm of soil and accumulate 41 g OC m-2 yr.-1 (IQR: 32-47). Considering only the additional OC, attributed to the presence of the marsh habitat, these values drop to 4 kg OC m-2 (IQR: 2-6) and 21 g OC m-2 yr.-1 (IQR: 11-33). Globally, both rates are comparatively low. OC stocks and accumulation rates increased with marsh age, root: shoot ratio (stress adaptation), and δ15N (fast N cycling), but declined with soil δ13C (related to faster decomposition under warmer conditions and sandier soils). Danish marshes had the highest but also most vulnerable OC stocks due to faster turnover, labile compounds, and coarser soil grain sizes. Although grazing only weakly increased soil OC stocks and had no effect on OC accumulation rates, it significantly reduced methane fluxes compared to ungrazed marshes. In ungrazed areas, methane emissions weakened the carbon sink by 32% in Finland and 68% in Denmark. However, estimated greenhouse gas emissions from on-site cattle, even at low grazing intensity, largely outweighed the coastal marsh climate benefits. A comprehensive Nordic marsh management strategy is needed, extending beyond the focus on their limited, yet relevant, role in climate mitigation, and considering biodiversity, coastal protection and nutrient retention.
Falls are the leading cause of accidental injury among older adults, 30% of community-dwelling adults aged 65 and over fall each year, with nearly half occurring outdoors. These falls are complex, understudied, and insufficiently addressed in current age-friendly cities or walkability frameworks. This study aimed to build interdisciplinary consensus on risks, preventive actions, and barriers to fall prevention in outdoor public spaces through a Delphi process. A three-phase Delphi study was conducted with 64 participants in round 1, 60 in round 2, and 49 in round 3, including four expert groups: older adults who had fallen outdoors, health and research professionals, urban planners, and decision-makers (local and regional policy-makers, elected officials, and public-space managers involved in urban planning). Phase one collected open responses on risks, preventive actions (modification of physical layout, public-space management, and behavior-related factors), and barriers to these actions. Responses were synthesized using AI-assisted analysis with systematic human validation. In phases two and three, the relevance of 124 propositions were rated on a 10-point Likert scale. Consensus was defined as ≥ 70% of ratings ≥ 7/10 and interquartile range ≤ 2.5. Consensus was reached for key intrinsic factors such as gait and balance impairments, visual and vestibular deficits, cognitive decline, and polypharmacy, as well as for environmental factors including irregular or inappropriate surfaces, obstacles, or signage, and crowding. Highly relevant preventive actions included integrating fall prevention into street and sidewalk design, training urban planning professionals, awareness campaigns, systematic maintenance, safer crossings, participatory co-design public-space adaptations and urban design features involving older adults and local stakeholders, and improved data monitoring through surveillance, mapping, and sharing of fall-related and environmental risk information. Main barriers were insufficient budgets, high costs, limited integration of fall prevention into planning priorities, and lack of evaluation of the impact of implemented actions. Outdoor fall prevention is a transversal challenge requiring integration of public health and urban planning. This Delphi highlights actionable priorities to embed fall prevention in local and national strategies, in particular in rapidly aging regions.
Waste from metal mining is a global and escalating issue. Risks are particularly severe for reactive minerals, which, upon exposure to oxygen, water and/or microbial activity, can cause widespread contamination (e.g., acid drainage from sulfide oxidation). This study investigates the nature of colloids within historic U-REE-Cu-rich mine wastes from Mount Painter in the Northern Flinders Ranges, South Australia. The primary mineralogical hosts of uranium (torbernite) and rare earth elements (monazite-(Ce)) are phosphate minerals, which are insoluble phases typically considered to limit U and REE mobility in groundwater. However, single particle ICP-MS analysis revealed substantial concentrations of polymetallic nanoparticles enriched in U-REE-(Fe) and concentrated within the surface layers (0-10 cm) of the waste. Microbial diversity is highest near the surface, which is interpreted to promote the dissolution of phosphate minerals and the transformation of liberated metals into nanoparticles. This correlation suggests the potential for microbial consortia to extract metals from stable minerals and transform them into environmentally mobile colloidal forms, with significant implications for the biogeochemical cycling and environmental management of metals such as U and REE released by mining of both base metals and critical minerals.
The grape white rot, caused by Coniella vitis, is a major threat in Chinese vineyards. Still, the environmental determinants governing the infection process are poorly understood, hindering the development of risk-based control strategies. This study investigated how temperature, wetness duration, and wetness interruption jointly influence infection in two grapevine cultivars (Chardonnay and Riesling) at berry development and maturation stages. Disease severity was significantly higher in mature berries than in developing berries, with Chardonnay being consistently more susceptible than Riesling. Optimal conditions for infection were 25°C, with 18-24 h of wetness, under which disease severity exceeded 90% at berry maturity. A temperature-wetness response equation accurately described infection dynamics at both berry growth stages (R2 ≥ 0.893) and was used to construct infection risk classification charts. There was a high infection risk in mature berries when the temperature ranged from 16°C to 34°C. The critical wetness duration required for infection was 2 hours. The temperature range for high infection risk in developing berries was narrow (14-28°C) and the wetness threshold long (≥12 h). Wetness interruption significantly suppressed infection, particularly during berry maturation, when extended dryness of 2-12 h reduced disease severity by up to 31.7%. A wetness-interruption equation was developed (R2 ≥ 0.927) to effectively quantify the inhibitory effect of dryness duration on berry infection. This study elucidates the interactive roles of temperature, moisture continuity, and dryness intervals in driving C. vitis infection, and provides mathematical equations to support the development of mechanistic models for a precision management of grape white rot.