Search Results (7,396)

Urbanization underscores the critical role of the built environment in shaping human health outcomes. Recently, technology-driven assessment enables a more precise, dynamic, and objective evaluation of individuals’ biobehavioral responses to built environments and their health. However, existing reviews are limited to single technologies, single health outcomes, or specific environmental features. As a result, this narrative review summarizes 269 studies (2003–2025) to examine how such technology-driven methodologies capture the effects of built environments on psychophysiological well-being. Findings reveal a four-stage evolution in methodology from subjective evaluations and single-device monitoring to integrated subjective-objective measures and, more recently, multimodal synergistic frameworks. Accordingly, based on a technology-driven assessment of biobehavioral responses, this review synthesizes a dual-pathway framework linking the built environment to health: (1) psychological responses are mediated through emotion-arousal mechanisms, encompassing 22 key emotions across both positive and negative valences; and (2) physiological outcomes are influenced by behavioral–psychological mediation and direct environmental exposure, encompassing six categories that span from subclinical dysfunction to clinical disease risk. This review thereby provides a framework derived from the reviewed evidence that connects built environments to health through measurable biobehavioral pathways, directly supporting human-centered urban design and assessment. Full article

The operationalization of the One Health (OH) approach remains a major challenge due to persistent fragmentation across human, animal, and environmental data systems. This gap is exacerbated by climate change, which acts as a risk multiplier for pathogen transmission and agri-food system vulnerability. Drawing on more than a decade of research, including the re-emergence of brucellosis in Italy and the 2024 Salmonella Umbilo outbreak, this perspective discusses key weaknesses in current data management, particularly the lack of real-time, interoperable data sharing. To address these challenges, we propose an AI-enabled One Health Information System (OH-IS), grounded in FAIR data principles and privacy-preserving architectures. The proposed conceptual framework integrates multi-matrix data streams, combining Earth observation data, genomic surveillance through whole-genome sequencing (WGS), and livestock mobility within a geospatially integrated architecture to support timely decision-making in vulnerable settings. By analyzing the constraints of siloed databases, we discuss how automated semantic harmonization could conceptually support improved risk assessment and outbreak reconstruction in recent zoonotic events. This approach may facilitate a transition from descriptive to anticipatory surveillance, providing a scalable model to move One Health from a conceptual paradigm toward a more integrated and data-driven surveillance framework aligned with EU digital health policies and global health security priorities. Full article

Accurate spray timing is essential for reducing unnecessary pesticide use in irrigated cotton production. This study developed and evaluated a locally calibrated six-stage Spraying Phase (SP) scale for the Karshi/Qashqadaryo production context. The scale was established through a two-round moderated consensus process involving 16 expert panelists representing this production context. A screened dataset of 14,400 non-standardized smartphone images was used to train and evaluate a ResNet-50 convolutional neural network (CNN) for SP-stage classification. Field validation was conducted at the Karshi Engineering and Economics Institute during the 2023 and 2024 seasons using an internally controlled randomized complete block design (RCBD)-style paired comparison of SP-based and BBCH-based spray timing. The CNN achieved 93.0% test accuracy. The mean number of pesticide applications was descriptively lower under SP-guided scheduling than under BBCH-based scheduling (3.75 versus 4.88 applications per season; −23.1%). For the inferentially evaluated outcomes, crop-protection cost decreased by 21.2%, the Environmental Risk Index decreased by 21.6%, and plot-level lint-equivalent yield increased by 4.5%. These findings support SP-guided timing as a promising locally calibrated decision-support approach under the tested Karshi/Qashqadaryo conditions; broader use requires multi-site, multi-cultivar, multi-season, device-stratified, and BBCH-level validation, together with technical deployment testing and implementation-cost assessment. Full article

Background/Objectives: Inappropriate medication storage and disposal are recognized public health concerns and represent a relevant source of environmental exposure to pharmaceutical compounds, with potential implications for ecosystem and human health. Identifying population-level determinants of these behaviors is essential to support targeted interventions and risk mitigation strategies. Methods: A cross-sectional population-based study was conducted using a structured survey administered to Brazilian adults. The questionnaire assessed medication storage practices, disposal behaviors, and prior exposure to guidance on appropriate medication handling. Descriptive statistics were used to characterize the sample, and multivariate logistic regression analysis was performed to identify independent predictors of appropriate disposal practices. Results: A total of 475 participants were included in the analysis. Although awareness of the environmental impact of improper medication disposal was high, inappropriate practices remained prevalent. Multivariate logistic regression identified educational level as the primary independent determinant of appropriate disposal practices (p < 0.001), while no significant associations were observed for age or gender (p > 0.05). Lack of prior guidance was frequent and may represent a relevant population-level exposure influencing unsafe behaviors. Conclusions: The findings highlight a gap between awareness and practice and identify key sociodemographic determinants influencing medication disposal behaviors. From an environmental epidemiology perspective, improper disposal may contribute to the dissemination of pharmaceutical residues in water systems, representing an indirect but widespread exposure pathway. Targeted public health strategies focusing on high-risk groups are needed to promote safe medication handling and reduce environmental and health risks. Full article

Against the backdrop of the rapid development of smart agriculture, pest and disease monitoring and crop growth assessment for large-scale farmlands are of substantial importance for precision management and risk early warning. However, traditional unimodal visual methods are highly susceptible to illumination variation, canopy occlusion, scale differences, and background interference in real field environments, and thus fail to make full use of environmental sensing information and spatial priors. To address these issues, a multimodal target perception framework for intelligent farmland inspection is proposed in this study. By jointly integrating UAV imagery, time-series data from ground Internet of Things sensors, and spatial positional information, joint modeling of pest and disease recognition and crop growth assessment is achieved through cross-modal alignment and collaborative encoding, multi-scale target perception, and dynamic multimodal fusion and decision-making. Experimental results demonstrate that, in the pest and disease recognition task, the proposed method achieved a Precision of $91.63\%$, a Recall of $90.27\%$, an F1-score of $90.94\%$, and an mAP of $93.15\%$, significantly outperforming comparison models such as Faster R-CNN with ResNet50 backbone, YOLOv8-m, Swin Transformer-Tiny, and Multimodal Transformer. In the crop growth assessment task, an Accuracy of $89.96\%$, a Precision of $89.11\%$, a Recall of $88.74\%$, and a Macro-F1 of $88.92\%$ were achieved, again clearly exceeding those of ResNet50, EfficientNet-B3, ViT-B/16, and conventional multimodal fusion models. The ablation study further verified the effectiveness of the cross-modal alignment module, the multi-scale target perception module, and the dynamic fusion module, with the complete model reaching $90.94\%$, $93.15\%$, and $88.92\%$ in Pest F1, Pest mAP, and Growth Macro-F1, respectively. Furthermore, the net economic return regression experiment at the unit-area level further demonstrates that the proposed method can effectively connect state information with economic outcomes, showing strong application potential in return prediction, performance evaluation, and resource allocation optimization. These findings indicate that the proposed method can effectively improve perception accuracy and robustness in complex farmland environments, thereby providing reliable technical support for intelligent inspection, pest and disease early warning, and precision management in agricultural scenarios. Full article

This study examines how the development of renewable energy influences environmental security and climate resilience in Ukraine amidst structural disruptions and external shocks spanning the period 2021–2025. The research is based on a systemic analytical framework that combines indicator-based assessment, statistical time-series analysis, and econometric modeling. Renewable energy is conceptualized as a structural factor that transforms the energy balance and affects emission intensity, environmental risks, and the operational stability of the energy system. A coherent system of indicators is developed to capture the causal chain linking renewable energy expansion with environmental and systemic outcomes. The dynamics of key indicators demonstrate that the increase in the share of renewable energy does not lead to a linear reduction in absolute emissions due to the strong influence of changes in electricity generation volumes, fuel structure, and wartime conditions. At the same time, intensity indicators reveal the presence of a decarbonization effect associated with renewable energy development. Correlation, differential, and regression analyses indicate that the relationship between renewable energy and carbon intensity is unstable in short time series and depends on structural and operational factors. The findings show that renewable energy contributes to reducing environmental pressure in the medium term, although its effect may be temporarily offset by crisis conditions. Full article

The widespread environmental presence of microplastics has led to their increasing co-occurrence with pesticides in agricultural soils, which raises concerns about their potential combined effects on pollutant behavior and toxicity. In this study, we investigated the environmental fate of boscalid and its toxicity to earthworms under co-exposure with two types of microplastics. Both polyethylene microplastics (PE) and polylactic acid microplastics (PLA) significantly enhanced boscalid retention in soil and delayed its degradation. Co-exposure impaired intestinal barrier function, promoted boscalid bioaccumulation, and triggered more severe oxidative stress and metabolic disturbances in earthworms. Notably, differences were observed between PE and PLA in their effects on boscalid behavior and earthworm responses. Our study suggests that microplastics may influence the ecological risk of boscalid through potential carrier effects and biological interface interactions and indicates mechanistic differences between conventional and biodegradable microplastics in modulating pesticide toxicity. These findings offer new insights into the environmental risk assessment of combined pollution. Full article

Cycling is a key component of sustainable urban mobility, yet cycling behaviours are influenced not only by infrastructure but also by psychological factors, particularly risk perception. This study examined how risk perception, pro-environmental attitudes, and cycling experience relate to cyclists’ behaviours in urban contexts. One hundred Italian cyclists completed questionnaires assessing cycling behaviours, environmental attitudes, perceived urban environment characteristics, and hazard perception. Bayesian regression analyses showed that pro-environmental attitudes were associated with safer cycling behaviours, while cycling frequency acted as a protective factor against violations. In contrast, risk perception did not significantly predict cycling errors. The findings suggest that violations and errors may rely on different psychological mechanisms, with violations linked to deliberative processes and errors more strongly associated with automatic cognitive and attentional processes. Overall, the study highlights the need for integrated interventions addressing not only risk awareness, but also environmental design, attentional demands, and motivational factors related to sustainability and social responsibility. Full article

Heavy-duty transit vehicles (N1–N3) (heavy vehicles) can generate disproportionate environmental and economic impacts in urban transport corridors despite representing a relatively small share of total traffic volume. This study develops an integrated probabilistic framework for assessing the relationships between traffic-flow variability, CO₂e emissions, particulate-matter-derived climate impacts, and external pollution costs associated with transit transport. The methodology combines traffic-flow modeling, emission estimation, PM-to-CO₂e transformation, probabilistic analysis, Monte Carlo simulation, sensitivity analysis, and scenario-based intervention assessment. Separate analyses were conducted for M1 passenger vehicles and heavy vehicles to evaluate differences in emission behavior, uncertainty, and economic impacts. The results indicate substantial structural differences between light-duty and heavy-vehicle regimes. Passenger-car traffic exhibited relatively stable emission distributions, whereas heavy vehicles demonstrated significantly greater variability, uncertainty, and emission intensity. Sensitivity analysis identified heavy-vehicle flow as the dominant factor influencing overall system emissions and pollution costs. Scenario analysis indicated that restrictions targeting heavy-vehicle traffic have the potential to generate considerably larger environmental benefits than generalized traffic-reduction measures. Probabilistic assessment further revealed that heavy vehicles contribute disproportionately to high-emission risk regimes and uncertainty propagation within the system. The proposed framework provides an integrated approach for evaluating climate impacts, uncertainty and economic externalities of transit transport. The results highlight the importance of heavy-vehicle management in reducing emissions and pollution costs while supporting risk-informed transport policy development. Full article

With over 140 species of primary freshwater fish, including 73 that are endemic, Greece is recognized as a biodiversity hotspot in Europe. However, like freshwater systems worldwide, these ecosystems face serious threats, particularly from the introduction of non-native species. This ongoing increase in invasive species has heightened scientific and policy-maker awareness, as such introductions can lead to population declines and even extinctions of native fish. In response to this growing concern, various risk assessment tools have been developed to evaluate the potential hazards posed by non-native species, both those already established and those likely to be introduced. These tools are critical for informing policy decisions and managing biological invasions effectively. In the current study, a new tool, the Hellenic Risk Assessment (HeRA), is proposed. Unlike its predecessors, HeRA places greater emphasis on assessing the biological traits of introduced species and incorporates region-specific considerations tailored to the Mediterranean basin. Its scoring system evaluates both the likelihood of a species establishing itself and its potential environmental impact, making it a valuable resource for stakeholders in prioritizing management actions and making informed decisions regarding the import of live fish species. Full article

This study addresses maritime traffic risks in the Labuan Bajo–Komodo marine tourism corridor, a spatially constrained archipelagic environment characterized by mixed vessel traffic, intensive tourism activity, and high ecological sensitivity. An integrated decision-support framework was developed by combining the Analytic Network Process (ANP) with stakeholder-supported grid-based spatial risk analysis. Expert pairwise comparisons from eight respondents were used to evaluate eight interdependent criteria: Natural Conditions, Navigational Channel, Vessel Factors, Maritime Traffic Conditions, Port Control, Authority/Stakeholders, Tourism, and Environmental Impact. The ANP calculation was conducted using geometric mean group aggregation, consistency ratio assessment, and targeted follow-up clarification for matrices requiring refinement. The final ANP results show that Port Control received the highest priority weight (0.172), followed by Natural Conditions (0.148), Maritime Traffic Conditions (0.144), Environmental Impact (0.135), Vessel Factors (0.121), Navigational Channel (0.120), Authority/Stakeholders (0.104), and Tourism (0.0566). At the global subcriteria level, communication effectiveness, channel complexity, environmental compliance, local traffic density, and seasonal traffic variation emerged as the dominant contributors to risk. A stakeholder-supported partial spatial risk index (SRI) was then calculated for 21 grid cells using spatially mappable ANP criteria. The highest-risk cells were grids 3, 5, 6, 8, 9, 10, and 14, while sensitivity analysis confirmed that grids 3, 5, 6, 9, 10, and 14 remained high risk across all tested spatial-weight scenarios. The findings indicate that maritime traffic risk in Komodo National Park is not driven by environmental exposure alone, but by the interaction of traffic control capacity, natural hazards, traffic concentration, environmental sensitivity, and institutional coordination. The proposed framework supports spatially informed traffic management, environmental compliance, and emergency preparedness planning in marine protected tourism corridors. Full article

Background: Burkholderia pseudomallei, the causative agent of melioidosis, infects diverse animal species and reflects environmental contamination. However, the global seroprevalence of B. pseudomallei in animals remains incompletely characterized. Methods: A systematic review and meta-analysis were conducted according to PRISMA guidelines and registered in PROSPERO (CRD420261306404). PubMed, Embase, and Scopus were searched for observational studies reporting seroprevalence of B. pseudomallei in animals. Random-effects meta-analysis was performed to estimate the pooled prevalence with 95% confidence intervals (CIs). Subgroup analyses were conducted by animal group, geographic region, diagnostic method, and indirect hemagglutination assay (IHA) cut-off value. Risk of bias was assessed using the Joanna Briggs Institute checklist. Results: Twenty studies involving 78,914 animals were included. The pooled seroprevalence of B. pseudomallei was 11% (95% CI: 6–19%), with substantial heterogeneity (I² = 98.1%, p < 0.0001). Wildlife showed the highest prevalence (16%; 95% CI: 10–25%), followed by livestock (11%; 95% CI: 6–19%). Significant geographic variation was observed (p < 0.0001), with higher prevalence reported in North America (18%) and Southeast Asia (10%). Seroprevalence estimates varied according to diagnostic method and IHA cut-off values. Sensitivity analyses yielded similar pooled prevalence estimates after exclusion of small studies, supporting the stability of the overall findings despite persistent heterogeneity. Conclusions: Exposure to B. pseudomallei is widespread among animal populations and influenced by geographic and methodological factors. Standardized diagnostic approaches and expanded animal surveillance are needed to improve understanding of melioidosis epidemiology within a One Health framework. Full article

Pollinators play a critical role in agricultural productivity and the maintenance of flowering plant diversity, yet their health is increasingly threatened by multiple environmental stressors. While research has traditionally focused on pathogens, pesticides, habitat loss, and nutritional limitation, fungal secondary metabolites, mycotoxins, remain an underappreciated risk factor. This review synthesizes current knowledge on the presence, exposure pathways, and biological impacts of key mycotoxins, including aflatoxin B1, ochratoxin A, deoxynivalenol, zearalenone, and T-2 toxin, in bee-collected pollen and bee bread. We discuss how contaminated food matrices act as reservoirs of chronic exposure, linking forager activity, nurse bee physiology, brood development, and colony-level outcomes. Evidence from laboratory studies highlights sublethal effects on survival, hypopharyngeal gland development, immunity, and gut microbiota, with potential interactions with pathogens, nutritional stress, pesticides, and climate change. Furthermore, we extend these insights to wild pollinators, emphasizing differences in colony size, diet breadth, and detoxification capacity. Analytical methods for detecting mycotoxins, including HPLC, LC-MS/MS, and ELISA, are evaluated in terms of sensitivity, specificity, and relevance to field exposure. By integrating environmental concentrations with laboratory toxicity thresholds, this review identifies critical knowledge gaps and proposes a mechanistic framework linking mycotoxin exposure to colony-level risk. The findings underscore the need for targeted monitoring, improved risk assessment, and multi-stressor evaluation to safeguard both managed and wild pollinator populations. Full article

Globally, wildfires are increasingly threatening forest ecosystems and human well-being, requiring proactive management strategies. Integrating wildfire mitigation with bioenergy production presents a dual opportunity to reduce fire risk while contributing to clean energy. This study builds upon previous work by incorporating updated annual heat load estimates from 32 off-grid communities in northern Canada to assess the amount of biomass at risk of wildfire that could be mobilized to meet local bioenergy needs. Our results reveal that energy consumption in the remote communities considered was previously significantly underestimated, with an average of 11,710 MWh per year, and a minimum and maximum of 1869 and 43,867 MWh per year, respectively. With the updated dataset, which includes both space heating and electricity energy usage, the average energy demand is approximately 300% higher than earlier estimates. Despite this substantial increase in energy consumption, the amount of biomass needed to meet local energy demand per year ranges from 352 to 8276 odt per year, representing only a small fraction (approximately 1.67% on average) of the total biomass identified as being at risk within a 10 km buffer. This corresponds to fuel treatment areas ranging from 4 to 222 hectares per year (around 51 ha on average), depending on the community. The results presented here, based on updated energy data, provide important insights into the operational feasibility of this approach. To be successful, implementation will require strong community leadership and collaboration with fire management agencies to design consistent and cost-effective fuel treatment strategies that are tailored to each community’s environmental conditions and energy needs. Full article

The risk of exposure to mycotoxins in livestock farming is still poorly characterized, particularly in Italy where human biomonitoring data are scarce. Livestock farms represent a high-risk setting due to frequent handling of contaminated feed and dust-generating activities. This pilot study applied a human biomonitoring approach to assess internal exposure to multiple mycotoxins among pig farmers in Southern Italy. Urinary biomarkers of aflatoxin B1 (AFB1), aflatoxin M1 (AFM1), ochratoxin A (OTA), and fumonisin B1 (FB1), together with oxidative stress biomarkers (8-oxo-7,8-dihydroguanine (8-oxoGua), 8-oxo-7,8-dihydro-2′-deoxyguanosine (8-oxodGuo), 8-oxo-7,8-dihydroguanosine (8-oxoGuo), 3-nitrotyrosine (3-NO₂Tyr), and 5-methylcytidine (5-MeCyt)), were measured in urine samples from 35 workers and 30 non-exposed controls. A sensitive and validated HPLC–MS/MS multi-mycotoxin method was developed and applied. Biomonitoring results were also discussed in relation to previous environmental monitoring. AFM1 emerged as the most frequently detected biomarker in the exposed group, with concentrations above the limit of detection (LOD) in 22.8% of samples; 11.4% exceeded the limit of quantification (LOQ). In contrast, only 10% of the control samples had values above the LOD and none exceeded the LOQ, suggesting a possible contribution linked to occupational tasks. This study provides original biomonitoring evidence of low-dose, mixed mycotoxin exposure among Italian swine farmers and highlights the value of integrating environmental and biological monitoring to improve occupational exposure assessment in livestock production systems. Full article