Search Results (14,055)

Fine particulate matter (PM_2.5) poses a significant global environmental health threat and is closely associated with diseases across multiple organ systems. This review systematically summarizes the toxic effects and underlying mechanisms of PM_2.5 in the respiratory, cardiovascular, nervous, immune, endocrine, digestive, and genitourinary systems. Key pathogenic processes involve shared pathways such as oxidative stress, inflammatory responses, endoplasmic reticulum stress, autophagy, and apoptosis, along with the activation of system-specific signaling networks. The complex composition and notable spatiotemporal variability of PM_2.5 present challenges for assessing its health risks and clarifying its mechanisms. Moving forward, integrating multi-omics and molecular epidemiology approaches will be essential to unravel its multi-system pathogenic networks and support the development of effective intervention strategies. Full article

The NA64 experiment at the CERN H4 beamline recently started a high-energy positron-beam program to search for light dark matter particles through a thick-target, missing-energy measurement. To fulfill the energy resolution requirement of the physics measurement ${\sigma }_E/E\simeq 2.5\%/\sqrt{E\left[\mathrm{GeV}\right]}\oplus 0.5\%$ and cope with the constraints and performance requests of the NA64 setup, a new high-resolution homogeneous electromagnetic calorimeter PKR-CAL has been designed. The detector is based on PbWO₄ crystals, each read by multiple SiPM sensors to maximize the light collection. The PKR-CAL design has been optimized to mitigate and control unavoidable SiPM saturation effects at high light levels, as well as to minimize the gain fluctuations induced by instantaneous variations of the H4 beam intensity. The R&D program culminated in the construction of a small-scale prototype, POKERINO. In this work, we present the results from the experimental characterization campaign of the POKERINO, aiming at demonstrating that the obtained performances are compatible with the application requirements. Full article

Alzheimer’s Disease (AD) is the leading cause of dementia worldwide, and early diagnosis is crucial to minimize neurological damage and loss of quality of life. Here, we report an electrochemical biosensor for detecting miRNAs 29a and 34a, potential non-invasive biomarkers associated with AD. The biosensor consisted of a glassy carbon electrode (GCE) modified with a novel nanohybrid of gold nanoparticles stabilized by 3-n-propyl(4-dimethylaminopyridinium) silsesquioxane chloride (AuNPs–Si4DMAP⁺Cl⁻). Thiolated anti-miRNA probes were immobilized separately on the GCE/AuNPs-Si4DMAP⁺Cl⁻, followed by BSA blocking. Target miRNAs were detected via hybridization with complementary probes using electrochemical impedance spectroscopy. The nanohybrid, characterized by spectroscopic and morphological techniques, significantly enhanced the electrochemical response and was effective detecting both miRNAs, showing suspension stability over 600 days. LOD and LOQ were 1.79 pM and 5.87 pM for miRNA-29a, and 2.21 pM and 11.01 pM for miRNA-34a. These results highlight the platform’s potential for electrochemical detection of these miRNAs in blood, supporting earlier detection of AD and other neurodegenerative diseases. Full article

This study presents a systematic literature review on the environmental impact of industrial applications of Rare Earth Elements (REEs), particularly those classified as Critical Raw Materials (CRMs), such as Neodymium alloys. These materials are key components of permanent magnets (PMs) used in electrical machines, including automotive applications, wind turbine generators, and various consumer electronics. A structured methodology began with a comprehensive search across multiple scientific databases utilizing primary and secondary keywords. Studies were selected through a multi-step process, including screening by title, abstract, and full-text review, ensuring the inclusion of relevant and high-quality research. This approach allowed for a rigorous and reproducible assessment of the literature. The review was conducted to address two central issues: the main environmental impacts of using rare earths in permanent magnets for electric motors, and the role of recycling and reuse strategies in reducing them. The review summarizes current knowledge on the life cycle environmental impacts of REEs, from extraction to end-of-life management, highlighting opportunities and challenges in recycling and reuse. While recycling can partially reduce environmental impact, significant gaps remain in efficiency and large-scale feasibility. The literature also emphasizes the substantial impacts of REEs in permanent magnets, including resource depletion, energy use, and emissions. Overall, the study highlights the need to integrate environmental considerations into the design and management of REE-containing systems and identifies research gaps to support more sustainable and efficient materials management. Full article

Nighttime light (NTL), a crucial indicator of human activity intensity, has not been systematically analyzed for its interactive mechanisms with air pollution and climate change. This study first investigates the spatiotemporal evolution of China’s total nighttime light (TNTL) and average nighttime light (ANTL), alongside key indicators of meteorological parameters and air pollution, at the grid scale from 2000 to 2023. We then employ prefecture-level city data and a geographically and temporally weighted regression (GTWR) model to quantify the spatiotemporally heterogeneous associations of temperature (TMP), precipitation (PRE), fine particulate matter (PM_2.5), ozone (O₃), land use (LUL), topography, and socioeconomic factors with NTL. The results indicate that (1) China’s NTL exhibits a significant overall upward trend, with areas of increase or significant increase comprising 92.04% of the total study area. TNTL growth demonstrates regional heterogeneity, expanding by a factor of 4.91 in East China and 2.65 in Northeast China; (2) meteorological and air pollution indicators display spatiotemporal non-stationarity, with the synergistic effect between O₃ and PRE being the strongest; (3) among NTL drivers, LUL contributes most significantly (0.44), followed by TMP (0.14) > PM_2.5 (−0.33 × 10⁻¹) > O₃ (0.17 × 10⁻¹) > PRE (−0.33 × 10⁻⁶); (4) TMP and PRE may primarily influence NTL by altering ecological conditions and nighttime activity patterns. TMP shows a strong positive correlation with NTL in the junction zone of South, East, and Central China, whereas PRE predominantly exerts a negative influence; (5) air pollution exhibits distinct spatiotemporal effects: high PM_2.5 and O₃ generally correspond to lower NTL, though positive correlations persist in some areas due to industrial structures, highlighting the need for integrated policies that balance air quality management with sustainable urban planning; (6) the 2013 “Air Pollution Prevention and Control Action Plan” significantly strengthened the negative correlation between PM_2.5 and NTL in North China. However, O₃ concentrations increased by 28.9% after 2017, underscoring the challenge of coordinating VOC and NOx controls for long-term atmospheric sustainability. Full article

Low-cost air quality sensors enable dense urban monitoring networks but require calibration against reference-grade instruments. While machine learning calibration is well-established for co-located sensor pairs, applying these calibrations to sensors deployed far from any reference station—the operational reality for most network sensors—lacks systematic methodology. We address this gap using 24 months of hourly data (August 2023–July 2025) from Sofia, Bulgaria, where five official reference stations (Executive Environmental Agency) operate alongside 22 AirThings low-cost sensors, four of which are co-located. Random Forest models achieved $R^2\in (0.53,0.75)$ across PM_2.5, PM₁₀, NO₂, and O₃, representing from 40% (for O₃) to 408% (for PM_2.5) improvement over Multiple Linear Regression baselines. Using leave-one-station-out spatial cross-validation, we derived pollutant-specific uncertainty growth rates ($\alpha$) from 3.84% to 5.62% per km, characterizing how calibration uncertainty increases with distance from reference stations (statistically significant for PM₁₀ and O₃, $p<0.05$). Applied to 18 non-co-located sensors, the framework generated 1.2 million calibrated hourly measurements with 95% prediction intervals over the study period. Co-location sites spaced 6 km apart achieve a less than 30% uncertainty increase at network midpoints, within EU Air Quality Directive thresholds for indicative monitoring. These empirically derived $\alpha$ parameters enable network planners to predict measurement reliability at arbitrary sensor locations without ground-truth validation, providing evidence-based guidance for cost-effective hybrid monitoring network design. Full article

Cascade dams fragment river habitats, but how seasonal hydrology modulates the biogeography and assembly of fish gut microbiota remains unclear. We surveyed gut bacterial communities of the omnivorous fish Saurogobio punctatus across 10 reaches separated by cascade dams in the Qijiang River during the wet (summer) and dry (winter) seasons using 16S rRNA gene amplicon sequencing. Sampling was synchronized among reaches to minimize temporal variability. Winter exhibited stronger differentiation among reaches and a steeper distance–decay pattern, and reach-scale environmental heterogeneity (especially dissolved inorganic nitrogen) was more stable under weak hydrodynamics. Null model analyses showed that stochastic processes dominated in summer, with dispersal-related processes and drift being prominent under high connectivity, whereas deterministic assembly increased in winter and was mainly associated with homogeneous selection. Compositionality-aware differential abundance analysis (ANCOM-BC2) identified 409 genera with a significant seasonal differential abundance after adjusting for reach (FDR q < 0.05). Random forest classification, used as a complementary prediction-oriented feature-ranking analysis, indicated higher reach discriminability in winter, with Nitrospirota ranking among the top features. PLS-PM indicated that α-diversity had the strongest direct association with β-diversity in the specified model, whereas spatial and environmental effects were linked to β-diversity mainly through indirect, α-diversity-mediated pathways. Biologically, α-diversity may reflect an integrative summary of the within-gut taxon pool shaped by host filtering and environmentally derived inputs (e.g., diet- and habitat-associated sources), which can influence the magnitude of between-reach compositional turnover. Together, these results show that seasonal hydrological regimes tune spatial turnover and assembly of fish gut microbiota in cascade-regulated rivers. Full article

This study evaluates the ability of three classification procedures to distinguish areas with different levels of atmospheric pollution, based on biomonitoring carried out by analyzing the color and spectral characteristics of mulberry (Morus L.) and linden (Tilia L.) leaves. Sampling was carried out in areas that were grouped into four classes according to the concentrations of fine particulate matter (PM2.5, PM10) and gaseous pollutants (TVOC, NOx, SOx, CO, and eCO₂), measured using a specialized multisensor device. A total of 57 informative features were analyzed, representing indices obtained from two color models (RGB and Lab), as well as from VIS and NIR spectral characteristics measured for the adaxial and abaxial leaf surfaces. The data processing methodology includes feature selection using the ReliefF method and a comparative analysis between two approaches to dimensionality reduction—principal components (PC) and latent variables (LV). The results indicate that data reduction using PC provides significantly higher accuracy and better class separability, regardless of the classifier used, compared to LV, where errors exceed 40%. The comparison between classifiers shows a clear superiority of nonlinear models. While linear discriminant analysis demonstrates low efficiency, quadratic discriminant analysis (Q and DQ) and SVM with radial basis function (RBF) achieve high accuracy of class separability, reaching 100% in the SVM-RBF model for both tree species. The study also reveals functional asymmetry: the adaxial side of the leaves is more informative for spectral indices, while the abaxial side is more sensitive to color changes. The results confirm that the combined optical characteristics obtained from the leaf surface of bioindicators form a reliable method for ecological monitoring of air quality in urban areas. Full article

To investigate the spatiotemporal variations in particulate matter (PM) retention by common urban greening species, six tree species were studied across different functional zones in Urumqi, China, which includes traffic area (TA), residential area (RA), park area (PA), and landscape ecological forest (LA) at varying altitudes. We measured the retention of PM_0.2–3, PM_3–10, PM_>10, and PM_total for Pinus sylvestris, Picea asperata, Ulmus pumila, Ligustrum obtusifolium, Ulmus densa, and Fraxinus rhynchophylla. Results showed significant differences (p < 0.05) among functional zones, with retention capacity following the order that evergreen trees > deciduous shrubs > deciduous trees. Specifically, P. sylvestris and Picea asperata exhibited the highest overall PM retention. Temporally, PM accumulation increased over time, reaching a minimum 3 days after heavy rainfall (>20.4 mm) and a maximum after 23 days. Spatially, retention was highest in the TA and lowest in the PA. On Yamalike Mountain, PM_3–10 and PM_>10 retention by Ulmus pumila increased significantly with altitude, while other fractions showed no clear trend. These findings suggest that the spatiotemporal differences in PM retention are distinct, and the strategic selection and management of species in specific urban environments can significantly enhance the regulation of atmospheric particulate pollution. Full article

Object detection in UAV-based fire rescue scenarios faces multiple challenges, including densely distributed small targets, severe occlusion, and interference from smoke and flames. Existing mainstream detection models, such as the YOLO series, often prioritize inference speed at the expense of modeling global context and spatial positional information, resulting in limited performance in such complex environments. To address these limitations, this paper proposes FirePM-YOLO, an object detection architecture optimized for fire rescue applications. Based on the YOLO framework, the proposed model introduces two key innovations: first, a Position-Aware Enhanced Mamba module (PEMamba) is designed, which incorporates a compact positional encoding mechanism, lightweight spatial enhancement, and an adaptive feature fusion strategy to significantly improve scene perception while maintaining computational efficiency. Second, a PEMBottleneck structure is constructed, which dynamically balances local convolutional features and global PEMamba features via learnable weights. This module is embedded into the shallow layers of the backbone network, forming an enhanced PEM-C3K2 module that captures long-range dependencies with linear complexity while preserving fine local details, thereby enabling holistic contextual understanding of fireground environments. Experimental results on the self-built “FireRescue” dataset demonstrate that compared with the original YOLOv12 and other mainstream detectors, the proposed model achieves improvements in both mean average precision (mAP) and recall while maintaining real-time inference capability. Notably, it exhibits superior detection performance on challenging samples, such as small-scale and partially occluded professional firefighting vehicles. Full article

Handgrip strength (HGS) is widely used as a biomarker of muscle function and overall health in older adults. However, conventional analyses based on peak force values may overlook relevant temporal features of the HGS curve. This cross-sectional study proposes a novel methodological approach that examines the shape and variability of HGS(t) curves recorded from community-dwelling older adults. Functional principal component analysis (FPCA) was applied to assess the consistency of individual trials and the representativeness of mean curves. Statistical non-parametric mapping (SnPM) was then used to identify time regions showing significant differences between groups. Complementary analyses of discrete and derivative parameters, together with non-parametric comparisons based on the Hodges–Lehmann estimator and corresponding 95% confidence intervals, were conducted to quantify effect sizes. FPCA revealed high within-participant consistency, supporting the use of mean curves for group-level comparisons. SPM analyses indicated significant differences in the early force development phase. Importantly, this approach shows that sex differences are attributable to magnitude effects, with men generating higher forces and faster early rates of force development, and not to differences in the neuromuscular strategy of force production. Traditional discrete parameters partly captured these patterns but failed to reflect the full temporal dynamics. This methodological approach to the HGS curve may provide further insights into neuromuscular control mechanisms that cannot be truly captured by the minimalistic HGS discrete parameters. Full article

Objectives: While the previous literature has established that meteorological conditions are associated with COVID-19 mortality fluctuations, the relative effect of each of these highly correlated factors remains unclear. This study aims to conduct a comparative analysis to determine which of three main meteorological variables—Ambient Temperature, Ultraviolet (UV) Index, and Sunlight Duration—have the strongest negative association with COVID-19 mortality. The objective is to quantify and rank their impact over a 7-to-21-day biological exposure window. Methods: We conducted retrospective spatiotemporal analyses in the form of panel Poisson Distributed Lag Models (PDLMs) regression using daily data from 21 January 2020 to 10 January 2023, spanning 129 distinct geographical regions worldwide. To ensure a direct and fair comparison of effect sizes, all meteorological and environmental variables were Z-score standardized. We estimated three independent PDLMs—each focusing separately on UV Index, Ambient Temperature, and Sunlight Duration—with lags ranging from 7 to 21 days. These models controlled for overarching time trends and utilized a categorical variable to account for Region Fixed Effects modeling time-invariant regional health and socioeconomic determinants (e.g., obesity, age demographics, healthcare capacity). Furthermore, distributed lags of daily PM2.5 (air pollution) and relative humidity were explicitly included in each model as dynamic confounders. Results: The comparison of PDLM results reveals that the UV Index has the strongest negative association with COVID-19 mortality. A one standard deviation increase in the UV Index corresponds to a massive, highly significant cumulative reduction in deaths observed 1 to 3 weeks later (p < 0.001). Sunlight Duration is the second-strongest protective meteorological factor, whereas Ambient Temperature has the weakest effect. The distributed lags of particulate matter (PM2.5) and relative humidity were found to be statistically insignificant when modeled alongside the meteorological variables. Conclusions: After standardizing variables and controlling for dynamic environmental confounders like air pollution and humidity, the study findings provide robust empirical evidence that meteorological conditions have a strong significant association with COVID-19 mortality fluctuation with a temporal delay, overcoming the confounding effects of merely dry or clear-air conditions. Full article

This study estimates the association between respiratory outcomes among employees of a secondary aluminum plant and airborne pollutants. Additionally, it looks into the relationship between pulmonary dysfunction in workers and X-Ray repair cross-complementing one (XRCC1) gene polymorphisms. 110 exposed workers and 58 non-exposed workers were enrolled in the study. Measurements were conducted on sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and particulate particles. Pulmonary function was tested. Eosinophil cationic protein (ECP), C-reactive protein (CRP), matrix metalloproteinase-1 (MMP-1), interleukin 6 (IL6), granulocyte-macrophage colony-stimulating factor (GM-CSF), XRCC1 protein, and genotyping of XRCC1 gene polymorphisms were examined. The annual average concentrations of particulate matter (PM_2.5, PM₁₀), total suspended particulates (TSP), SO₂, and NO₂ were lower than the permissible limit. The areas around ovens, evaporators, and cold rolling mills exhibited the highest amounts. The majority of employees in these departments had impaired lung function. Prolonged exposure was associated with a significant decrease in forced expiratory volume in 1 s (FEV1%) and forced vital capacity (FVC%) among the exposed group (p = 0.001 & 0.04, respectively). Serum XRCC1 levels were significantly higher among exposed workers (p = 0.02). Inflammatory biomarkers showed no statistically significant differences between groups. Aluminum workers are at risk of developing respiratory disorders. The level of serum XRCC1 may serve as a potential biomarker for detecting susceptible workers. Full article

Driven by rapid global urbanization and expanding urban footprints, air pollution, particularly from industrial emissions and vehicular exhaust, has intensified, with rising concentrations of inhalable particulate matter (PM) posing direct threats to public health. To address this challenge, we conducted field measurements of ambient PM concentrations across diverse urban plant communities and quantitatively compared their capacity to mitigate four key size-fractionated pollutants: total suspended particles (TSPs), PM₁₀, PM_2.5, and PM₁. Our objective was to identify the most effective plant community type for PM abatement in urban settings. Results demonstrate that: (1) evergreen broad-leaved forests exhibit the highest overall PM removal efficiency among all studied communities; (2) removal efficacy declines markedly with decreasing particle size, indicating limited capacity to capture ultrafine particles (e.g., PM₁); and (3) seasonal performance peaks in summer, especially for deciduous broad-leaved forests attributable to maximal leaf area index, enhanced stomatal activity, and favorable meteorological conditions. By rigorously evaluating species composition, canopy structure, and seasonal dynamics, this study provides empirically grounded guidance for evidence-based urban greening strategies aimed at optimizing airborne particulate mitigation worldwide. Full article

Urban air pollution is increasingly recognised as a determinant of mental wellbeing, yet most existing studies rely on static exposure estimates and lack spatial granularity. This limits understanding of how pollutant-specific patterns influence psychological states in real-world settings. To address this gap, we integrate real-time environmental and physiological data from 40 participants using the DigitalExposome dataset, applying multivariate and spatial analysis techniques. Our findings confirm that Particulate Matter (PM_2.5) exerts the strongest negative association with mental wellbeing while extending prior work by establishing a preliminary ranking of other pollutants Particulate Matter (PM₁₀), Particulate Matter (PM₁), Carbon Monoxide (CO), Nitrogen Dioxide (NO₂), Ammonia (NH₃). We applied statistical and spatial analysis methods, including heatmaps and Voronoi diagrams, to explore links between pollutants and wellbeing and compare the relative influence of air pollution and noise. This enabled identification of pollutant-specific hotspots and multi-level wellbeing patterns across individual, accumulated, and collective scales. These results demonstrate the value of spatial analysis for environmental health research and support targeted urban interventions, such as green space placement and traffic re-routing, to mitigate mental wellbeing risks. Full article