Search Results (2,974)

Coastal green tides may alter nearshore bioaerosols through coupled marine, atmospheric, and meteorological processes, yet their effects on airborne microbial communities remain poorly resolved. Atmospheric samples were collected in Aoshan Bay, Qingdao, China, during five phases of the Ulva prolifera green tide in 2019 (pre-bloom, 19 April; early bloom, 15 June; middle bloom, 15 July; late bloom, 6 August; post-bloom, 30 August); seawater samples were collected at one nearshore site on each of the five sampling dates, with microbial sequencing performed for the middle-bloom (15 July) and late-bloom (6 August) phases. Bacterial and fungal communities were characterized; although bioaerosols may also contain microalgae and viruses, this study profiled only the bacterial and fungal fractions, using bacterial 16S rRNA gene (V3-V4 region) and fungal internal transcribed spacer (ITS2) amplicon sequencing and evaluated together with meteorological variables, air-pollutant concentrations, and 72-h backward air-mass trajectories. Proteobacteria dominated the airborne bacterial assemblages (81.28–97.83%), with Sphingomonas as the most abundant genus (47.85–89.84%). Basidiomycota and Ascomycota dominated the fungal assemblages, whereas Cryptococcus and Alternaria were the major fungal genera. Community richness and composition varied across bloom phases. Chytridiomycota was undetected before the bloom (0%), appeared after bloom onset, and reached its highest relative abundance during the middle phase (8.19%). Spatial patterns indicated joint terrestrial and marine influences, although bacterial communities in seawater and air remained highly dissimilar. Temperature, relative humidity, particulate matter, ozone, and air-mass origin were associated with changes in microbial diversity and composition. These findings provide an observational baseline for coastal bioaerosol dynamics during a macroalgal green tide, extending the HAB–bioaerosol literature—which has focused predominantly on cyanobacterial blooms—to a large green macroalga. Bacteria and fungi showed contrasting environmental responses: bacterial richness increased with temperature, whereas fungal diversity declined. Greater compositional similarity between seawater and air for fungi than for bacteria suggests differential environmental filtering at the air–sea interface and implies that multiple source pathways—direct aerosolization, sea-surface release, and in-situ atmospheric production—may differentially shape the two domains. Given the single-date-per-phase sampling design, the absence of sequenced laboratory contamination controls, and the lack of absolute abundance data, these results should be regarded as preliminary and hypothesis-generating, underscoring the need for ASV-level source tracking, controlled chamber experiments, and replicated multi-year designs in future assessments of bloom–atmosphere interactions. Full article

Airborne bacterial diseases driven by pathogen aerosols in enclosed greenhouses spread rapidly, challenging traditional early-warning methods. This study developed a two-step monitoring system for cucumber angular leaf spot using low-cost particulate matter (PM) sensors, qPCR, and machine learning. Evaluated across spatially independent greenhouse trials using 732 plot-days of data, PM sensors were utilized as dynamic physical proxies alongside microclimate data. These proxies continuously estimated the fluctuations of pathogen aerosols suspended in the greenhouse air. When estimated aerosol risks exceeded a pathogenic threshold, targeted air sampling and qPCR quantification were triggered. For pathogen monitoring, the Extra Trees (ET) surveillance model accurately predicted the accumulation of airborne pathogen aerosols (R² = 0.884). For disease forecasting, by integrating the quantified aerosol loads with environmental factors, the XGBoost prediction model forecasted the daily disease index change rate with high precision (R² = 0.874). SHapley Additive exPlanations (SHAP) analysis confirmed that the concentration of airborne pathogen aerosols and vapor pressure deficit were primary drivers of disease expansion. By combining continuous physical sensing of greenhouse air with risk-triggered biological quantification, this framework provides a feasible strategy to partly compensate for the lack of biological specificity of PM sensors and supports early-warning management of airborne bacterial diseases in protected agriculture. Full article

Air pollution poses a major health risk, particularly in children, who spend considerable time in school. We monitored air pollution in school environments and investigated its impact on cardiovascular and neurocognitive outcomes. Seven elementary schools in Antwerp, Belgium, including one open-air school, participated in a longitudinal study. Indoor and outdoor particulate matter (PM) and outdoor NO₂ concentrations were measured. Endothelial function and attentional outcomes were assessed repeatedly (n = 3) in 138 healthy children (mean age of 10.3 ± 0.5 years, 55.8% male). Mixed effect models were used to investigate the association between PM and NO₂ exposure and endothelial and attentional outcomes. This study considered median PM concentrations during the 4 h of the study visits (direct exposure), those during the 24 h (acute exposure) and 1 week preceding the study visits (recent exposure), and monthly NO₂ concentrations. Models were adjusted for sex, age, BMI-z, and visit. Increased direct (4 h) indoor PM exposure was associated with reduced microvascular dilatation, while selective and sustained attention improved with direct and recent PM exposure, respectively. No associations were found with NO₂. Exclusion of the open-air school slightly altered the findings. Larger longitudinal studies with improved exposure assessment are needed to clarify the effects of PM exposure and the influence of (open-air) school environments on children’s learning and development. Full article

Long-term exposure to ambient fine particulate matter (PM_2.5) is the leading environmental risk factor for premature mortality worldwide, yet comprehensive province-level evidence quantifying its health burden across Türkiye remains limited. This study investigated the spatial relationship between long-term PM_2.5 exposure and all-cause attributable mortality across all 81 Turkish provinces in 2022 using province-level annual mean PM_2.5 concentrations and World Health Organisation (WHO) AirQ+ estimates of PM_2.5-attributable deaths among adults aged ≥30 years, assuming a counterfactual concentration of 5 µg/m³. The association between PM_2.5 exposure and mortality was evaluated using Pearson and Spearman correlation analyses, ordinary least squares (OLS) regression, a log–log elasticity model, and population-weighted regional and exposure-quartile comparisons, while national temporal indicators for 2010–2023 were reported solely as supplementary context for the primary single-year 2022 cross-sectional analysis. The population-weighted annual mean PM_2.5 concentration was 27.0 µg/m³, exceeding the WHO Air Quality Guideline by a factor of 5.4, and all 81 provinces exceeded the recommended threshold. The bivariate OLS model accounted for 41% of the between-province variation in attributable mortality rates (OLS slope = 3.23 additional deaths per 100,000 population for each 1 µg/m³ increase in PM_2.5; 95% CI: 2.37–4.10; R² = 0.41; p < 0.001), while the log–log elasticity model indicated that a 1% increase in PM_2.5 concentration was associated with a 0.80% increase in the attributable mortality rate (95% CI: 0.65–0.95). The attributable fraction of natural-cause mortality increased progressively from 8.8% in the lowest exposure quartile to 24.6% in the highest. Nationwide, an estimated 68,440 premature deaths, representing 14.2% of all natural-cause deaths among adults aged ≥30 years, were attributable to PM_2.5 exposure. These findings quantify a steep, spatially graded PM_2.5-attributable mortality burden across Türkiye. As the attributable estimates derive from the WHO AirQ+ concentration–response function, the gradient describes the magnitude and spatial distribution of the modelled burden rather than an independently estimated exposure–response relationship, and on that basis the results support the adoption of WHO-aligned air-quality standards and accelerated decarbonization strategies to reduce the national health burden attributable to ambient air pollution. Full article

Climate change has an increasing influence on the composition, abundance, and distribution of bioaerosols in the atmosphere. Bioaerosols, which include airborne bacteria, fungi, viruses, pollen, and microbial fragments, represent an important component of atmospheric particulate matter and play a significant role in environmental and public health processes. Changes in temperature, humidity, extreme weather events, and air pollution can alter microbial survival, dispersal, and seasonal patterns in the air. These climatic shifts may enhance the long-distance transport of microorganisms and modify the diversity and concentration of airborne microbial communities. As a result, exposure to pathogenic or allergenic bioaerosols may increase, contributing to respiratory diseases, allergies, and the spread of infectious pathogens. Furthermore, climate-driven environmental disturbances such as dust storms, wildfires, and urbanization can intensify bioaerosol emissions and atmospheric transport. Understanding the interactions between climate change and bioaerosols is therefore essential for predicting microbial dispersion patterns and developing strategies to mitigate associated public health risks. Full article

Estuarine turbidity fronts are narrow transition zones where suspended particulate matter concentrations change sharply. Their detection from remote sensing imagery remains challenging because conventional methods rely on empirical thresholds, are sensitive to mixed pixels, and often lack transferability and physical interpretability. Here, we evaluate the potential of foundation-model representations by integrating AlphaEarth 64-dimensional embeddings with Sentinel-2 spectral and multi-scale spatial features. A 229-dimensional feature set is constructed and fed into a two-step framework combining random forest classification with an empirically calibrated optical mask based on low red-band reflectance. The fused feature set achieves an overall accuracy of 91.2%, an F1 score of 87.5%, and a Kappa coefficient of 0.807, outperforming both spectral–spatial features alone and AlphaEarth embeddings alone. To elucidate the contribution mechanism of AlphaEarth embeddings, we conduct two complementary SHAP analyses: one evaluating each dimension’s direct contribution to front classification, and the other assessing its capacity to predict Sentinel-2 band reflectance. Only nine dimensions overlap between the respective top 20 lists, revealing a clear functional division within the embedding space—some dimensions primarily encode spectral reflectance information, while others encode spatial context, edge patterns, or topological structures that are not directly accessible from local spectral features. This division represents the added value of AlphaEarth beyond conventional optical data. The empirically calibrated optical mask reduces candidate frontal area by 59.09% in turbid estuaries and restores linear front morphology. However, leave-one-estuary validation yields F1 scores ranging from 0.33 to 0.84, substantially below the within-estuary score of 0.93, demonstrating limited cross-region transferability and challenging the assumption of domain invariance in foundation-model embeddings. These findings highlight both the value of fusing foundation-model representations with local spectral–spatial features and the critical need for domain-adaptation strategies to improve generalization across contrasting estuarine hydrodynamic regimes. Full article

Background: Residential wood combustion (RWC) is a major local source of fine particulate matter (PM_2.5) in Nordic regions. We quantified the health impacts and economic costs of long-term exposure to PM_2.5 from RWC in Västerbotten County, northern Sweden. Methods: High-resolution annual mean concentrations of PM_2.5 from small-scale residential heating in 2023 were obtained from a national dispersion-modelling system. Population-weighted exposure was calculated by combining grid-level concentrations with home address coordinates for the resident population. Health impact assessment (HIA) followed the framework used in Nordic studies and the VALESOR project, applying a near-source PM_2.5–mortality exposure–response function with a relative risk of 1.26 per 10 µg/m³ for natural-cause mortality and VALESOR default functions for morbidity (ischaemic heart disease, stroke, COPD, lung cancer, type 2 diabetes, dementia, childhood asthma, cardiovascular hospital admissions). Attributable cases and years of life lost (YLL) were estimated, and the economic valuation used VALESOR unit costs for Sweden to derive central, low and high damage-cost estimates. Results: The population (n = 280,742)-weighted mean RWC-PM_2.5 exposure was 0.16 µg/m³ (interquartile range 0.10–0.21). This was associated with an estimated 30 years of life lost (YLL) per year from natural-cause mortality, plus incident morbidity of approximately 0.3 lung cancer cases, four childhood asthma cases, three COPD cases, three IHD events, one stroke, two dementia cases, one diabetes case and 0.8 cardiovascular hospital admissions annually. The total annual health-related cost attributable to RWC-PM_2.5 was about €7.0 million (95% uncertainty range €4.6–9.4 million), dominated by mortality and disutility costs. Conclusions: PM_2.5 concentrations from residential wood burning are associated with health impacts costing multi-million Euros in a Nordic region, supporting policies to reduce emissions from residential wood heating and to promote cleaner heating alternatives. Full article

Urban air pollution remains a major public health concern, with road traffic representing one of the dominant sources of particulate matter and volatile organic compounds in growing cities. This study evaluates the level of chemical air pollution in Alba-Iulia municipality (Romania) through two measurement campaigns (May 2025 and October 2025), carried out at 26 and 19 points, respectively, selected based on traffic intensity and population vulnerability criteria (schools, kindergartens, hospitals, and the food market). Measurements targeted PM_2.5, PM₁₀, total volatile organic compounds (TVOC) and formaldehyde (HCHO), as well as carbon dioxide (CO₂), correlated with road traffic intensity. Statistical analysis revealed significant differences between the two seasons for PM_2.5, PM₁₀, and CO₂ (p < 0.01), as well as a strong correlation between particulate matter concentrations and vehicle counts (r = 0.60–0.95), consistent with road traffic being an important local contributor to particulate matter, though correlation alone cannot establish source dominance in a strict causal sense. A multiple regression controlling for both traffic and season explained over 73% of the variance in PM_2.5 and PM₁₀ and showed that traffic and season each contribute independently to particulate levels. Both mean PM_2.5 and PM₁₀ exceeded WHO and EU limit values in both campaigns, and the low-cost sensor over-read absolute concentrations by roughly 2–4× relative to the official monitoring network, so absolute values should be treated as orientative. Despite this bias, the consistent spatial and seasonal patterns show that portable low-cost sensors can reliably rank exposure hotspots and support the targeting of traffic-mitigation measures, such as selective catalytic reduction (SCR) systems, in medium-sized cities that lack dense reference monitoring networks. Full article

Background/Objectives: Stroke remains a leading cause of death and disability globally, with considerable regional heterogeneity in incidence and mortality reflecting differences in classical vascular risk factors and environmental determinants. Fine particulate matter (PM2.5) is an established modifiable environmental risk factor, yet its association with regional stroke disparities remains understudied in Mediterranean European contexts. We examined regional differences in stroke hospitalization and mortality and their ecological associations with long-term air-pollution exposure across Spain’s 17 autonomous communities from 2013 to 2021. Methods: We conducted a nationwide longitudinal ecological study using aggregated stroke indicators from INCLASNS and ambient air-pollution data from MITECO. The dataset comprised 306 sex-specific region-year observations. The primary outcome was the age-standardized hospitalization rate (ASHR); secondary outcomes were CHR, CMR, ASMR, CFR, and IMI. Associations were assessed using correlation analyses, temporal trends, regional comparisons, and multivariable regression. Sex-stratified analyses were considered exploratory because pollutant concentrations were measured at the regional-year level and were not sex-specific. Results: Mean ASHR was 20.1 ± 5.4 per 10,000 population, with marked regional variation. PM2.5 showed the largest positive ecological correlation with ASMR among all pollutants examined (r = 0.264, p < 0.001), although the correlation remained weak. Overall pollutant–outcome correlations were weak and heterogeneous in direction. Regional variation in stroke burden was not adequately explained by the available air-pollution indicators. Conclusions: PM2.5 was weakly associated with ASMR, whereas associations with hospitalization and other pollutants were small and inconsistent. These ecological findings do not support causal or individual-level inference and warrant confirmation using higher-resolution exposure and individual-level data. Full article

Accurately predicting fine particulate matter (${\mathrm{PM}}_{2.5}$) concentrations in regions with sparse monitoring networks remains a critical challenge for air quality management and public health. This study evaluates a machine learning (ML) data fusion approach that integrates daily federal regulatory observations, daily low-cost community sensor measurements, and monthly satellite-derived aerosol products (functioning as a regional background field) to improve ${\mathrm{PM}}_{2.5}$ prediction across under-monitored environments. Using a Long Short-Term Memory (LSTM) neural network architecture, the analysis examines how combining heterogeneous data sources influences predictions. Results show that pooled multi-source training was associated with higher holdout skill relative to some single-source configurations under this parsimonious baseline, though associations are city- and configuration-dependent and cannot be attributed solely to fusion because evaluation populations are not common. Comparisons against tree-based baselines (Random Forest, Gradient Boosting, XGBoost) indicate that overall predictive skill, not just the LSTM’s, is constrained by data availability, suggesting that data composition, rather than model choice, is the primary driver of the observed performance patterns. These findings highlight both the potential and the practical constraints of multi-source ML approaches for air quality prediction and exposure assessment, with implications for model design, monitoring strategy, and environmental equity. This study is intentionally scoped as an applied evaluation of data fusion performance rather than a comprehensive assessment of algorithmic optimality or operational forecasting readiness. The analysis focuses on daily ${\mathrm{PM}}_{2.5}$ prediction across a selected set of U.S. cities and does not address sub-daily variability, real-time deployment constraints, or event-specific model optimization. Model performance is therefore interpreted in the context of data availability, consistency, and representativeness, rather than as an upper bound on achievable predictive skill. Full article

One of the major environmental problems in recent years is the deterioration of atmospheric air quality. Human activity in urban areas leads to the emission of particulate mat-ter containing various chemical components, including toxic heavy metals, which can accumulate in living organisms. The accumulation of pollutants by various species in mountain environments remains insufficiently understood. Therefore, the aim of this study was to assess the potential use of two species commonly co-occurring in mountain habitats—epiphytic lichen Parmelia sulcata Taylor and spruce Picea abies L., H. Karst.—as biomonitors of anthropogenic atmospheric pollution. The study was conducted in the Central Sudetes (Poland), across three site categories characterized by varying degrees of anthropogenic pressure (green, rural and urban areas). The results confirm that Parmelia sulcata can serve as a species reflecting heavy-metal contamination originating from atmospheric deposition, mainly lead, iron, cadmium and chromium, whereas the needles of Picea abies are useful mainly for indicating manganese pollution. No significant effect of anthropogenic pressure on heavy-metal concentrations was found, which is most likely related to specific air circulation in mountain areas that leads to the dispersion of air pollutants and their accumulation by the study species in the higher regions of the Central Sudetes. Full article

Ambient fine particulate matter (PM_2.5) is associated with oxidative stress, metabolic dysregulation, and cardiometabolic disease. However, systemic metabolic responses to contrasting real-world ambient air pollution exposure environments remain poorly characterized. In this cross-sectional study, untargeted proton nuclear magnetic resonance (¹H-NMR)-based urinary metabolomics was used to investigate metabolic signatures associated with contrasting ambient air pollution exposure environments in Thailand. Adults residing in Chiang Mai (high-exposure region; n = 51) and Songkhla (low-exposure region; n = 52) were recruited during a period of elevated regional air pollution, with long-term residence serving as a proxy for differential exposure to ambient air pollution environments. Partial least squares-discriminant analysis (PLS-DA) demonstrated separation between exposure groups (R² = 0.873, Q² = 0.447), indicating good model fit but only modest predictive ability. Eight urinary metabolites differed significantly (p < 0.05), implicating pathways related to tryptophan metabolism, nucleotide metabolism, energy metabolism, and host–microbial co-metabolism. L-arginine and L-cystathionine showed lower relative abundance in the high-exposure group, and six metabolites remained significant after Benjamini–Hochberg false discovery rate correction. Following covariate adjustment, L-tryptophan, hippuric acid, xanthine, and 5-hydroxyindoleacetic acid (5-HIAA) remained significantly associated with the high-exposure group. Contrasting ambient air pollution exposure environments, indexed by regional PM_2.5 concentrations, were associated with coordinated urinary metabolic alterations. Because long-term regional residence served as a proxy for individual-level exposure and diet and lifestyle differences between regions were not fully controlled, these findings should be interpreted as associations with contrasting regional exposure environments. Metabolite annotations remain putative and require validation in longitudinal studies with comprehensive pollutant characterization and individual-level exposure assessment. Full article

Mass concentrations of particulate matter are a fundamental metric for air quality and health impact assessment; however, they are insufficient for accurately characterizing exposure. They do not capture particle size distribution or number concentration. Therefore, aerosol assessment should include particle number concentration (PNC), which better represents toxicologically relevant fractions and enables more precise source identification. The aim of this study was to conduct a comprehensive evaluation of particle number concentration (PNC) measurements in the 0.3–10 µm size range obtained using three low-cost Sensirion SPS30 particle sensors under field conditions in an urban environment. The analyses included an assessment of agreement between the SPS30 sensors, an evaluation of their measurement performance against the OPS 3330 optical particle spectrometer, and the development of calibration models. The SPS30 sensors showed high inter-device repeatability for PNC in the 0.3–1.0 µm range (CV_d < 2%). However, measurement performance declined with increasing particle size, with the index of agreement (IOA) decreasing from 0.8 (0.3–0.5 µm) to −0.5 (2.5–10 µm). Sensor accuracy was influenced by meteorological conditions: relative humidity primarily affected short-term variability (precision and dynamic agreement), while temperature controlled systematic bias. Although incorporating these variables into advanced calibration models improved performance, SPS30 sensors remained unsuitable for PNC measurements in the 2.5–10 µm range, exhibiting systematic errors of ~25% even after nonlinear correction. The findings support the responsible use of low-cost particle sensors for supplementary air quality monitoring, contributing to accessible environmental data and sustainable urban air quality management. Full article

Background/Objectives: Meropenem is a broad-spectrum carbapenem antibiotic with demonstrated efficacy against multidrug-resistant Gram-negative pathogens. Although its use as an ophthalmic formulation is off-label, growing clinical evidence supports its application in severe ocular infections such as keratitis and endophthalmitis. However, the intrinsic instability of meropenem in aqueous solutions and the absence of standardized ophthalmic preparations limit its routine use. Furthermore, no stability studies are currently available for meropenem 50 mg/mL eye drops stored in polypropylene (PP) dropper bottles under freezing and subsequent refrigerated conditions. The aim of this study was to evaluate the physicochemical and microbiological stability of a 50 mg/mL meropenem ophthalmic solution prepared in a hospital pharmacy using a commercial meropenem pharmaceutical product, and packaged in PP containers. Methods: Eye drops were aseptically prepared from a commercially available pharmaceutical product, containing 1g de meropenem and anhydrous sodium carbonate as an excipient. After preparation, the drops were stored at −20 ± 2 °C for up to 42 days, followed by refrigerated storage (5 ± 3 °C) after thawing for up to 7 days. Chemical stability was assessed using a validated stability-indicating HPLC method in accordance with ICH guidelines and was defined as 90–110% recovery of the initial concentration. Physical stability (appearance, pH, particulate matter) and microbiological stability were also evaluated under simulated in-use conditions. Results: The HPLC method demonstrated excellent linearity, precision, and accuracy. Meropenem concentrations remained within the predefined acceptance limits throughout the 42-day study period under freezing conditions, with no significant changes in pH, color, or particulate formation. After thawing, a progressive decrease in drug concentration was observed under refrigerated conditions, falling below 90% of the initial concentration within 24–48 h. A concomitant color change from colorless to yellow was also detected, consistent with β-lactam ring hydrolysis. Despite this degradation, no significant changes in physical parameters other than color were observed, and microbiological testing confirmed sterility for up to 7 days under refrigerated conditions. Conclusions: Meropenem drops 50 mg/mL in PP dropper bottles are physicochemically and microbiologically stable for 43 days (42 days under frozen conditions plus 1 day, in-use conditions, after opening and under refrigeration). Full article

Background: Long-term exposure to fine particulate matter (PM_2.5) and its components, as well as lower nutritional scores or irregular leisure-time physical activity, has been individually linked to reduced kidney function. However, the joint associations of PM_2.5 component mixture combined with nutritional scores or leisure-time physical activity with estimated glomerular filtration rate (eGFR) remain underexplored, especially among subway workers. Method: This study included 8477 subway workers in Wuhan, China. PM_2.5, and component data were obtained from the Tracking Air Pollution in China (TAP) dataset. Leisure-time physical activity was assessed using a self-administered questionnaire adapted from the International Physical Activity Questionnaire (IPAQ). Kidney function was evaluated by creatinine-based eGFR. Nutritional indices of Geriatric Nutritional Risk Index (GNRI) and the Prognostic Nutritional Index (PNI) were calculated. Generalized linear models were used to examine the associations of PM_2.5 and each of its components or nutritional indices with lower creatinine-based eGFR. The associations of PM_2.5 component mixture with creatinine-based eGFR were evaluated using WQS, QGC, and BKMR models. Joint associations of PM_2.5 component mixture with nutritional indices or leisure-time physical activity were further explored. Results: Negative associations of individual PM_2.5 components and their mixture with eGFR values were observed. WQS and QGC models showed that the mixture of PM_2.5 components was associated with an increased risk of lower creatinine-based eGFR, and their corresponding ORs (95%CI) were 1.183 (1.106, 1.266) and 1.183 (1.083, 1.291), respectively. BKMR analysis further showed the overall effect on eGFR values in relation to a 5-percentile increase in the PM_2.5 component mixture was −0.045 (−0.068, −0.022), compared with their levels fixed at their respective medians. Meanwhile, the probability of lower creatinine-based eGFR increased with rising quantiles of PM_2.5 component mixture concentration. Notably, differential associations were observed across subgroups classified by nutritional indices or leisure-time physical activity, but no significant interaction was observed. Conclusions: Long-term exposure to residential ambient PM_2.5 components or their mixture is associated with lower creatinine-based eGFR. This different association was observed among subgroups classified by nutritional indices or leisure-time physical activity, which may inform hypothesis generation for future studies. Full article