Search Results (1,460)

Fine particulate matter (PM_2.5) composition, rather than mass alone, plays a critical role in determining toxicity and health impact. This study examined short-term associations between daily PM_2.5 constituents—black carbon (BC), nitrate (NO₃⁻), ammonium (NH₄⁺), and trace elements—and cardiopulmonary hospital admissions in Makkah, Saudi Arabia. Twelve months of constituent data from the Alharam monitoring site were linked to Herra hospital admissions for cardiovascular (CVD) and pulmonary diseases, stratified by visit type, age, and sex. Negative-binomial generalized linear models estimated adjusted relative risks (aRRs) per interquartile range increase in each constituent, controlling for meteorology, seasonality, and temporal trends. Mean PM_2.5 was 113.6 µg/m³; BC, sulfur, NO₃⁻, and NH₄⁺ dominated the fine fraction. Crustal elements were strongly intercorrelated (r > 0.9), while BC, lead (Pb), and nickel (Ni) showed moderate correlations (r ≈ 0.4–0.6), suggesting shared anthropogenic origins. BC increased CVD emergency/outpatient visits by 18% (aRR = 1.18; 95% CI: 1.08–1.29) and inpatient admissions by 25% (aRR = 1.25; 95% CI: 1.07–1.46). Ni and sulfur were also significant predictors; crustal elements were not. Multi-pollutant models confirmed BC and Pb as independent predictors (aRR = 1.19; 95% CI: 1.02–1.38). Effects were strongest among older adults aged 45–65 at lag 0–2 days. These findings highlight the need for emission controls targeting traffic and industrial combustion sources. Full article

Biomass burning is a major source of atmospheric pollution. However, rapid and quantitative assessment of particulate matter in smoke plumes remains challenging, owing to the physical uncertainties, limited coverage, and labor-intensive quality control of conventional monitoring approaches. Existing image-based deep learning methods typically address either smoke detection or air quality assessment separately. To address this gap, we develop a Unified Smoke Detection and Aerosol Estimation Framework (SDAF), a three-stage deep learning approach evaluated using a smoke-rich airborne dataset. The framework integrates smoke localization with PM₁ estimation by combining a YOLOv11-based detector with an optimized convolutional neural network. The model achieves high accuracy under in-plume conditions (R² of 0.985). However, its performance degrades under out-of-plume conditions due to substantial differences in visual features between the two domains. Consequently, direct across-domain transfer performs poorly, whereas region of interest (ROI)-level fine-tuning substantially improves performance for out-of-plume images (R² of 0.621). Despite these promising results, fundamental limitations remain. Image-based PM₁ estimation is intrinsically ill-posed due to the non-unique mapping between visual observations and particle mass. Overall, the framework enables an integrated workflow from smoke localization to quantitative PM₁ estimation using image data alone, offering a scalable solution for biomass burning monitoring and air quality assessment while highlighting the fundamentally indirect nature of image-based PM₁ inference relative to spatially resolved retrievals. Full article

Submicron particulate matter in the 0.1–1.0 µm range is difficult to remove using conventional air pollution control devices because of its low capture efficiency. Condensation-induced particle enlargement has therefore been proposed as a preconditioning method to increase particle size before collection. This study aims to numerically investigate the condensation-induced growth of submicron particles in a cylindrical tube under different pressure-recovery conditions and to clarify how pressure-controlled supersaturation affects droplet-growth kinetics. A three-dimensional computational fluid dynamics (CFD) model was developed in ANSYS Fluent by coupling the Discrete Phase Model (DPM) with a custom User-Defined Function (UDF) growth law to predict droplet growth, condensation time, and associated heat and mass transfer characteristics. Initial particle diameters of 0.1–1.0 µm were examined for growth to a target diameter of 5 µm under initial pressure conditions of 0.5–0.9 bar followed by recovery to 1 atm, corresponding to calculated nominal supersaturated RH values of 202.65–112.58%, respectively. The results show that pressure-induced supersaturation is the dominant factor controlling condensation kinetics. Lower initial pressures resulted in shorter condensation times and higher mass and heat transfer rates. For an initial diameter of 0.5 µm, the condensation time decreased from approximately 0.1434 s at 0.9 bar to 0.0167 s at 0.5 bar, corresponding to an 88.35% reduction. These findings indicate that pressure-controlled supersaturation can significantly accelerate submicron particle enlargement and provide design guidance for condensation-assisted fine-particle removal technologies. Full article

While the association between criteria air pollutants and sleep duration is well-documented, evidence on the impact of fine particulate matter (PM_2.5) chemical components on sleep remains limited. This study investigated the effects of intermediate- (6-month) and long-term (2-year) exposure to PM_2.5 and its five major components—black carbon (BC), organic matter (OM), sulfate (SO₄²⁻), nitrate (NO₃⁻), and ammonium (NH₄⁺)—on nocturnal sleep and daytime napping duration. We included 19,505 participants aged ≥ 45 years from the China Health and Retirement Longitudinal Study (CHARLS, 2011–2018). Residential PM_2.5 and component concentrations were estimated via the Tracking Air Pollution in China dataset, and sleep data were collected through self-reported questionnaires. Linear mixed-effects models and quantile-based g-computation (qgcomp) were used to assess single- and multi-pollutant effects. Results showed that both intermediate- and long-term exposure to PM_2.5 components was associated with shorter nocturnal sleep and longer daytime napping. Subgroup analyses revealed greater susceptibility among rural residents, solid fuel users, and individuals without pensions. These findings emphasize the need for component-specific PM_2.5 control strategies and targeted public health interventions to reduce sleep-related health inequalities, especially in socioeconomically disadvantaged populations. Full article

Reliable tracking of multi-pollutant air-quality progress is essential for assessing policy effectiveness and health risks, yet most assessments still focus on single pollutants. We analysed population-weighted exposures to fine particulate matter (PM_2.5), nitrogen dioxide (NO₂) and household air pollution (HAP) for Australia and the global average over 1990–2019, using harmonised estimates from a Global Burden of Disease–type framework. Non-parametric LOESS and semi-parametric generalised additive models were applied to characterise long-term trends, and a composite clean-air progress index (CAPI; 1990 = 1) was constructed to summarise joint changes in the three pollutants. Statistical and Monte Carlo methods were used to propagate reported exposure uncertainty into both pollutant-specific trends and the composite index. Globally, exposures to PM_2.5, NO₂ and HAP all declined, and the CAPI fell to around 0.7 by 2019, indicating substantial multi-pollutant improvement relative to 1990. In Australia, NO₂ decreased more rapidly than the global mean, but PM_2.5 showed little long-term decline and the HAP-related metric increased more than three-fold. As a result, Australia’s CAPI rose to approximately 1.6–1.7, with Monte Carlo uncertainty envelopes remaining well above 1 from the early 2000s onwards. Correlation analyses revealed that pollutants improved together at the global scale, but were partially decoupled in Australia, implying that source-specific gains have not translated into aggregate clean-air progress. These findings demonstrate that single-pollutant assessments can obscure important trade-offs and that multi-pollutant, uncertainty-aware indices such as CAPI provide a more informative basis for benchmarking national trajectories against global experience and for guiding integrated clean-air policy. Full article

Urban forests (UFs) are increasingly promoted as a nature-based solution for mitigating particulate matter (PM) pollution, yet their removal performance can vary depending on surrounding emission sources and environmental conditions. Here, we quantified the particulate matter reduction efficiency (PMRE) of UFs located near roads, industrial complexes, and urban areas, together with background forests in South Korea, based on field observations during the late autumn–spring period across two consecutive years (November–May in 2021–2022 and 2022–2023). We applied vector autoregression (VAR) to examine the dynamic relationships between PMRE and meteorological and air pollutant variables across eight representative sites. The results revealed that PM mitigation dynamics were strongly particle-size-dependent and context-specific. Across all sites, ΔPM₁₀ RE was predominantly self-driven, explaining over 90% of its own variance, whereas fine-particle dynamics showed stronger interdependence. In particular, ΔPM_2.5 RE consistently acted as a key mediator, accounting for up to 70%–80% of the variation in ΔPM_1.0 RE depending on source context. Industrial-complex-adjacent UFs exhibited the strongest cross-variable interactions, while urban-core UFs were largely governed by intrinsic mitigation processes. Roadside UFs showed site-specific responses associated with CO and temperature variability. Notably, PMRE responses exhibited damped oscillation patterns across all source contexts, converging toward equilibrium over time, indicating stabilization of mitigation performance following disturbance events. These findings demonstrate that urban forest air-quality benefits are highly context dependent and governed by particle-size-specific dynamics. Our results provide evidence-based guidance for designing and managing urban forests, emphasizing the need for source-specific strategies and prioritization of PM_2.5-oriented mitigation, particularly in industrial and roadside environments where fine-particle interactions are strongest. Full article

Chronic exposure to fine particulate matter (${\mathrm{PM}}_{2.5}$) derived from residential wood combustion is a major environmental health concern in southern Chile and other cold-climate regions. Although ${\mathrm{PM}}_{2.5}$ has been linked to adverse reproductive outcomes, it remains unclear whether sustained exposure induces pregestational uterine alterations that compromise reproductive competence before the first pregnancy. This study evaluated the effects of chronic wood smoke-derived ${\mathrm{PM}}_{2.5}$ exposure on uterine morphology and molecular markers in nulliparous rats. A two-generation exposure model was used to assess cumulative effects. Second-generation (G2) female Sprague Dawley rats continuously exposed from conception were housed in filtered air (FA, control; $n=12$) or ${\mathrm{PM}}_{2.5}$-containing ambient air (NFA; $n=12$) until reproductive maturity (82 days). Uterine horns were analyzed by histology, planimetry, immunohistochemistry, immunofluorescence, and second harmonic generation microscopy. Markers of hypoxia, inflammation, extracellular matrix remodeling, angiogenesis, proliferation, apoptosis, and DNA repair were quantified. Chronic ${\mathrm{PM}}_{2.5}$ exposure increased hypoxia-inducible factor 1$\alpha$, tumor necrosis factor-$\alpha$, vascular endothelial growth factor A, and collagen types I, III, and IV, while transforming growth factor-$\beta$ expression and Ki-67-positive proliferating cells were reduced. Exposed rats showed increased apoptosis and decreased nuclear expression of ${\mathrm{O}}^6$-methylguanine-DNA methyltransferase, indicating impaired DNA repair capacity. Second harmonic generation imaging demonstrated increased collagen deposition with marked fibrillar disorganization. These findings indicate that chronic wood smoke-derived ${\mathrm{PM}}_{2.5}$ exposure induces hypoxia-driven structural and molecular alterations in the uterus of nulliparous rats before first pregnancy, including extracellular matrix remodeling, inflammatory imbalance, angiogenic dysregulation, reduced proliferation, and compromised DNA repair, suggesting early disruption of uterine homeostasis and increased susceptibility to adverse reproductive outcomes. Full article

This study presents an integrated environmental assessment of plastic waste-to-hydrogen systems with varying CO₂ utilization ratios, combining process-level simulation with life-cycle assessment (LCA). The environmental impacts are evaluated across key categories, including global warming potential (GWP), fine particulate matter formation (PM), fossil resource scarcity (FRC), and water consumption (WC). The results reveal a non-linear relationship between CO₂ utilization and environmental impacts. As the CO₂ utilization ratio increases from the N₂ baseline to moderate levels (CO₂-40 to CO₂-50), environmental impacts decrease due to improved carbon utilization and reduced direct CO₂ emissions. However, further increases in CO₂ utilization lead to a reversal of this trend, with environmental burdens rising significantly due to increased energy and utility demand associated with intensified CO₂ recycling. Process contribution analysis shows that the dominant impact drivers shift from direct CO₂ emissions to utility-related contributions, particularly heat (steam) and electricity, at higher utilization levels. A trade-off analysis between direct CO₂ emissions and utility-related impacts identifies an optimal environmental operating range around CO₂-50. An integrated comparison with techno-economic performance, represented by the minimum hydrogen selling price (MHSP), reveals a divergence between environmental and economic optima. While environmental impacts are minimized at CO₂-40 to CO₂-50, the economic optimum occurs at higher utilization levels (CO₂-60 to CO₂-70). These results highlight that CO₂ utilization acts as a key design variable governing the trade-off between carbon efficiency and energy demand. An optimal compromise region is identified around CO₂-50 to CO₂-60, providing a balanced operating window for both environmental and economic performance. This study demonstrates that maximizing CO₂ utilization is not necessarily optimal from a system-level sustainability perspective and provides practical insights for the design and optimization of integrated plastic waste-to-hydrogen systems. Full article

(1) Background: This study investigated the characteristics and influencing factors of exposure to fine particulate matter (PM_2.5) and its chemical composition among elderly residents, with the aim of revealing potential differences in exposure. (2) Methods: A total of 258 elderly individuals were monitored for 72 h through individual, indoor, and outdoor PM_2.5 measurements. Concentrations were determined, and non-targeted components were analyzed by gas chromatography-mass spectrometry (GC-MS). Through Spearman correlation analysis, generalized linear model, and linear regression to explore the influencing factors. (3) Results: The individual PM_2.5 concentration was higher than both the indoor and outdoor concentrations. A total of 20,962 compounds were detected in personal PM_2.5 samples, 6794 in indoor PM_2.5 samples, among which 4285 compounds were shared between the two sample types. The components were mainly esters, aromatic compounds, and amines. PM_2.5 concentration was correlated with age, housing area, humidifier use, and second-hand smoke exposure. Chemical composition is related to outdoor pollution, furniture material, and daily behavior. (4) Conclusions: The individual PM_2.5 concentration is higher than the environmental concentration, and its chemical composition overlaps with the indoor and outdoor environment, which is jointly affected by demography, living conditions, and daily behavior. Full article

Farmland particulate pollution seriously affects regional atmospheric quality, and exploring efficient field dust control strategies is an urgent need for agricultural ecological protection. This study employed a wind tunnel and online dust monitoring system to investigate the dust reduction effect of straw return in conservation tillage in Beijing farmland under varying wind speeds and precipitation levels, providing theoretical and technical support for straw coverage configuration and dust pollution control. Given the insufficient understanding of the combined impacts of straw coverage, wind speed and precipitation on farmland particulate emissions, this study examined how these key factors jointly affect fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), and total suspended particulate (TSP) emissions. A three-factor, three-level response surface experiment modeled these relationships and identified optimal conditions for suppressing PM emissions—51.35% straw coverage, 3.96 m·s⁻¹ wind speed, and 32.36 mm precipitation—yielding average PM_2.5, PM₁₀, and TSP concentrations of 26.31, 31.71, and 42.43 μg·m⁻³, respectively. Field data showed that the mean absolute errors (MAEs) between predicted and measured concentrations were 0.52–5.80, 0.46–3.93, and 1.83–5.68 μg·m⁻³ for PM_2.5, PM₁₀, and TSP, respectively, corresponding to relative prediction accuracies of 90.42–97.95%, 95.03–98.52%, and 93.10–97.21%—indicating strong model accuracy. This approach enhances dynamic monitoring of straw return practices and guides rational field management. By integrating meteorological conditions and particulate emission characteristics, the model can quantitatively assess regional straw coverage and screen optimal straw mulching rates. It provides a clear data reference for decision-makers to formulate targeted dust prevention policies, standardize straw return regulation, and advance eco-friendly and sustainable agricultural production. Full article

Soil organic matter (SOM) dynamics in tropical montane ecosystems remain poorly understood, particularly regarding the relative importance of particulate versus mineral-associated fractions under varying disturbance regimes. This study investigated SOM fraction distribution across the Ericaceous and Afroalpine belts of Bale Mountains National Park, Ethiopia, an Andosol-dominated landscape subject to recurrent fire. Using a stratified sampling design (n = 30 plots) across four vegetation classes (Ericaceous belt, fragmented Ericaceous belt, herbaceous and heathland, and giant Lobelia areas), three fire history categories (<10, 10–25, and >25 years since fire), and three topographic positions (northern slopes, southern slopes, and central plateau), we quantified coarse particulate organic matter (cPOM: 149–2000 μm), fine particulate organic matter (fPOM: 53–149 μm), and mineral-associated organic matter (MAOM: <53 μm). Particulate fractions dominated the SOM pool, with cPOM and fPOM together accounting for >99% of measured organic carbon. Multivariate ordination revealed a primary gradient (PC1, 61.7%) contrasting particulate-dominated soils in less disturbed areas with relatively MAOM-enriched soils in fire-impacted and fragmented zones. A global comparison reveals a profound stability gap: the Bale Mountains utilize <2% of the mineral stabilization potential of comparable Andosols, demonstrating that extreme fire frequency (<25 yr return interval) overrides even the most reactive mineralogy. We critically evaluate whether standard size-based fractionation adequately captures mineral-associated carbon in volcanic soils and discuss methodological limitations. These results provide baseline data for conservation planning in this biodiversity hotspot and underscore the need for fire management strategies that balance ecological integrity with carbon storage objectives. Full article

Atmospheric arsenic (As) poses significant health threats in heavily polluted urban environments. However, the size-resolved distribution of water-soluble arsenic (WSAs) in atmospheric particulate matter, as well as the size-dependent variation in As concentration and solubility under contrasting haze and non-haze conditions, remains insufficiently characterized. This study investigated the concentration, size distribution, water solubility, sources, and health risks of particulate-bound As and WSAs in Beijing from April 2014 to February 2015. The annual mean PM_0.1–18 concentration was 136.96 ± 54.21 μg·m⁻³, with significantly higher levels observed during haze episodes (179.61 ± 41.71 μg·m⁻³) compared to non-haze periods (118.00 ± 49.42 μg·m⁻³). The annual mean concentration of As was 6.42 ± 3.69 ng·m⁻³, exceeding both WHO guidelines and Chinese standards during haze periods, while WSAs averaged 4.54 ± 2.50 ng·m⁻³. Distinct size distribution patterns were observed: As displayed, a unimodal fine-mode peak (0.32–0.56 μm) was observed during haze periods and a bimodal distribution during non-haze conditions, whereas WSAs followed comparable size-dependent behavior, reflecting shifts in dominant emission sources and atmospheric processes. The average WSAs/As ratio (0.72 ± 0.07) indicated high As solubility and strong associations with secondary species and anthropogenic emissions. Size-resolved analysis revealed that As was preferentially enriched in fine particles, particularly during haze episodes, whereas coarse particles became more prominent under non-haze conditions, especially in spring, likely driven by regional dust transport and its interactions with anthropogenic emissions. Deposition modeling based on the ICRP framework showed that As and WSAs were primarily deposited in the headway (HA: 0.68 and 0.32 ng·h⁻¹, respectively), followed by the alveolar region (AR: 0.29 and 0.20 ng·h⁻¹, respectively). Fine particles enhanced deposition in deeper lung regions during haze episodes, whereas coarse particles contributed more to upper airway deposition under non-haze conditions. Although inhalation carcinogenic risks remained within acceptable limits (10⁻⁶–10⁻⁴), risks were 1.60 times higher during haze periods, with adults bearing the greatest exposure burden. These findings demonstrate that haze conditions substantially alter the size distribution, solubility, and health risks of atmospheric arsenic, and provide a scientific basis for developing size-resolved and haze-targeted heavy metal monitoring strategies in urban environments subject to significant anthropogenic pollution. Full article

(1) Airborne fine particulate matter (PM_2.5) poses a growing threat to poultry production by impairing intestinal health, disturbing microbial balance, and reducing growth performance. Rosmarinic acid (RA), a natural polyphenol with antioxidant, anti-inflammatory, and gut microbiota-regulating properties, can effectively maintain intestinal homeostasis. To date, its protective effects against PM_2.5-induced intestinal injury in broilers remain largely unclear. This study investigated whether dietary RA supplementation mitigates intestinal damage and microbiota dysbiosis caused by PM_2.5 in broilers and explored the related mechanisms. (2) A total of 144 21-day-old broilers were randomly allocated to three groups, control (CON), PM_2.5 exposure (PM), and PM_2.5 exposure plus rosmarinic acid (RA), with six replicates of eight broilers each. (3) Results indicated that PM_2.5 exposure severely impaired growth performance, whereas dietary RA significantly increased average daily feed intake and average daily gain, decreased the feed-to-gain ratio, and elevated final body weight in broilers. RA significantly attenuated PM_2.5-induced intestinal inflammation, as evidenced by reduced expression of inflammatory cytokines (IL-6 and IFN-γ) and downregulation of key components in the TLR4 signaling pathway (TLR4, MyD88, and NF-κB). Inhaled PM_2.5 exposure impaired the intestinal epithelial barrier, marked by decreased mRNA levels of MUC2 and CLDN1 and increased caspase3 expression. Dietary RA treatment effectively restored these indicators, suggesting its role in maintaining epithelial integrity. Furthermore, RA reshaped the gut microbiota structure, altering both α- and β-diversity. Notably, RA led to a higher proportion of potentially health-promoting bacterial taxa, including Lactobacillus, V9D2013_group, and Oscillospirales, while reducing opportunistic pathogens like Shuttleworthia. (4) In conclusion, RA alleviates PM_2.5-induced intestinal inflammation, reinforces the epithelial barrier, and modulates the intestinal microbiota in broilers, likely through inhibition of the TLR4/NF-κB signaling. These findings reveal a novel mechanism by which RA mitigates pollutant-induced intestinal injury via gut microbiota modulation and TLR4/NF-κB suppression, offering new insights into the gut–lung axis in avian species. Full article

Coordinated control of fine particulate matter (PM_2.5) and ozone (O₃) is an urgent national strategic priority for China’s air pollution governance. Biogenic volatile organic compounds (BVOCs) are important precursors of O₃ and secondary organic aerosol (SOA). To quantify the species-specific impacts of BVOCs, we used the Model of Emissions of Gases and Aerosols from Nature (MEGAN, v3.2) and the Community Multiscale Air Quality (CMAQ, v5.3.2) model to investigate BVOC emission characteristics and their modulating effects on summertime O₃ and PM_2.5 across China. In July 2020, total BVOC emissions were 6.50 × 10⁶ tons, showing a spatial pattern that decreased from southeast to northwest and a unimodal diurnal variation that peaked at 13:00–14:00. BVOC emissions significantly promoted O₃ formation, with a maximum concentration increment of 47.36 μg m⁻³ in VOC-limited regions such as the Sichuan Basin (SCB) and Yangtze River Delta (YRD). Their impact on PM_2.5 was limited, with most regional increments below 3 μg m⁻³. Isoprene dominated O₃ enhancement, while monoterpenes acted as the key BVOC for PM_2.5 via SOA formation. Anthropogenic emission reductions elevated the relative contribution of BVOC emissions to air pollution in most regions. These findings highlighted the importance of considering BVOC emissions and their species-specific effects in China’s coordinated PM_2.5-O₃ control strategies for more precise air quality management. Full article

Wildfire smoke contributes substantially to episodic PM_2.5 exposure, yet outdoor measurements may not represent indoor conditions. We analyzed indoor PurpleAir sensors and nearby outdoor monitors from U.S. residences (2019–2024) to estimate smoke-day changes in the outdoor–indoor PM_2.5 difference and characterize heterogeneity across regions. After data quality control and the application of completeness criteria, 509 monitor pairs contributed 250,873 monitor-days. Smoke days were assigned using the NOAA Hazard Mapping System smoke-plume polygons. Pair-specific time-series models estimated smoke-day changes in the outdoor–indoor PM_2.5 difference, which were pooled using random-effects meta-analysis; heterogeneity was summarized by clustering indoor and outdoor smoke–non-smoke contrasts. In the unadjusted summary, the mean outdoor PM_2.5 was 8.61 vs. 5.63 µg/m³ on smoke vs. non-smoke days and the mean indoor PM_2.5 was 6.33 vs. 5.09 µg/m³, reflecting an increase in the mean outdoor–indoor difference from 0.54 to 2.27 µg/m³ (p < 0.001). The pooled smoke-day effect on the outdoor–indoor difference was 0.88 µg/m³ (95% CI: 0.80, 0.96). Clustering identified four distinct response patterns, most commonly outdoor increases exceeding indoor increases, with smaller subsets showing extreme outdoor amplification or net indoor reductions under modest outdoor increases. These results indicate that indoor protection during smoke episodes is common but variable and support exposure characterization beyond outdoor concentrations alone. Full article