Search Results (3,961)

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

The aim of this research was to quantify the capacity of different shrub species to remove atmospheric CO₂, to adsorb particulate matter and to dissipate latent heat through transpiration. A total of 308 established plants comprising Deutzia scabra, Elaeagnus × ebbingei, Euonymus japonicus, Forsythia × intermedia, Laurus nobilis, Ligustrum vulgare, Pittosporum tobira, Prunus laurocerasus and Viburnum tinus were selected in Lugano (Switzerland) and Bolzano (Italy). Stem diameter, crown radius, Leaf Area Index, net CO₂ assimilation per unit leaf area (A_leaf), transpiration, and stomatal conductance (g_s) were measured during spring, summer, and fall. The net CO₂ assimilation per unit of crown projection area and per plant were calculated by upscaling A_leaf using a multilayer model. Latent heat dissipation was calculated using the Penman–Monteith equation. The amount of PM trapped on leaves was measured using a gravimetric method. Differences in leaf area and leaf gas exchange among species affected their capacity to deliver specific ecosystem services. Forsythia, Pittosporum, Elaeagnus and Deutzia removed about 40% more CO₂ per unit crown projection area than Laurus, Ligustrum, and Euonymus. Latent heat dissipation by shrubs was, on average, 130 W m⁻², which is comparable to that of tree species. PM removal per unit leaf area was higher in species with sparse canopies and rough leaf surfaces. Full article

Dimethyl ether (DME) is a promising alternative fuel for compression ignition (CI) engines due to its potential to simultaneously reduce nitrogen oxides (NO_x) and particulate matter (PM) emissions while maintaining diesel-equivalent power. However, its combustion behavior under varying injection timing and exhaust gas recirculation (EGR) conditions remains insufficiently characterized for practical calibration. This study investigates the combustion, emissions, and performance of DME relative to diesel using a fully instrumented John Deere 6068CI550 single-cylinder research engine modified for high-pressure common-rail DME operation. Baseline tests were conducted at three ISO 8178 C1 steady-state modes with matched combustion phasing, load, and EGR to isolate fuel property effects. Injection timing and EGR sweeps were then performed at 1600 rpm and 50% load. Results show that DME produces 10–35% lower NO_x and orders-of-magnitude lower PM than diesel while maintaining comparable thermal efficiency. DME exhibits a single-stage premixed heat release structure with reduced peak apparent heat release rates and 4–5° shorter combustion durations than diesel. Stable combustion was sustained up to 55% EGR, beyond which incomplete combustion increased carbon monoxide (CO), total hydrocarbons (THC), and fuel consumption. Optimal low-emission operation occurred near CA50 ≈ 16° ATDC and EGR levels of 30–40%. These findings demonstrate DME’s ability to mitigate the traditional diesel NO_x–PM tradeoff and support its viability as a low-emission CI fuel. Full article

Airborne particulate matter pollution has posed severe threats to public health, while conventional air filtration materials suffer from non-biodegradability and poor structural stability. Herein, a series of eco-friendly konjac glucomannan/sodium alginate (KGM/SA) composite aerogels reinforced by silk microfibers (SFs) were fabricated via freeze-drying. The extracted SF had a concentrated diameter distribution of 500 nm, with a well-preserved crystalline structure and the β-sheet secondary structure of natural silk. Results demonstrated that SF incorporation effectively regulated the pore structure, with reduced pore sizes, and an optimized uniform and compact cobweb-like porous network was achieved at 70% SF addition (KSSF₇₀), with a maximum compressive stress of 78.89 kPa at 60% strain, a PM₁₀ filtration efficiency of 99.8%, and a PM_2.5 efficiency of 71.2%. Also, the removal efficiency of particles < 0.3 μm was boosted from 26% to 47% compared with the KGM/SA aerogel. Furthermore, the calculated quality factor met mainstream commercial standards. These findings guided SF use in improving the pore structure of biomass aerogels for enhanced air filtration performance. Full article

The combustion of candles is known to emit various air pollutants, including particulate matter (PM) and volatile organic compounds (VOCs), into the air. This study characterizes emissions of these pollutants from natural and synthetic candles in a standard, sealed, unventilated residential environment. In addition, computational fluid dynamics (CFD) modeling was used to study the potential effects of inlet air velocity on a paraffin candle flame. A laminar diffusion flame model simulated the distributions of temperature, CO₂, and H₂O. A Testo DiSC mini air sampler was used for ultrafine particles and Lung-Deposited Surface Area (LDSA) data collection, and a CEM DT-9881 sampler was used for recording larger particle number concentrations, temperature, and relative humidity. VOC sorbent tubes were used for the collection of individual and total VOCs. Study findings showed that natural candles produced significantly (p < 0.05) higher LDSA ranges (mean 195.2 µm²/cm³) and ultrafine particle concentrations (mean 8.4 × 10¹¹ No/m³), while paraffin wax synthetic candles exhibited higher 0.3–10 µm PM concentrations (mean 2.0 × 10⁷ No/m³). CFD modeling showed that increasing air velocity produced a shorter, more compact flame and reduced CO₂ and H₂O mass fractions due to enhanced mixing and aerodynamic dilution, highlighting the strong interaction between airflow, temperature, and product formation in laminar paraffin flames. Full article

Interelectrode distance is a key structural parameter of passive air purifiers based on the particle sink effect, but its influence on particulate matter (PM) dynamics remains unclear. This study experimentally investigates the concentration evolution of PM in four size ranges (≤1, 1–2.5, 2.5–10, >10 μm) under different interelectrode distances in a confined space, combined with multifractal detrended fluctuation analysis (MF-DFA) and coupling detrended fluctuation analysis (CDFA). Results show a non-monotonic effect of interelectrode distance on PM removal, with an optimal range of 3–4 cm. Submicron PM (≤1 μm) exhibits a short-term rise followed by a decline; the rising phase duration increases with larger interelectrode distance, revealing a competition between fragmentation-induced release of large particles and their capture. MF-DFA indicates that concentration series of each PM size range display typical multifractal behavior, and the skewness direction of the multifractal spectrum varies with interelectrode distance and particle size. CDFA further reveals strong coupled multifractal features among concentration series of the same particle size at different distances, with coupling strength increasing with particle size. The skewness evolution of the coupled multifractal spectrum quantitatively uncovers how the interelectrode distance, through non-monotonic matching between electric field intensity and dust collection area, regulates PM concentrations toward higher or lower values. This study provides a mechanistic basis for optimizing passive air purifier structures. Full article

This study evaluates the environmental performance of 34 single-engine light utility helicopters across five operational phases: ground idle departure, ground idle arrival, takeoff, approach, and landing-takeoff (LTO). A hybrid multi-criteria decision-making (MCDM) framework integrating interval type-2 fuzzy sets with the Analytic Hierarchy Process (AHP), VIKOR, and TOPSIS was applied to ensure robust and reliable assessment. Six criteria: shaft horsepower (SHP), fuel flow, hydrocarbon (HC), carbon monoxide (CO), particulate matter (PM), and nitrogen oxides (NOx) were considered to capture both engine performance and environmental impact, with relative importance determined through AHP. VIKOR generated a compromise ranking, while TOPSIS validated the results. The analysis revealed that the HUGHES 500 (DDA250-C18, A34), HUGHES 501 (DDA250-C20B, A29), and BELL 206B-1 (DDA250-C20, A32) engines achieved the best environmental performance due to low fuel consumption and reduced emissions across NOx, PM, HC, and CO. In contrast, engines such as K-1200 (T53 17A-1, A1) and BELL UH-1H (T53 L13, A2) performed the poorest, with high fuel flow and elevated emissions. Sensitivity analysis showed minimal changes in rankings when the NOx weight was varied, confirming the robustness of the framework. These results highlight that emissions and fuel efficiency are more critical than engine power in determining environmental sustainability. 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

Background: Air pollution is a major environmental determinant of respiratory health and a significant contributor to the global burden of childhood asthma. Although several recent narrative and systematic reviews have examined environmental triggers of asthma, highlighting air pollution as a consistent risk factor across diverse populations and study designs, recent epidemiological evidence—including multicenter cohort studies and region-specific analyses from Europe and Greece—has not been systematically synthesized. Objective: To systematically review recent epidemiological evidence (2000–2025) on the association between ambient air pollution and childhood asthma incidence and exacerbations, with emphasis on European and Greek populations. Methods: Following PRISMA guidelines, we systematically reviewed observational studies published between 2000 and 2025 in PubMed, Scopus, Web of Science, BMC, and Google Scholar. Studies evaluating quantitative exposure to PM_2.5, PM₁₀, NO₂, O₃, or SO₂ and asthma incidence, prevalence, or exacerbations in children (≤18 years) were included. Evidence was synthesized by pollutant type, exposure window, geographic region, and study design. Results: Twenty-four studies involving more than 3.5 million children were included. Consistent associations were observed across international and European cohorts between long-term exposure to PM_2.5, PM₁₀, and NO₂ and increased asthma incidence. Risk estimates typically ranged from 15% to 30% increases in asthma incidence per 10 μg/m³ increase in long-term exposure to PM_2.5 or NO₂, as reported across multiple cohort analyses. Early-life exposure showed the strongest effects on asthma development and lung function decline. European and Greek studies demonstrated comparable trends, highlighting increased hospitalizations and symptom burden in urban populations despite pollutant concentrations often below current regulatory thresholds. Short-term pollution peaks were additionally associated with increased asthma exacerbations and hospital admissions, particularly during seasonal episodes of elevated particulate matter and ozone concentrations. Conclusions: This review provides an updated synthesis of 21st-century evidence demonstrating that ambient air pollution is a major and modifiable determinant of childhood asthma. The consistency of findings across regions, combined with limited longitudinal evidence from Greece, highlight the importance of improved air-quality management and continued public-health efforts to reduce exposure and the need for enhanced epidemiological monitoring. 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