Search Results (214)

Climate change is intensifying heatwaves, wildfire activity, and dust transport in the Mediterranean region, yet their combined impacts on mortality remain poorly characterized. Associations between heatwaves, ambient air pollution, source-specific particulate matter, and cardiovascular and respiratory mortality were investigated across Greece at the NUTS2 regional level. Monthly mortality counts for 2014–2022 were analyzed using generalized linear Poisson regression models adjusted for region, year, age group, sex, relative humidity, and population size. Analyses included conventional pollutants (PM_2.5, PM₁₀, and O₃), wildfire-related particulate matter, and Saharan dust-related particulate matter, while multiplicative interaction terms were used to evaluate modification of pollution effects during heatwave conditions. All major pollutants were positively associated with cardiovascular and respiratory mortality, with the strongest and most consistent effects observed for wildfire-related particulate matter. Heatwave–pollution interaction terms were generally weak, indicating that the combined effects of heat and air pollution were predominantly additive rather than strongly synergistic at the national scale. Spatial analyses revealed substantial regional heterogeneity, with stronger associations identified in parts of northern mainland Greece and selected urbanized regions. These findings highlight the importance of considering wildfire smoke and desert dust separately from conventional air pollutants and support the development of integrated climate-health adaptation strategies in Greece and other climate-vulnerable Mediterranean regions. Full article

Accurate detection in the early stages of a fire is a crucial prerequisite for the efficient implementation of fire suppression and emergency rescue operations. Its accuracy and timeliness directly affect the control of disaster loss severity. Traditional fire detection methods mainly include three categories, which are manual inspection, sensor detection, and visual recognition. However, manual inspection is restricted by labor costs and time efficiency, making it difficult to achieve large-scale, high-frequency and real-time fire monitoring. Sensor detection is easily interfered by environmental factors such as temperature, humidity, and dust, leading to frequent false alarms and missed alarms. Visual recognition technology has shortcomings in aspects such as detailed feature perception, dynamic scene modeling, and reasoning robustness in complex environments, making it difficult to meet the requirements of high-precision detection. To address these issues, this study innovatively proposes a lightweight fire and smoke detection model based on semantic enhancement and frequency domain perception modeling, which is named the SemaFire you only look once (SemaFire-YOLO) model. The model constructs a large language and vision assistant (LLaVA) semantic guidance module, which uses a large language model to understand and guide the semantic features of images, thereby enhancing the saliency representation intensity of small and weak target regions. Then, a Haar wavelet-based downsampling module is adopted, which compresses spatial information while preserving high-frequency features such as flame edges and smoke textures, improving the accuracy of target recognition. Next, the convolution modulation mechanism is introduced to replace the traditional attention mechanism, enhancing the overall modeling efficiency and reducing computational overhead. Finally, a Dynamic Tanh normalization module is adopted to replace the batch normalization module in the traditional YOLO algorithm, strengthening the model’s representation stability and reasoning robustness under unstable input distributions. Experimental results show that the SemaFire-YOLO model achieves a mean average precision (mAP@0.5) of 64.30% on the fire image dataset, which is 0.8, 2.0, 0.6, and 3.8 percentage points higher than that of mainstream models such as YOLOv5n, YOLOv8n, YOLOv11n, and YOLOv12n, respectively. It exhibits better boundary detection capability and practical deployment potential. Through visual analysis, the results indicate that the improved SemaFire-YOLO model achieves more accurate detection and higher confidence in actual complex scenarios, further verifying the model’s robustness and accuracy in complex scenarios such as low contrast and dynamic fire conditions. Full article

Dust concentration during intermittent processing of powder and granular materials is characterized by high-frequency abrupt changes, local accumulation, and complex coupling among multiple sensors. Existing forecasting models still exhibit limitations in modeling global dependencies and characterizing local trends. To address these issues, this paper proposes an iTransformer-based dust concentration forecasting model that integrates a dual-stage feed-forward network and a DLinear branch. With iTransformer as the backbone network, the proposed model captures the coupling relationships among multi-source sensing signals through variate-wise modeling. A progressive dual-stage feed-forward feature refinement mechanism is constructed to enhance the model’s representation capability for transient variations and peak fluctuations in dust concentration. In addition, a collaborative modeling framework consisting of an iTransformer main branch and a DLinear auxiliary branch is designed to jointly learn global nonlinear features and local linear trends. An adaptive gated fusion mechanism is further introduced to dynamically allocate the contribution weights of different branches according to sequential characteristics. Experiments were conducted on a public 1 Hz smoke-sensing dataset, which was used as a proxy benchmark for high-frequency multivariate PM2.5 forecasting rather than direct industrial dust data. Under the setting of a 300-step input length and a 60-step forecasting horizon, the proposed model achieves an MSE of 1.8292 × 10⁻³, an MAE of 0.0334, an RMSE of 0.0428, an MAPE of 0.0177, and an R² of 0.9744, outperforming the compared baseline models in overall performance. The results indicate that the proposed method improves overall forecasting accuracy and provides a methodological reference for sensor-driven particulate concentration forecasting and early warning, while further validation using field data from actual powder and granular material processing workshops is still required before practical deployment. Full article

Occupational lung diseases remain a significant public health concern in mining populations, particularly in high-exposure environments. This study examined occupational exposure profiles and respiratory health outcomes among surface and underground miners in Mpumalanga Province. A cross-sectional analytical design was employed among 239 mine workers. Data on socio-demographic characteristics, occupational exposures, behavioural factors, and respiratory outcomes were analysed using descriptive statistics, chi-square tests, and logistic regression models. Underground miners were significantly more likely to report high dust exposure (44.9% vs. 24.1%), poor ventilation (60.6% vs. 39.3%), and longer working hours (>8 h: 68.5% vs. 50.0%) compared to surface miners. They also reported a higher prevalence of respiratory symptoms, including chronic cough (45.7% vs. 25.9%), shortness of breath (41.7% vs. 23.2%), wheezing (34.6% vs. 18.8%), and diagnosed lung disease (23.6% vs. 9.8%). Multivariable analysis showed that underground mining (AOR = 1.92; 95% CI: 1.08–3.41), smoking (AOR = 1.78; 95% CI: 1.02–3.11), and high dust exposure (AOR = 2.89; 95% CI: 1.45–5.76) were independent predictors of chronic cough. A significant interaction between smoking and underground mining (AOR = 2.74; 95% CI: 1.32–5.68) further amplified respiratory risk. Additionally, underground miners demonstrated lower levels of knowledge (48.8% vs. 66.1%) and poorer preventive practices (44.1% vs. 64.3%). These findings highlight the combined influence of occupational and behavioural factors on respiratory health and highlight the need for integrated interventions to reduce the burden of occupational lung diseases. Full article

Background: Chronic Obstructive Pulmonary Disease (COPD) is a progressive, incurable condition marked by irreversible airflow limitation and systemic inflammation. Cardiovascular comorbidities, particularly pulmonary hypertension (PH), exacerbate disease severity. While cigarette smoke is a well-known trigger, non-smoking-related inflammatory pathways remain underexplored. This study investigates vascular remodeling in a murine model of inflammation induced by chronic exposure to house dust mite Farinae (HDM). Methods: Female C57BL/6 mice were sensitized with HDM in Freund’s Complete Adjuvant and challenged intranasally with HDM for six weeks. Lung inflammation, mucus hypersecretion, and vascular remodeling were evaluated via BAL, histology, immunofluorescence, echocardiography, gene expression, proteomics, and FlexiVent pulmonary function tests (FlexiVent system). Results: HDM exposure induced a mixed inflammatory response, with elevated neutrophils, monocytes, and lymphocytes in BALF. Mucus hyperproduction (increase in MUC5AC/MUC5B) and impaired lung function (reduced FEV0.1/FVC) were observed. Vascular remodeling was evidenced by increased wall thickness, α-SMA expression, and collagen deposition. Proteomic analysis revealed dysregulation of endothelial markers and protease/antiprotease imbalance. HIF1-α was significantly upregulated in lung tissue and correlated with vascular and epithelial remodeling. Conclusions: Chronic HDM exposure in mice recapitulates key features observed in subsets of COPD and PH, including inflammation-driven airway and vascular remodeling. HIF1-α emerges as a central regulator, linking hypoxia to structural changes. This model offers insights into the effect of non-smoking-related inflammatory pathways on bronchial and vascular remodeling that are potentially relevant for subgroups of COPD patients and highlights HIF1-α as a potential therapeutic target. Full article

Rotary kiln shell temperature monitoring is essential for metallic shell protection and refractory lining maintenance in high-temperature industrial processes, while smoke, dust, thermal diffusion and non-kiln heat sources make valid shell temperature extraction difficult. This study develops a multi-scale cascaded network with low-resolution space-to-depth downsampling (MSC-LSTD) for infrared kiln shell segmentation and temperature recognition. Global infrared thermal images and local laser temperature measurements are used to construct a calibrated rotary kiln infrared dataset, and predicted kiln shell masks are mapped to temperature matrices for valid shell temperature analysis. MSC-LSTD achieves 99.82% aAcc, 99.14% mAcc and 97.03% mIoU on the rotary kiln infrared dataset, showing robust segmentation performance under complex thermal interference. The proposed framework provides a practical image-based solution for kiln shell overheating warning and refractory lining degradation assessment. Full article

This study analyzes a balanced panel of 41 Yangtze River Delta cities from 2006 to 2021 to assess whether and why economic growth has decoupled from industrial pollution. Furthermore, this study proposes a two-dimensional decoupling framework that combines Tapio elasticity with development stages, quantifies driver contributions using an LMDI–Tapio decomposition, and estimates spatial β-convergence in pollution intensity. Key findings include the following: (1) By 2021, all YRD cities exhibit decoupling, with heterogeneity across pollutants and cities. (2) Technological progress effect is the dominant enabler of decoupling, while economic development poses a significant barrier. (3) Industrial sulfur dioxide, smoke and dust intensity, and the composite industrial pollution index show notable spatial β-convergence, with smoke and dust intensity converging most rapidly. The results inform technology-focused policies and cross-city coordination in the YRD. Full article

Explosive ordnance (EO), including AXO (abandoned explosive ordnance), IEDs (improvised explosives devices), and UXO (unexploded ordnance), are widely recognised for their blast and fragmentation hazards, but they also represent a persistent and under-addressed source of occupational chemical exposure for explosive ordnance disposal (EOD) personnel. EOD core activities liberate mixed metals and energetic chemicals, resulting in exposures that are multi-route (inhalation of dusts and fumes, dermal loading amplified by sweat and glove occlusion, and ingestion via hand-to-mouth transfer during eating, drinking, or smoking) and multi-temporal (repeated low-dose background plus task-driven spikes), as well as chemically complex. Clinically, this can present as syndromic overlap across acute and chronic domains, with symptoms that are easily misattributed to heat stress, dehydration, infection, or fatigue. Acute effects of concern include neurotoxic presentations (headache, dizziness, confusion, tremor, and seizure), respiratory and mucosal irritation following dust or fume events, gastrointestinal symptoms, and patterns suggestive of acute hepatic or renal stress, particularly when high-intensity tasks occur in hot environments that compound physiologic strain. Chronic outcomes relevant to repeatedly exposed EOD personnel include renal function decline, neurocognitive effects that can degrade operational decision making and safety, persistent haematologic abnormalities, and endocrine disruption signals, with long-latency risks requiring cautious interpretation given sparse longitudinal data and confounding co-exposures. This review synthesises the current evidence base through an EOD lens and translates it into pragmatic clinical and programmatic actions: task-based exposure characterisation; tiered biomonitoring and medical surveillance aligned to operational tempo; incident-triggered assessment pathways after high-residue events; and prevention strategies that work under field constraints, including contamination control zones, hygiene enforcement, glove and respiratory protection optimisation, tool and vehicle decontamination, and measures to prevent secondary transfer and take-home exposure. The central takeaway is practical: EOD programs can reduce morbidity and improve readiness by treating explosive ordnance as a chemical mixture exposure problem, adopting mixture-aware clinical triage, and embedding surveillance and controls that match how EOD work is actually performed. Full article

Fine particulate matter PM_2.5 continues to pose a critical public health risk in Northern Thailand, particularly in Chiang Mai, where traditional filtration methods often face limitations in cost and efficiency for large-scale applications. This study introduces a novel “Evaporator Condensation Growth Particle Scrubber (ECGP)” designed to enhance the collection efficiency of sub-micron particles by enlarging their physical size through a pressure-driven growth mechanism. The ECGP system utilizes synergistic effects between solid nuclei, high relative humidity, and mechanical pressure modulation. The ECGP system integrates solid nuclei, ~95% relative humidity and mechanical pressure modulation within a single chamber. Using incense smoke as a PM surrogate, the process utilizes controlled adiabatic cycles to induce stable heterogeneous condensation. The results indicate that the integrated process effectively shifts particle size distribution, reducing the PM_2.5/PM₁₀ mass ratio from 1.00 to 0.83. This indicates that approximately 17.5% (with a standard deviation < 1% across 10 trials, p < 0.05) of the fine mass successfully transitioned into the larger, more filterable PM₁₀ fraction and exhibited high physical stability and resistance to re-evaporation, effectively overcoming the low-efficiency threshold (typically <10%) of standard mechanical scrubbers and cyclones for sub-micron dust. This study concludes that ECGP technology offers a promising, cost-effective alternative for improving indoor air quality in large public infrastructures by leveraging particle inertia for enhanced removal, providing a scalable solution to the persistent smog crisis. Full article

Search and rescue (SAR) missions in collapsed or underground environments remain challenging due to GPS unavailability, which hinders localization and autonomous navigation. Systems that rely on single-sensor inputs or structured settings often degrade under smoke, dust, or dynamic clutter. This paper presents an autonomous ground robot for GPS-denied SAR that integrates low-cost thermal, visual, inertial, and acoustic cues within a unified, computation-efficient architecture. The stack combines Thermal–Visual Odometry (TV–VO) with Zero-Velocity Updates (ZUPT) for drift-resistant localization, RescueGraph for multimodal survivor detection, and a Proximal Policy Optimization (PPO) planner for adaptive navigation under uncertainty. Across simulated disaster scenarios and benchmark corridor runs, the system shows embedded-feasible runtime behavior and supports return to base without external beacons under the evaluated conditions. Quantitatively, TV–VO+ZUPT reduces drift in short internal evaluations, while RescueGraph attains an F1-score of 0.6923 and an area under the ROC curve (AUC) of 0.976 for survivor detection. At the system level, the integrated navigation stack achieves full mission completion in the reported SAR-style trials, while the separate A*/PPO comparison highlights a trade-off between completion rate, traversal time, and collisions. Overall, the results support the practical promise of a low-cost sensor-fusion and learning-assisted navigation framework for GPS-denied SAR robotics. Full article

Atmospheric aerosols influence the terrestrial carbon cycle through diverse radiative and biogeochemical effects, yet their net impact on vegetation productivity remains contentious and region-specific. To address this, we analyzed the spatiotemporal coupling between aerosol optical depth (AOD) and net primary productivity (NPP) over three African biomes (2013–2023), using multi-source datasets (MODIS, CERES, ERA5, CRU TS). We explicitly distinguished statistically significant relationships (p < 0.05) from non-significant ones when interpreting correlation patterns. Because AOD is an optical measure and does not provide aerosol composition, interpretations involving dust versus smoke are treated as qualitative and indirect. Through structural equation modeling (SEM), we identified two contrasting mechanistic pathways: in the humid Congo Basin rainforest, aerosols were associated with lower NPP via a cooling-mediated pathway (increased cloud albedo leading to reduced temperature and light availability), whereas in the arid savanna, they were associated with more substantial limitations on NPP via a warming-aggravated pathway (increased temperature and potentially coupled water stress). SEM fit was poor for the semi-arid South African plateau, underscoring the dominant role of water availability in strongly water-limited systems. This framework reconciles the paradox of dual aerosol effects by demonstrating that the net impact is dictated by regional climate context. Overall, our conclusions emphasize context-dependent associations rather than direct causal attribution from correlations alone. Our findings provide a process-based understanding that is critical for improving carbon cycle models and for formulating targeted climate adaptation strategies in Africa. Full article

Cellular senescence (CSEN) is caused by a variety of factors that trigger complex molecular pathways. These include telomere shortening, oncogene activation and replicative stress, as well as DNA damage caused by genotoxic anticancer drugs and endogenous and exogenous genotoxins. Here, we review the induction of CSEN by exogenous genotoxic insults resulting from food and environmental exposures. The available data show that genotoxins/carcinogens in tobacco smoke and smokeless tobacco, in the environment, in food, beverages and life-style products induce CNS. The exposures include N-nitroso compounds, polycyclic aromatic hydrocarbons, heterocyclic aromatic amines, acrylamide, heavy metals, fine dust, mycotoxins, phytotoxins, and phycotoxins. Also, heme in red meat contributes to CSEN as it catalyzes the formation of genotoxic species in the colon. Induction of CSEN by external genotoxins/carcinogens is bound on the DNA damage response pathway (DDR), which relies on activation of the ATM/ATR-CHK2/CHK1-p53-p21 axis and the p53-independent p16/p14 axis, eliciting cyclin-dependent kinase inhibition and permanent cell cycle arrest. Other factors that can be involved are DREAM, MAPK, cGAS/Sting, and NF-κB. The accumulation of non-repaired DNA damage triggering CSEN following external genotoxic exposures may contribute significantly to the amelioration of senescent cells and organ failure with age in humans. Senescent cells drive, via the senescence-associated secretory phenotype (SASP), inflammation that is involved in many diseases, including cancer. Although most of the studies were performed with in vitro cell systems, the consequences of CSEN induction by genotoxic nutritional components and environmental exposures seem to be underestimated. Since CSEN correlates with aging, it is reasonable to conclude that exogenous genotoxic pollutants contribute significantly to the aging process through CSEN induction. In light of these findings, it is deduced that reducing genotoxin exposures and using “rejuvenation” supplements (senotherapeutics) are reasonable strategies to counteract cellular senescence and the aging process. Full article

Fire hazards in closed industrial environments pose a significant threat to workers, infrastructure and production processes. Traditional detection systems, such as smoke and heat detectors, often have limitations in their settings, including delayed response time and a tendency for false alarms due to non-fire factors such as dust, humidity and vapors. This paper researches the applicability of gas sensors as an alternative or complementary method for early fire detection. This research presents an experimental evaluation of six gas sensors integrated with a microcontroller. Tests were conducted in a controlled environment, simulating industrial conditions by monitoring the combustion of different materials, such as wood, plastic and textile. Sensor responses were analyzed at horizontal distances of 2 m and 4 m from the fire source. Results show that all sensors detected combustion byproducts, with those at 2 m exhibiting a faster response and higher concentration readings. The findings confirm that a multi-sensor approach significantly increases detection reliability and enables an earlier response compared to conventional systems. Full article

In fire and emergency rescue operations, visibility is often severely degraded by smoke, airborne debris, or atmospheric pollutants including smog and yellow dust. Several image restoration techniques, including Dark Channel Prior (DCP), Color Attribution Prior (CAP), Peplography, and Adaptive Removal via Mask for Scatter (ARMS), have been proposed to recover clear images under such conditions. However, these methods exhibit significant limitations in heavy scattering environments. This paper proposes a novel visibility restoration method for disaster situations, building upon the state-of-the-art ARMS method. To maximize the suppression of scattering effects, the Scattering Media Model is refined through Gaussian estimation. Additionally, an overlapping matrix is introduced to effectively handle non-uniformly distributed scattering conditions. The proposed method is evaluated using a real rescue operation image dataset provided by the Fire and Disaster Management Agency of Japan. Qualitative visual assessments and quantitative performance metrics demonstrate that the proposed approach significantly outperforms conventional methods under severe scattering conditions. Full article

Wildfire smoke visualization using geostationary satellite imagery is essential for real-time monitoring and atmospheric analysis; however, inconsistencies in color tone across Geostationary Environment Monitoring Spectrometer (GEMS) images hinder reliable interpretation and model training. This study proposes a Standardized False Color Composite (SFCC) framework based on deep learning style transfer to enhance the visual consistency and interpretability of wildfire smoke scenes. Four tone-standardization methods were compared: the statistical Empirical Cumulative Distribution Function (ECDF) correction and three neural approaches—ReHistoGAN, StyTr², and Style Injection Diffusion Model (SI-DM). Each model was evaluated visually and quantitatively using six metrics (SSIM, LPIPS, FID, histogram similarity, ArtFID, and LSCI) and validated on three major wildfire events in Korea (2022–2025). Among the tested models, SI-DM achieved the most balanced performance, preserving structural features while ensuring consistent color-tone alignment (ArtFID = 1.620; LSCI mean = 0.894). Qualitative assessments further confirmed that SI-DM effectively delineated smoke boundaries and maintained natural background tones under complex atmospheric conditions. Additional analysis using GEMS UVAI, VISAI, and CHOCHO demonstrated that the styled composites partially reflect the optical and chemical characteristics distinguishing wildfire smoke from dust aerosols. The proposed SFCC framework establishes a foundation for visually standardized satellite smoke imagery and provides potential for future aerosol-type classification and automated detection applications. Full article