Search Results (4,350)

Reactive oxygen species (ROS) and reactive nitrogen species (RNS) are central regulators of monocyte and macrophage biology, shaping their survival, differentiation, migration, and effector functions. In monocytes and macrophages, ROS and RNS arise from endogenous sources, such as mitochondria, NADPH oxidases, and myeloperoxidase, and from exogenous stimuli including pathogens, damaged tissues, and environmental oxidants. These reactive intermediates converge on redox-sensitive pathways such as NF-κB, Nrf2/HO-1, mitochondrial ROS signalling, and the NLRP3 inflammasome, thereby integrating metabolic stress with inflammatory activation. Redox balance is a key determinant of macrophage polarization: heightened ROS and RNS production drives pro-inflammatory M1 programs, whereas tightly regulated oxidative signalling supports M2 phenotypes associated with tissue repair and resolution. In chronic inflammatory disorders, notably atherosclerosis, oxidative stress amplifies monocyte recruitment, foam-cell formation, plaque instability, and maladaptive immunometabolic responses. The aim of this review is to recapitulate the major sources and functions of ROS and RNS in monocytes and macrophages and to synthesize current evidence on how these pathways collectively maintain or disrupt immune homeostasis. We further highlight emerging therapeutic strategies, such as NOX inhibitors, mitochondrial-targeted antioxidants, and Nrf2 activators, that seek to restore redox balance and offer promising avenues for the treatment of cardiovascular and immune-mediated diseases. Full article

The commercialization of V-based catalysts for the selective catalytic reduction of NOx by NH₃ (NH₃-SCR) is hindered by their narrow operating temperature window, insufficient low-temperature (LT) activity, and severe SO₂-to-SO₃ oxidation. To bridge this gap, we herein introduced Nb and hexagonal BN into a VW/TiO₂ system to simultaneously enhance its LT SCR activity, suppress undesired side reactions, and improve durability. Nb incorporation promoted V⁵⁺/V⁴⁺ redox cycling and enhanced lattice oxygen mobility, thus reducing the apparent activation energy and suppressing SO₂ oxidation at elevated temperatures. However, excessive Nb loading induced NH₃ oxidation and N₂O formation. This drawback was mitigated by introducing BN as a dispersion promoter, which helped secure high catalytic performance at a reduced Nb content. The VWNb/Ti-BN catalyst achieved superior NOx conversion and N₂ selectivity over a wide temperature range and benefited from notably suppressed NH₃ oxidation and SO₂-to-SO₃ oxidation. Kinetic analysis revealed that Nb primarily lowered the reaction energy barrier via redox property enhancement, whereas BN accelerated surface reaction turnover by stabilizing and dispersing active acidic sites, markedly increasing the turnover frequency without reducing the activation energy. In situ spectroscopic analysis confirmed the accelerated consumption of adsorbed NH₃ species and enhanced formation of reactive NOx intermediates, indicating SCR pathway enhancement. After aging in the presence of SO₂ and H₂O, the best-performing honeycomb-type monolithic catalyst retained and NOx conversion of >80%, demonstrating excellent long-term durability under practical conditions. A composition-aware machine learning model based on log-ratio-transformed variables quantitatively identified the synergistic balance among V, Nb, W, BN, and TiO₂ as the dominant factor governing LT SCR performance. Thus, this work provides valuable mechanistic insights and a strategy for designing wide-temperature-window SCR catalysts with improved activity, selectivity, and resistance to sulfur poisoning. Full article

Ammonium bisulfate (ABS) fouling in air preheaters has become a critical challenge restricting the safe and efficient operation of coal-fired units. Optimizing the flow field of the outlet of the upstream SCR system is a potentially effective path to mitigate ABS fouling. In this work, CFD simulations were conducted on the SCR De-NO_x system and its succeeding flue ducts connected to the air preheater. The simulation results of the original design show that a significant velocity deviation exists at the inlet of the air preheater (with the CV₁ up to 53.2%), with a portion of the flue gas adhering to the walls, which could induce ABS fouling in the low-temperature region. By adding flow guide plates into the flue duct, the flow uniformity before the air preheater was expected to be effectively improved. Notably, considering the deposition characteristics of ABS and the operating characteristics of the rotary air preheater, this study proposed a novel evaluation indicator, radial variance coefficient (CV₂), which focuses on the velocity uniformity based on the annular sector unit, to indicate the risk of ABS deposition. The influence on velocity uniformity of different flow guide plate layouts was analyzed. Based on the multiple evaluation metrics including pressure drop and flow uniformity, the optimal layout scheme was then selected. After optimization, the radial variance coefficient decreased from 30.7% to 11.7%, with the pressure drop slightly increased from 50 Pa to 80 Pa. This study could help to reduce unit failure frequency and support efficient operation of coal-fired power plants. Full article

This paper presents the design of a sensor system for remote measurements of exhaust emissions from automotive combustion engines. The system’s purpose is to determine the likelihood of a given vehicle’s potential harmfulness to the environment. This system, if implemented, could detect vehicles posing a threat to the environment in road traffic. A remote measurement system can be installed in the front of a measuring vehicle driving behind the vehicle being diagnosed. This approach allows for rapid road testing of multiple vehicles while they are operating in real-world conditions where engines can emit the highest levels of undesirable pollutants. Exceeding emission standards may be related to modifications made to the vehicle’s exhaust gas aftertreatment systems, engine wear, or malfunctions of engine-related systems such as the diesel particulate filter (DPF) or catalytic converter. Toxic and undesirable substances include carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NO_x), carbon dioxide (${\mathrm{C}\mathrm{O}}_2$), and particulate matter (PM) particles. The main goal of the measurements is to identify vehicles that potentially pose a threat to the environment during normal operation. The sensor system consists of several types of sensors utilizing various physical and chemical phenomena, with particular emphasis on their low cost and easy availability. The measurement unit utilizes MEMS technology, photoacoustic spectroscopy, electrochemical methods, light absorption and scattering, spectrophotometry, and electro-optical detection. Full article

China’s stringent clean air policies have substantially reduced nitrogen oxides (NO_x) emissions, leading to a general decline in nitrogen dioxide (NO₂). However, surface ozone (O₃) pollution remains severe, creating a complex challenge due to the non-linear relationship between O₃ and its precursors. To disentangle the drivers behind these trends, this study quantifies the impacts of interannual variations in top-down constrained NO_x emissions on surface NO₂ and O₃ concentrations from 2014 to 2021 across mainland China and five national urban agglomerations. We employed the WRF-CMAQ model with a fixed-emission simulation approach, using an observationally optimized NO_x emission inventory derived from the assimilation of surface NO₂ measurements. Results reveal that NO₂ reductions were predominantly emission-driven (>80% post-2017), with declines most pronounced in winter. A strong linear consistency was found between interannual changes in top-down NO_x emissions and attributed NO₂ concentration variations, validating the methodology. In contrast, O₃ responses to NO_x reductions were spatially and seasonally heterogeneous, reflecting a non-linear photochemical regime. In major urban agglomerations (e.g., Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD)), NO_x reductions post-2018 showed limited effectiveness in mitigating summertime O₃ and even increased O₃ in spring and autumn, indicating a prevalent VOC-sensitive regime where NO_x reduction can disinhibit O₃ formation. Conversely, certain provinces (e.g., Anhui, Shanxi, Jilin) exhibited O₃ decreases, suggesting a NO_x-sensitive regime. The area benefiting from NO_x reductions expanded steadily in summer after 2017 but not in other seasons. This study confirms the efficacy of NO_x-focused policies for reducing primary NO₂ pollution but highlights that mitigating persistent O₃ requires a strategic shift to synergistic, region-specific control of volatile organic compounds alongside NO_x, informed by local chemical sensitivity. Full article

Whole-grain rice (Oryza sativa L.) is a rich source of polyphenols. The in vivo mechanisms linking its phytochemical profile to antioxidant and anti-inflammatory effects remain incompletely defined. This study investigated the antioxidant and anti-inflammatory activity of a whole-grain rice flour 70% ethanol extract (OSEE) and correlated these effects with its phenolic composition. OSEE showed high total phenolic content 0.121 ± 0.002 mg GAE/g d.w.), a lower total flavonoid content (61.83 ± 4.03 µg QE/g d.w.), and a phenolic profile dominated by phenolic acids (~87%), with ferulic and protocatechuic acids among the most abundant constituents. OSEE displayed significant in vitro antioxidant activity in DPPH, FRAP, hydrogen peroxide, and nitric oxide scavenging assays. In vivo activity was evaluated in male Wistar rats with turpentine oil-induced acute inflammation using both therapeutic (post-induction) and prophylactic (pre-induction) protocols, testing three oral doses of lyophilized extract (1.0, 0.50, and 0.25 g/kg/day). In vivo, OSEE attenuated systemic oxidative stress (TOS, TAC, OSI, AOPP, MDA, NOx, 3-nitrotyrosine, total thiols) and the expression of pro-inflammatory markers (NF-κB p65, IL-1β, IL-18, caspase-1) in a dose-dependent manner with both protocols, with the highest dose producing the most consistent reductions, while the expression level of the anti-inflammatory factor IL-10 remained unchanged. PCA supported a shift in biomarker networks toward a non-inflamed profile. These findings indicate that OSEE exerts coordinated antioxidant and anti-inflammatory effects in vivo that are strongly associated with its phenolic composition. Full article

Metal-free electrodes are essential to promote electrochemical reactions, the core of sustainable energy resources. In search of better carbon-based electrode materials, we have explored several spatial arrangements of boron (B) within proximity in the graphene lattice, as evident in recent experimental observations. Multi-boron substitution enriches sites by tuning electronic structure and strengthens binding of key intermediates of oxygen reduction, oxygen evolution, and hydrogen evolution reactions facilitating electrocatalytic performance. Our optimal B-doped site shows near thermo-neutral H adsorption ($\Delta G_{H^{*}}$∼$\pm 0.4\mathrm{eV}$), consistent with experiments. The overpotentials are highly sensitive to the dopant motifs and the spread among configurations shows that experimentally accessible multi-B doping can serve as a practical active site engineering knob to achieve optimized multi-functional performance. In parallel, we find that specific multi-B configurations selectively capture and pre-activate NO_x (NO/NO₂) under ambient conditions while retaining weak affinity for NH₃. These sites also interact with SO₂ and related hazardous species, enabling selective air filtration and targeted NO_x control within the electrocatalytic scope of this study. Full article

Background: Tyrosine kinase inhibitors (TKIs) have transformed cancer therapy; however, they are associated with cardiovascular toxicity. Metabolomics provides a comprehensive framework for identifying early biochemical disruptions that precede clinical manifestations and for formulating mechanism-based intervention strategies. Methods: We conducted a narrative synthesis of published preclinical and translational studies on TKI cardiotoxicity, focusing on untargeted and targeted metabolomic findings and complementary proteomic and transcriptomic data. Functional validation was performed using rodent and cellular models. Mechanistic themes were identified, and implications for biomarker panels, multi-omic integration, and metabolomics-guided interventions were proposed. Conclusions: Metabolomic analyses of various TKIs identified convergent signatures along three interconnected axes: (1) mitochondrial bioenergetic dysfunction characterized by impaired long-chain fatty acid oxidation and adenylate depletion; (2) disruption of endothelial nitric oxide signaling with redox imbalance, including increased nitrotyrosine, Nox activation, and eNOS uncoupling; and (3) an inflammatory metabolic profile marked by elevated branched-chain and aromatic amino acids, creatine, and osmolytes. Rodent models of sunitinib and sorafenib replicate these signatures and demonstrate histological injury, contractile dysfunction, and fibrosis. Preclinical intervention data, particularly restoration of myocardial carnitine, AMPK signaling, and fatty acid oxidation by L-carnitine, provide proof of concept for metabolomics-guided cardioprotection. Metabolomics can identify mechanistic biomarkers that facilitate the early detection, risk stratification, and targeted prevention of TKI-induced cardiovascular injury. Translation into precision cardio-oncology requires prospective validation, standardized assays, and biomarker-driven interventional trials. Full article

To address the complex calibration parameters and low optimization efficiency of dual-fuel engines, this paper innovatively proposes an optimization calibration method based on a simulation model and the Non-Dominated Sorting Whale Optimization Algorithm (NSWOA). Taking the YC6K dual-fuel engine as the research object, a high-precision simulation model was constructed within the GT-Power environment, and its reliability was confirmed through the external characteristic curve (the maximum deviation of torque and specific fuel consumption rate is less than 5%). A total of 260 parameter samples were generated using a Sobol sequence space-filling experimental design, and a performance prediction model was established by combining the Crested Porcupine Optimization algorithm and the Back-Propagation Neural Network (CPO-BP). The experimental results show that the CPO-BP model exhibits excellent predictive capability, with the coefficient of determination (R²) of nitrogen oxides (NO_x) and brake-specific fuel consumption rate (BSFC) reaching 0.98964 and 0.99501 respectively. Based on this, the NSWOA algorithm was introduced to optimize key parameters such as speed, torque, main injection timing, and rail pressure, with the optimization objectives being NO_x emissions and BSFC. The optimization results show that under 100% load conditions, the reduction in BSFC ranges from 1.5% to 4.3%, and NO_x emissions are reduced by 48.6% to 67.1%. The effectiveness of the optimized parameters was also verified through bench tests, providing an efficient solution for complex engineering optimization problems. Full article

This work investigates the influence of hybrid NiO–SiO₂ nanoparticles on the engine behavior of biodiesel derived from waste sunflower oil and evaluates the experimental outcomes using a data-driven modeling approach. Biodiesel was produced via transesterification and doped with nanoparticles at concentrations of 50, 75, and 100 ppm. Performance and emission tests were conducted on a single-cylinder diesel engine operating at constant speed under varying loads. Specific fuel consumption, brake thermal efficiency, CO, HC, NO_x, smoke opacity, and exhaust gas temperature were recorded and analyzed. The incorporation of nanoparticles improved combustion quality and contributed to substantial reductions in harmful emissions. The WSOB20 blend containing 100 ppm NiO–SiO₂ provided the most balanced results, decreasing CO, HC, and smoke emissions by 39.50%, 39.40%, and 35.20%, respectively, relative to diesel fuel, while preserving competitive thermal efficiency. A linear regression model developed for CO prediction produced a low mean squared error (1.08 × 10⁻⁵), indicating strong predictive capability. The findings confirm that hybrid nanoparticle additives can enhance biodiesel performance while supporting accurate emission forecasting. Full article

Heavy-duty vehicles (HDVs) are major greenhouse gas emitters, and liquefied natural gas (LNG)-powered HDVs have emerged as a promising low-carbon alternative. However, their real-world emission performance and mitigation potential remain insufficiently studied, necessitating the characterization of LNG container trucks’ on-road CO₂ emissions via advanced sensing technologies. To characterize HDVs’ emission characteristics, real-driving emissions from China VI LNG and diesel-powered container trucks were measured employing portable emissions measurement systems (PEMS). The results reveal that high CO₂ emissions predominantly occur during low- to medium-speed acceleration and at speeds above 40 km/h with an acceleration exceeding 0.3 m/s² on highways, whereas emissions on port roads are more dispersed. A third-degree polynomial function fits emissions well with vehicle-specific power (VSP). Engine parameters mainly influence CO₂ emissions for LNG trucks, while VSP and acceleration significantly impact diesel trucks. The Random Forest model achieves superior prediction accuracy, particularly in highway scenarios, and significantly better CO₂ forecasting for LNG-powered trucks. These findings validate the effectiveness of PEMS-based sensing in characterizing low-carbon HDVs’ real-world emissions. The integration of multi-source sensor data and machine learning also provides a reference for intelligent sensing in transportation environmental monitoring. Full article

Inflammatory bowel disease (IBD) is triggered by genetic predisposition and chronic inflammation, with aberrant activation of the innate immune complex NLRP3 inflammasome playing a pivotal role in its pathogenesis. In this study, we investigated the effects of a hot water extract from the brown alga Endarachne binghamiae (EB-WE) on the inhibition of NLRP3 inflammasome activation, with a focus on its antioxidant properties, in various inflammation models. In bone marrow-derived macrophages (BMDMs), NLRP3 inflammasome activation was induced using LPS and ATP, and EB-WE pretreatment (100, 200 µg/mL) significantly reduced the secretion of IL-1β and IL-18. Confocal immunofluorescence analysis further confirmed that EB-WE suppressed the formation of the NLRP3-ASC/caspase-1 complex. Furthermore, the in vivo anti-IBD efficacy of EB-WE was assessed using a DSS-induced mouse model, in which colonic inflammation and NLRP3-mediated responses were prominent. Oral administration of EB-WE (2 or 5 mg/day) markedly ameliorated clinical symptoms, such as weight loss, diarrhea, and rectal bleeding, and significantly reduced the disease activity index (DAI). EB-WE also decreased serum pro-inflammatory cytokine levels and the expression of NLRP3 inflammasome-related molecules in colon tissue at both the gene and protein levels. In both BMDMs and the IBD mouse model, we further analyzed the upstream regulatory pathway involving NOX2-iNOS. EB-WE efficiently inhibited the activation of the NOX-iNOS axis and NF-κB phosphorylation, thereby alleviating inflammasome activation associated with DSS-induced oxidative stress and neutrophil/macrophage infiltration. Collectively, these results demonstrate that EB-WE effectively suppresses the formation and activation of the NLRP3 inflammasome by modulating the NOX-iNOS axis and the NF-κB pathway via antioxidant mechanisms. These findings suggest that EB-WE holds promise as a novel marine-derived natural therapeutic agent for the treatment of chronic inflammatory diseases. Full article

This study provides a comprehensive assessment of the impacts of COVID-19 pandemic-induced lockdowns on urban air quality at Marienplatz in Stuttgart, Germany, from 2018 to 2022. Utilizing high-resolution temporal datasets and advanced analytical techniques, including meteorological normalization and Shapley Additive Explanations (SHAP), the research disentangles the effects of emission reductions from meteorological variability on key atmospheric pollutants (CO, NO, NO₂, O₃, PM2.5, PM10). The findings reveal that the lockdown phases resulted in pronounced and significant reductions in primary traffic-related pollutants, with CO and NO concentrations declining by more than 50% relative to pre-pandemic baselines. In contrast, secondary pollutants, notably ozone, exhibited substantial increases (up to 50%), attributable to altered photochemical regimes and reduced NO_x titration, as confirmed by O_x-NO_x relationship analyses and photochemical sensitivity diagnostics. Particulate matter trends revealed limited short-term response, indicating persistent contributions from non-traffic sources such as residential heating and regional transport. Meteorologically normalized trends and SHAP analyses further confirmed that emission reductions, rather than meteorological fluctuations, were the primary drivers of the observed improvements in air quality. These insights highlight the transient and pollutant-specific nature of air-quality responses to abrupt emission reductions and provide critical scientific evidence to inform the design of robust, multi-sectoral urban air quality management and climate adaptation strategies in the post-pandemic era. Full article

Decarbonizing the maritime sector requires not only adopting alternative fuels and propulsion technologies but also quantitatively assessing their impacts on coastal and urban air quality. This study develops a stochastic, time-resolved air-quality modelling framework to evaluate ship-related pollutant dispersion in port environments. The approach integrates Automatic Identification System (AIS) trajectories, vessel-specific emission factors, and meteorological inputs within a moving-source Gaussian dispersion model to simulate the spatio-temporal evolution of pollutant concentrations. A 24 h case study for the Ports of Los Angeles and Long Beach demonstrates highly intermittent emission behaviour, with peak aggregated emission rates reaching approximately 1.2 kg/s for CO₂ and 3.8 g/s for SO₂. Temporally integrated concentration fields reveal maximum cumulative dosages of 0.145 g·s/m³ for NO_x, 0.023 g·s/m³ for SO₂, 0.014 g·s/m³ for total PM, and 7.5 g·s/m³ for CO₂ in near-port traffic corridors. Sensitivity analysis indicates that effective emission height variations alter cumulative exposure by up to 17%, whereas temporal resolution changes produce deviations below 7%, confirming numerical stability. Monte Carlo uncertainty propagation demonstrates bounded but non-negligible variability in exposure estimates under realistic emission and wind uncertainties. Results show that cumulative exposure patterns differ substantially from short-term concentration peaks, highlighting the importance of time-integrated and receptor-based metrics for port air quality assessment. The proposed AIS-driven stochastic framework provides a reproducible and computationally efficient tool for evaluating operational mitigation strategies and supporting evidence-based maritime decarbonization pathways. Full article

Human skin and hair act as multifunctional barriers but are highly sensitive to environmental pollutants originating from air, water, and cosmetic products. Epidemiological studies report that exposure to particulate matter (PM_2.5–PM₁₀), nitrogen oxides (NO_x), and volatile organic compounds increases the risk of skin and hair disorders. For instance, women in high-traffic areas (N = 211) show significantly more pigment spots and nasolabial wrinkles compared to those in rural areas (N = 189), indicating accelerated skin ageing. Children aged 9–11 exposed to PM₁₀, benzene, and NO_x exhibit increased incidence of atopic dermatitis. Systemic exposure to dioxins causes chloracne, while co-exposure to polycyclic aromatic hydrocarbons (PAHs) and UVA radiation elevates skin cancer risk. Psoriasis flares are associated with mean pollutant concentrations over the 60 days preceding flare events in 957 patients, and hyperpigmentation prevalence increases in populations exposed to traffic-related PM and ROS-inducing pollutants. Hair loss is linked to oxidative stress from PM and PAHs absorbed on hair fibers, with in vitro studies showing keratinocyte apoptosis in scalp hair follicles. This review evaluates natural adsorbents such as zeolites, clays, activated carbon, and polyphenol-rich plant extracts for anti-pollution cosmetic formulations. Adsorption capacities range from 60 to 150 mg·g⁻¹ depending on the pollutant, with removal efficiencies of 30–55% in model topical systems. Mechanisms include ion exchange, surface adsorption, hydrophobic interactions, and radical scavenging. Incorporating 2–5% w/w of these adsorbents in cosmetic formulations significantly reduces pollutant deposition on skin and hair. These findings support the development of evidence-based, sustainable anti-pollution cosmetic strategies that quantitatively mitigate environmental stressor effects. Full article