Search Results (445)

Three TOP TIER^TM gasoline deposit control additives (DCAs) of differing chemistries were tested for their impact on particulate matter emissions in terms of particulate mass (PM) and particle number (PN) at operating conditions representative of road load, cold start, and high load on a 2.0 L, 4-cylinder, gasoline direct injection (GDI) spark ignition (SI) engine. The PM-PN emissions were measured using an Exhaust Emissions Particle Sizer (EEPS). Deposit control additives or detergents are gasoline additives used to prevent and clean combustion chamber and injector deposits in gasoline spark ignition (SI) engines. All three gasoline additives were tested at each operating condition at three different treatment rates. In addition, one of the additives was tested with a fuel-based friction modifier (FM). The results showed that of the treatment rates tested, the lowest allowable concentration (LAC) for all additives requires the least time for the emissions to settle. However, the impact of the gasoline additives on PM-PN emissions is not linear and changes with additive concentration depending on the additive chemistry and operating conditions. The additive with the friction modifier resulted in an increase of over 19% particle number and over 30% particulate mass at the road load operating condition, while the increase at high load was over 27% for particle number and 11% for particle mass. Full article

The densely populated city of Athens has been facing air pollution problems over the past few decades due to the high population density associated with an intense emission load constrained by the local topography causing poor ventilation. In this study, the evolution of the interannual and seasonal variation in primary and secondary gaseous as well as particulate urban air pollution in Athens was examined for the 2001–2023 period and for the following pollution parameters: SO₂, CO, NO₂, NO_x (NO + NO₂), O₃, O_x (O₃ + NO₂), PM₁₀, and PM_2.5. For this purpose, the annual and monthly averages from the Athens air pollution monitoring stations of Peireas (SO₂, CO, NO₂, NO_x), Patission (SO₂, CO, NO₂, NO_x), Aristotelous (PM₁₀, PM_2.5), Lykovrissi (PM₁₀, PM_2.5, O₃, O_x), and Liossia (O₃, O_x) in the selected periods of 2001–2004 and 2020–2023 were examined. There was a clear reduction in most air pollution parameters at all stations during the period examined, relative to the average values. The ozone and O_x values, presenting a high interannual variability, remained generally unchanged. The smallest reductions are observed for NO₂ and NO_X (about −10% to −20%), while the highest reductions are observed for SO₂, CO, and PM₁₀ (about −50% to −60%). The change in pollutant concentrations for every month of the year between the 2001–2004 and 2020–2023 time periods is also examined, and the observed seasonal differences are discussed. Full article

Hangzhou Bay (HZB) has become a hot spot in hydro-morphodynamic research due to human impacts and natural influences, as well as the substantial quantities of water discharge and sediment load of the Yangtze River and Qiantang River. Although many previous studies have analyzed the spatial–temporal variations in suspended particulate matter (TSM) from in situ and satellite observations, the long-term changes in suspended sediment dynamics remain unclear. In this study, we quantified the long-term variation in TSM load using MODIS/Aqua data during 2003–2024. The TSM products in the HZB displayed a decreasing trend from 2003 to 2024 (k = −1.90 mg/L/year, p < 0.05), which may be attributed to decreased sediment discharge from the Yangtze River. The spatial variation in TSM provided quantitative results for HZB, with a substantially increasing trend in the southern shallow areas and a decreasing trend in the northern deep troughs and central bay. The interannual variations in TSM in winter displayed a positive correlation with the sediment load from the Yangtze River (R = 0.640 for the data during 2014–2022) and with wind speed (R = 0.676 for the data during 2009–2021). The TSM of HZB was partly affected by the combined impacts of human activities and climate change. A distinct difference in TSM concentrations on both sides of the Hangzhou Bay Bridge was observed, with higher TSM on the western side than on the eastern side for most of the year during 2003–2024. A decline in TSM was observed near Yushan Island from 2003 to 2024, attributed to large-scale land reclamation and associated alterations in tide-dominated areas. This study provides valuable insights into the long-term changes in suspended sediment and water quality in HZB, which is crucial for managing water resources, creating effective water strategies, predicting future needs, and ensuring sustainable water management. Full article

Airborne and surface-residing microorganisms in indoor environments pose potential risks for infectious disease transmission. To address this issue, we developed a composite device combining a generator of vaporized free chlorine components with a fine particle removal filter. Field tests were conducted in occupied university classrooms to evaluate the device’s efficacy in reducing airborne bacterial loads. Airborne bacteria were sampled under three operational conditions [Electrolyzed (+)/Filter (+), Electrolyzed (−)/Filter (+), and Electrolyzed (−)/Filter (−)]. Significant reductions in bacterial counts were observed in the Electrolyzed (+)/Filter (+) condition, with a residual rate of 14.5% after 2.25 h (p = 0.00001). Additionally, surface contact tests demonstrated that vaporized free chlorine components, primarily consisting of hypochlorous acid (HOCl), reduced viable counts of E. coli, P. aeruginosa, and S. aureus by 59.0–99.7% even at a distance of 8.0 m. The concentrations of vaporized free chlorine components during operation remained within safe exposure limits (0–19 ppb), consistent with the effective range reported in prior literature (10–50 ppb). Computational fluid dynamics simulations confirmed the diffusion of vaporized free chlorine components throughout the room, including distant sampling points. These findings suggest the combined use of a vaporized free chlorine generator and a particulate filter effectively reduces microbial contamination in indoor environments, providing a promising approach for infection control in residential and public settings. Full article

This study investigates the tribological behavior and wear mechanisms of Q235 steel components subjected to abrasive interaction with rice, a critical challenge in agricultural machinery performance and longevity. We employed a comprehensive multi-scale framework, integrating bench-top tribological testing, advanced Discrete Element Method (DEM) coupled with a wear model (DEM-Wear), and detailed surface characterization. Bench tests revealed a composite wear mechanism for the rice–steel tribo-pair, transitioning from mechanical polishing under mild conditions to significant soft abrasive micro-cutting driven by the silica particles inherent in rice during high-load, high-velocity interactions. This elucidated fundamental friction and wear phenomena at the micro-level. A novel, calibrated DEM-Wear model was developed and validated, accurately predicting macroscopic wear “hot spots” on full-scale combine harvester header platforms with excellent geometric similarity to real-world wear profiles. This provides a robust predictive tool for component lifespan and performance optimization. Furthermore, fractal analysis was successfully applied to quantitatively characterize worn surfaces, establishing fractal dimension (Ds) as a sensitive metric for wear severity, increasing from ~2.17 on unworn surfaces to ~2.3156 in severely worn regions, directly correlating with the dominant wear mechanisms. This study offers a valuable computational approach for understanding and mitigating wear in tribosystems involving complex particulate matter, contributing to improved machinery reliability and reduced operational costs. Full article

Aluminium metal matrix composites (AMMCs) incorporate aluminium alloys reinforced with fibres (continuous/discontinuous), whiskers, or particulate. These materials were engineered as advanced solutions for demanding sectors including construction, aerospace, automotive, and marine. Micro- and nano-scale reinforcing particles typically enable attainment of exceptional combined properties, including reduced density with ultra-high strength, enhanced fatigue strength, superior creep resistance, high specific strength, and specific stiffness. Microstructural, mechanical, and tribological characterizations were performed, evaluating input parameters like reinforcement weight percentage, applied normal load, sliding speed, and sliding distance. Fabricated nanocomposites underwent tribometer testing to quantify abrasive and erosive wear behaviour. Multiple investigations employed the Taguchi technique with regression modelling. Analysis of variance (ANOVA) assessed the influence of varied test constraints. Applied load constituted the most significant factor affecting the physical/statistical attributes of nanocomposites. Sliding velocity critically governed the coefficient of friction (COF), becoming highly significant for minimizing COF and wear loss. In this review, the reinforcement homogeneity, fractural behaviour, and worn surface morphology of AMMCswere examined. Full article

Improving the thermal efficiency of internal combustion engines plays a crucial role in reducing fuel consumption and engine emissions. Studies have shown that the friction loss caused by the piston ring–cylinder liner pair accounts for approximately 30–40% of the engine’s total mechanical friction. The key to improving mechanical and thermal efficiency lies in reducing frictional losses through advanced solutions. However, as engine intensification increases, the growing thermal and mechanical loads lead to out-of-round deformation of the cylinder liner. This deformation reduces the sealing conformity of the piston rings, leading to increased blow-by and elevated particulate matter (PM) emissions. To address this, a dynamic–static deformation testing system for cylinder liners, combined with a multi-physics simulation for data validation, has been developed to achieve energy conservation and emission reduction in engines. Based on established strain gauge and eddy current displacement sensors, this study developed a dynamic deformation testing system, modified for a specific type of diesel engine, and analyzed the cylinder liner deformation under fired conditions. Test results show that under engine speeds ranging from 700 rpm to 1100 rpm, the overall radial out-of-roundness of the cylinder liner increased, with a maximum deformation of 49.2 μm. The second-order component of out-of-roundness also increases with speed, showing a maximum rise of 28.9 μm, while the third-order and fourth-order components exhibit relatively minor changes. These findings suggest that the overall radial deformation under fired conditions is mainly dominated by second-order out-of-roundness, with third-order and fourth-order components contributing marginally. Full article

Accurate forecasting of energy consumption in buildings is essential for achieving energy efficiency and reducing carbon emissions. However, many existing models rely on limited input variables and overlook the complex influence of indoor environmental quality (IEQ). In this study, we assess the performance of hybrid machine learning ensembles for predicting hourly energy demand in a smart office environment using high-frequency IEQ sensor data. Environmental variables including carbon dioxide concentration (CO₂), particulate matter (PM_2.5), total volatile organic compounds (TVOCs), noise levels, humidity, and temperature were recorded over a four-month period. We evaluated two ensemble configurations combining support vector regression (SVR) with either Random Forest or LightGBM as base learners and Ridge regression as a meta-learner, alongside single-model baselines such as SVR and artificial neural networks (ANN). The SVR combined with Random Forest and Ridge regression demonstrated the highest predictive performance, achieving a mean absolute error (MAE) of 1.20, a mean absolute percentage error (MAPE) of 8.92%, and a coefficient of determination (R²) of 0.82. Feature importance analysis using SHAP values, together with non-parametric statistical testing, identified TVOCs, humidity, and PM_2.5 as the most influential predictors of energy use. These findings highlight the value of integrating high-resolution IEQ data into predictive frameworks and demonstrate that such data can significantly improve forecasting accuracy. This effect is attributed to the direct link between these IEQ variables and the activation of energy-intensive systems; fluctuations in humidity drive HVAC energy use for dehumidification, while elevated pollutant levels (TVOCs, PM_2.5) trigger increased ventilation to maintain indoor air quality, thus raising the total energy load. Full article

Boda boda motorbike taxis are a primary mode of transport in Nairobi, Kenya, and a major source of urban air pollution. This study investigates the environmental and electrical grid impacts of electrifying Nairobi’s boda boda fleet. Using real-world tracking data from 118 motorbikes, we simulated the effects of a full-scale transition from internal combustion engine (ICE) vehicles to electric motorbikes. We analysed various scenarios, including different battery charging strategies (swapping and home charging), motor efficiencies, battery capacities, charging rates, and the potential for solar power offsetting. The results indicate that electrification could reduce daily ${\mathrm{CO}}_2$ emissions by approximately 85% and eliminate tailpipe particulate matter emissions. However, transitioning the entire country’s fleet would increase the national daily energy demand by up to 6.85 GWh and could introduce peak grid loads as high as 2.40 GW, depending on the charging approach and vehicle efficiency. Battery swapping was found to distribute the grid load more evenly and better complement solar power integration compared to home charging, which concentrates demand in the evening. This research provides a scalable, data-driven framework for policymakers to assess the impacts of transport electrification in similar urban contexts, highlighting the critical trade-offs between environmental benefits and grid infrastructure requirements. Full article

Liquefied natural gas (LNG) is increasingly used as a marine fuel due to its capacity to significantly reduce emissions of particulate matter, sulfur oxides (SO_x), and nitrogen oxides (NO_x), compared to conventional fuels. In addition, LNG combustion produces less carbon dioxide (CO₂) than conventional marine fuels, and the use of non-fossil LNG offers further potential for reducing greenhouse gas emissions. However, this benefit can be partially offset by methane slip—the release of unburned methane in engine exhaust—which has a much higher global warming potential than CO₂. This study presents an experimental evaluation of methane emissions from a RoPax vessel powered by low-pressure dual-fuel four-stroke engines with a direct mechanical propulsion system. Methane slip was measured directly during onboard testing and combined with a year-long analysis of engine operation using an Engine Load Monitoring (ELM) method. The yearly average methane slip coefficient (C_slip) obtained was 1.57%, slightly lower than values reported in previous studies on cruise ships (1.7%), and significantly lower than the default values specified by the FuelEU (3.1%) Maritime regulation and IMO (3.5%) LCA guidelines. This result reflects the ship’s operational profile, characterized by long crossings at high and stable engine loads. This study provides results that could support more representative emission assessments and can contribute to ongoing regulatory discussions. Full article

To improve the prediction accuracy of soot load in gasoline particulate filters (GPFs) and the control accuracy during GPF regeneration, this study developed a prediction model to predict the soot mass concentration at the GPF inlet of gasoline direct injection (GDI) engines using advanced machine learning methods. Three machine learning approaches, namely, support vector regression (SVR), deep neural network (DNN), and a Stacking integration model of SVR and DNN, were employed, respectively, to predict the soot mass concentration at the GPF inlet. The input data includes engine speed, torque, ignition timing, throttle valve opening angle, fuel injection pressure, and pulse width. Exhaust gas soot mass concentration at the three-way catalyst (TWC) outlet is obtained by an engine bench test. The results show that the correlation coefficients (R²) of SVR, DNN, and Stacking integration model of SVR and DNN are 0.937, 0.984, and 0.992, respectively, and the prediction ranges of soot mass concentration are 0–0.038 mg/s, 0–0.030 mg/s, and 0–0.07 mg/s, respectively. The distribution, median, and data density of prediction results obtained by the three machine learning approaches fit well with the test results. However, the prediction result of the SVR model is poor when the soot mass concentration exceeds 0.038 mg/s. The median of the prediction result obtained by the DNN model is closer to the test result, specifically for data points in the 25–75% range. However, there are a few negative prediction results in the test dataset due to overfitting. Integrating SVR and DNN models through stacked models extends the predictive range of a single SVR or DNN model while mitigating the overfitting of DNN models. The results of the study can serve as a reference for the development of accurate prediction algorithms to estimate soot loads in GPFs, which in turn can provide some basis for the control of the particulate mass and particle number (PN) emitted from GDI engines. Full article

Air pollution caused by pesticide residues is an emerging concern in urban environments influenced by nearby agricultural activities. In this study, weekly air samples were collected between May 2018 and March 2020 in Strasbourg, France, to quantify 104 pesticides in both gas and particle phases using GC-MS/MS and LC-MS/MS. Herbicides and fungicides were the most frequently detected classes, appearing in 98% of both phases followed by insecticides. Key compounds such as metalaxyl-M, diphenylamine, and bifenthrin were present in over 90% of samples. Concentrations ranged from 2.5 to 63 ng m⁻³ weekly, with cumulative annual loads exceeding 1200 ng m⁻³. Gas–particle partitioning revealed that highly volatile compounds like azinphos-ethyl favored the gas phase, while less volatile ones like bifenthrin and tebuconazole partitioned >95% into particles. A third-degree polynomial regression (R² of 0.74) revealed a nonlinear relationship between Kₚ and particle-phase concentrations, highlighting a threshold above Kₚ of 0.025 beyond which compounds accumulate disproportionately in the particulate phase. Seasonal variability showed that 36% of the annual pesticide load occurred in autumn, with total airborne levels peaking near 400 ng m⁻³, while the lowest load occurred during summer. Principal component analysis identified rainfall and total suspended particles as major drivers of pesticide phase distribution. The inhalation health risk assessed yielded hazard index values < 1 × 10⁻⁷ for all population groups, suggesting negligible non-cancer risk. This study highlights the prevalence, seasonal dynamics, and partition behavior of airborne pesticides in urban air and underscores the need for regulatory attention to this overlooked exposure route. Full article

Based on engine bench testing, this study investigated the effect of diesel oxidation catalytic converter (DOC) formulations on the gaseous emissions performance of diesel engines equipped with a DOC+ catalyzed diesel particulate filter (CDPF)+selective catalytic reduction (SCR) system after the treatment system. The experimental results indicate that changes in DOC formulations have no significant effect on engine fuel economy. As the precious metal loading increases and the Pt/Pd ratio decreases, the T₅₀ for CO and HC decreases, and the low-temperature conversion rates (<300 °C) for CO and HC increase. However, as the temperature continues to rise, the beneficial effect of increased precious metal loading or Pd on CO and HC conversion rates gradually weakens. The average conversion rates in the high-temperature range (≥300 °C) show little difference. The NO conversion rate increases with increasing precious metal loading. The NO conversion rate is more sensitive to Pt content, with higher Pt content formulations promoting NO oxidation, contrary to the trends observed for CO and HC conversion rates. When the SCR inlet temperature is low, high NO₂ concentrations are beneficial for improving the SCR’s NOx conversion efficiency. When the SCR inlet temperature is high, the SCR’s NOx conversion efficiency exceeds 90% with no significant differences. No significant impact of DOC formulation changes on CDPF pressure drop under external conditions was observed. Full article

The event scale method, employed for assessing changes in nitrogen (N) dynamics pre- and post-rain, provides insights into its transport to surface water systems. However, the relationships between N discharge in catchments dominated by different land uses and water quality remain unclear. This study quantified variations in key N components in ponds across forest, paddy field, and orchard catchments before and after six rainfall events. The results showed that nitrate (NO₃⁻-N) was the main N component in the ponds. Post-rainfall, N concentrations increased, with ammonium (NH₄⁺-N) and particulate nitrogen (PN) exhibiting significant elevations in agricultural ponds. Orchard catchments contributed the highest N load to the ponds, while forest catchments contributed the lowest. Following a heavy rainstorm event, total nitrogen (TN) loads in the ponds within forest, paddy field, and orchard catchments reached 6.68, 20.93, and 34.62 kg/ha, respectively. These loads were approximately three times higher than those observed after heavy rain events. The partial least squares structural equation model (PLS-SEM) identified that rainfall amount and changes in water volume were the dominant factors influencing N dynamics. Furthermore, the greater slopes of forest and orchard catchments promoted more N loss to the ponds post-rain. In paddy field catchments, larger catchment areas were associated with decreased N flux into the ponds, while larger pond surface areas minimized the variability in N concentration after rainfall events. In orchard catchment ponds, pond area was positively correlated with N concentrations and loads. This study elucidates the effects of rainfall characteristics and catchment heterogeneity on N dynamics in surface waters, offering valuable insights for developing pollution management strategies to mitigate rainfall-induced alterations. Full article

Aerosol optical depth (AOD) serves as a critical indicator for atmospheric aerosol monitoring and air quality assessment, and quantifies the radiative attenuation caused by airborne particulate matter. This study uses MODIS remote sensing imagery together with land use transition datasets (2000–2020) and road network density metrics (2014–2020), to investigate the spatiotemporal evolution of AOD in Zhejiang Province and its synergistic correlations with urbanization patterns and transportation infrastructure. By integrating MODIS_1KM AOD product, grid-based road network density mapping, land use dynamic degree modeling, and transfer matrix analysis, this study systematically evaluates the interdependencies among aerosol loading, impervious surface expansion, and transportation network intensification. The results indicate that during the study period (2000–2020), the provincial AOD level shows a significant declining trend, with obvious spatial heterogeneity: the AOD values in eastern coastal industrial zones and urban agglomerations continue to increase, with lower values dominating southwestern forested highlands. Meanwhile, statistical analyses confirm highly positive correlations between AOD, impervious surface coverage, and road network density, emphasizing the dominant role of anthropogenic activities in aerosol accumulation. These findings provide actionable insights for enhancing land-use zoning, minimizing vehicular emissions, and developing spatially targeted air quality management strategies in rapidly urbanizing regions. This study provides a solid scientific foundation for advancing environmental sustainability by supporting policy development that balances urban expansion and air quality. It contributes to building more sustainable and resilient cities in Zhejiang Province. Full article