Search Results (1,047)

Air pollution poses a significant threat to public health and the environment, particularly fine particulate matter (PM2.5). Machine learning (ML) models have proven their accuracy in classifying and predicting air pollution levels. This research trains and compares the performance of eight machine learning regression models on a time series air quality dataset containing data from 12 dispersed air quality stations in Kuwait, to predict the PM2.5 Air Quality Index (AQI). After cleaning then trimming the large dataset to about 13.4% of its original size, we performed thorough data visualization and analysis of the dataset to identify important patterns. Next, in a set of five experiments exploring feature pruning, the tree-based models, namely Gradient Boosting and AdaBoost, generated mean square errors below 1.5 and $R^2$ numbers above 0.998, outperforming the other ML models. By integrating meteorological data, pollution source information, and geographical factors specific to Kuwait, these models provide a precise prediction of air quality levels. This research contributes to a deeper understanding and visualization of Kuwait’s air pollution challenges, and draws some public policy recommendations to mitigate environmental and health impacts. Full article

When driving at highway speeds, the airtightness and ventilation mode of the cabin can significantly affect the in-cabin air quality. Accordingly, this study conducted on-road driving experiments along four highways in Tainan City, Taiwan, to examine the effects of different ventilation strategies and driving speeds on the concentrations of three pollutants (carbon dioxide (CO₂), PM2.5, and PM10) in the cabin of a mid-size sedan. During the test, the vehicle will travel at a constant speed of 60, 70, 80, 90, 100, 110 and 120 km/h depending on the traffic conditions. When driving on the system interchanges, the vehicle speed was maintained at 40 and 50 km/h. Ventilation strategies are divided into fresh air mode and recirculation air mode. The results revealed that leakage ventilation at high speeds allowed more outdoor air to infiltrate the cabin. This reduced the CO₂ concentration but slightly increased the particulate matter (PM) when the ventilation system was operated in the recirculation mode. The continuous use of the recirculation air mode for extended periods resulted in a potentially hazardous increase in the CO₂ concentration. Thus, periodic switching to the fresh air mode is recommended to ensure that the in-cabin CO₂ concentration remains below the ASHRAE threshold of 1000 ppm. In the fresh air mode, the PM2.5 and PM10 concentrations decreased as the vehicle speed increased. In the recirculation mode, the cabin filters maintained lower in-cabin PM levels than in the fresh-air mode. The experimental data were fitted using a curve-fitting technique to quantify the relationships between the vehicle speed and the in-cabin CO₂, PM2.5, and PM10 concentrations under the two ventilation strategies. The findings of this study provide useful practical guidelines for optimizing the vehicle ventilation strategy to improve the in-cabin air quality and enhance occupant health and safety during highway driving. Full article

Bioavailable fractions of particulate-bound trace elements are key determinants of inhalation toxicity, yet air-quality assessments typically rely on total metal concentrations, which may underestimate health risks. This study integrates the exchangeable (F1) and reducible (F2) fractions of trace elements in fine (PM_2.5) and coarse (PM₁₀) particulate matter with multi-criteria decision-making (TOPSIS) and similarity-based classification (Czekanowski’s method). Archival weekly-integrated samples from the summer season were collected at eight industrially influenced sites in southern Poland. Sequential extraction (F1–F2) and ICP-MS were applied to quantify concentrations of cadmium, cobalt, chromium, nickel, and lead in PM_2.5 and PM₁₀. Aggregated hazard values were then derived with TOPSIS, and site similarity was explored using Czekanowski’s reordered distance matrices. Regulatory targets for cadmium and nickel, and the limit for lead in PM₁₀ were not exceeded, but F1/F2 profiles revealed pronounced site-to-site differences in potential mobility that were not evident from total concentrations. Rankings were consistent across size fractions, with site P₁ exhibiting the lowest hazard indices and P₈ the highest, while mid-rank sites formed reproducible similarity clusters. The proposed chemical-fractionation and multivariate framework provides a reproducible screening tool for multi-element exposure, complementing compliance checks and supporting prioritisation of sites for targeted investigation and environmental management. In the sustainability context, bioavailability-based indicators strengthen air-quality assessment by linking monitoring data with health-relevant and cost-effective management strategies, supporting efficient resource allocation and reducing exposure in vulnerable populations. Full article

Introduction: Increased exposure to air pollution poses a burden to society and healthcare systems worldwide, with increased risk of morbidity and mortality. Indoor concentrations of air pollutants, such as particulate matter, are a public health concern because they can be present in higher concentrations than outside. Unlike the effects of indoor environmental tobacco smoke (ETS), there is a dearth of research that includes the impact of e-cigarettes on particulate matter concentrations in the home, which is the focus of this study. Method: Participant, household, and sensor information were obtained from 164 lower-income households located in Cornwall, South West of England. Daily sensor readings were obtained for PM_2.5 for one year. Descriptive statistics were used to describe study participant characteristics and health status. Mean indoor averages, median PM_2.5 measurements, and two-tailed tests were used to assess differences in concentrations of PM_2.5. Results: The 164 surveyed households included 315 residents (67% female) with a mean adult age of 57 (22–92). Half of all homes were in the 10% most deprived neighbourhoods in England. Thirty-four per cent of participants were current smokers, and of these 36% have asthma and had seen a doctor in the last year (cf. never smokers 14%, ex-smokers 25%). Mean annual PM_2.5 was highest in smoking households (14.07 µg/m³) and smoking and vaping households (9.18 µg/m³), and lower in exclusive vaping households (2.00 µg/m³) and smoke and vape-free households (1.28 µg/m³). Monthly levels of PM_2.5 fluctuated seasonally for all groups, with the highest recordings in winter and the lowest in summer. Discussion and Conclusion: In this preliminary study, we conducted secondary data analyses using monitoring data from a large health and housing study to assess factors leading to elevated indoor concentrations of particulate matter. Indoor concentrations appeared to be highest in homes where residents smoked indoors. The use of e-cigarettes in the home also appeared to modify concentrations of particulate matter, but levels were lower than in homes with tobacco smoke. We were not able to determine the relationship between smoking and/or vaping indoors and particulate matter, which supports the need for studies of larger sample sizes and more complex longitudinal monitoring. This will help assess the timing and extent of exposures resulting from smoking and vaping indoors, along with a range of other chemical and biological exposures and their corresponding health effects. Full article

Indoor sports facilities present unique challenges for air quality management due to high crowd densities and limited ventilation. This study investigated air quality in a municipal athletic facility in Komotini, Greece, focusing on concentrations of airborne particulate matter (PM_1.0, PM_2.5, PM₁₀), humidity, and temperature across spectator zones, under varying mask scenarios. Sensing devices were installed in the stands to collect high-frequency environmental data. The system, based on optical particle counters and cloud-enabled analytics, enabled real-time data capture and retrospective analysis. The main experiment investigated the impact of spectators wearing medical masks during two basketball games. The results show consistently elevated PM levels during games, often exceeding recommended international thresholds in the spectator area. Notably, the use of masks by spectators led to measurable reductions in PM_1.0 and PM_2.5 concentrations, because they seem to have limited the release of human-generated aerosols as well as the amount of movement among spectators, supporting their effectiveness in limiting fine particulate exposure in inadequately ventilated environments. Humidity emerged as a reliable indicator of occupancy and potential high-risk periods, making it a valuable parameter for real-time monitoring. The findings underscore the urgent need for improved ventilation strategies in small to medium-sized indoor sports facilities and support the deployment of low-cost sensor networks for actionable environmental health management. Full article

The environmental fate of nickel (Ni) is dictated by its interaction with organic matter (OM), yet the specific roles of OM source and molecular size remain unclear. This study investigated the binding characteristics of Ni with size-fractionated dissolved OM (DOM) from the water column and alkaline-extractable OM (AEOM) from sediments in a tropical wetland. Using ultrafiltration and spectroscopy, we found that sedimentary AEOM was predominantly high-molecular-weight (HMW) and terrestrial compounds, whereas aquatic DOM was dominated by low-molecular-weight (LMW), microbial-derived compounds. Counterintuitively, the highest Ni binding affinity (NiBA) for both DOM and AEOM occurred in the smallest-molecular-weight fraction (<0.3 kDa). Predictive models confirmed this divergence: the model for the more chemically homogeneous AEOM was highly predictive (r = 0.89), while the model for the complex DOM was less robust (r = 0.70). Our findings demonstrate that LMW fractions are hotspots for Ni binding, challenging the common assumption that larger molecules are more reactive. We conclude that biogeochemical processing in sediments creates an OM pool that is chemically distinct and more predictable than that in the overlying water. This distinction is critical for accurately assessing Ni mobility and ecological risk in aquatic systems. Full article

Urban expansion intensifies population exposures to fine particulate matter (PM_2.5). Trees mitigate pollution by dry deposition, in which particles settle on plants. However, city-scale models frequently overlook differences in tree species and structure. This study assesses PM_2.5 removal by individual city-owned street trees in Mississauga, Canada, throughout the 2019 leaf-growing season (May to September). Using a modified i-Tree Eco framework, we evaluated the removal of PM_2.5 by 200,560 city-owned street trees (245 species) in Mississauga from May to September 2019. The model used species-specific deposition velocities (V_d) from the literature or leaf morphology estimates, adjusted for local winds, a 3 m-resolution satellite-derived Leaf Area Index (LAI), field-validated, crown area modelled from diameter at breast height, and 1 km² resolution PM_2.5 data geolocated to individual trees. About twenty-eight tons of PM_2.5 were removed from 200,560 city-owned trees (245 species). Coniferous species (14.37% of trees) removed 25.62 tons (92% of total), much higher than deciduous species (85.63%, 2.18 tons). Picea pungens (18.33 tons, 66%), Pinus nigra (3.29 tons, 12%), and Picea abies (1.50 tons, 5%) are three key species. Conifers’ removal efficiency originates from the faster deposition velocities, larger tree size, and dense foliage, all of which enhance particle deposition. This study emphasizes species-specific approaches for improving urban air quality through targeted tree planting. Prioritizing coniferous species such as spruce and pine can improve pollution mitigation, providing actionable strategies for Mississauga and other cities worldwide to develop green infrastructure planning for air pollution. Full article

Indoor air pollution remains a critical health issue in the rural areas of low- and middle-income countries like Indonesia, where solid fuels are commonly used for cooking. This study assessed real-time indoor particulate matter (PM) concentrations in three rural households in Jorong V Botung, West Sumatra, using PurpleAir low-cost sensors (PurpleAir Inc., Draper, UT, USA). Mass concentrations of PM₁, PM_2.5, and PM₁₀, along with size-segregated number concentrations (0.3–10 µm), were monitored continuously over six days (30 March–4 April 2024) during the Eid al-Fitr holiday, which involves extensive cooking activities. PM_2.5 concentrations frequently exceeded 200 µg/m³, with a peak of 249.9 µg/m³ recorded during morning cooking hours. The smallest particle size (0.3–0.5 µm) dominated number concentrations, reaching up to 17,098 particles/dL, while larger particle levels were significantly lower. Strong positive correlations (r > 0.95) were observed among PM₁, PM_2.5, PM₁₀ and AQI, indicating that cooking emissions substantially contributed to indoor PM levels. The findings highlight the need for targeted interventions, including clean fuel subsidies, improved ventilation, and public awareness efforts. This study contributes critical data on indoor air quality in rural Indonesia and supports broader initiatives to reduce exposure to household air pollution in Southeast Asia. Full article

Polymer matrix composites have been used extensively in the aerospace and automotive industries. Nevertheless, the growing demand for composites raises concerns about the thermal stability, cost, and environmental impacts of synthetic fillers like graphene and carbon nanotubes. Hence, this study investigates the possibility of enhancing the thermomechanical properties of polymer composites through the incorporation of agricultural waste as fillers. Particles from walnut, coffee, and coconut shells were used as fillers to create particulate composites. Bio-based composites with 10 to 30 wt.% filler were created by sifting these particles into various mesh sizes and dispersing them in an epoxy matrix. In comparison to the pure polymer, DSC results indicated that the inclusion of 50 mesh 30 wt.% agricultural waste fillers increased the glass transition temperature by 8.5%, from 55.6 °C to 60.33 °C. Also, the TGA data showed improved thermal stability. Subsequently, the agricultural wastes were employed as reinforcement for laminated composites containing woven glass fiber with a 50% fiber volume fraction, eight plies, and varying particle filler weight percentages from 0% to 6% with respect to the laminated composite. The hybrid laminated composite demonstrated improved impact resistance of 142% in low-velocity impact testing. These results demonstrate that fillers made of agricultural wastes can enhance the thermomechanical properties of sustainable composites, creating new environmentally friendly prospects for the automotive and aerospace industries. Full article

The emissions from biomass combustion systems have recently been the subject of increased attention. In addition to elevated concentrations of particulate matter and hydrocarbons (HCs) in the flue gas, significant levels of NO_x emissions occur depending on the used fuel, such as biogenic residues. In response to legal requirements, owners of medium-sized plants (≈100 kW) are now also forced to minimize these emissions by means of selective catalytic reduction systems (SCR). The implementation of a selective sensor is essential for the efficient dosing of the reducing agent, which is converted to ammonia (NH₃) in the flue gas. Preliminary laboratory investigations on a capacitive NH₃ sensor based on a zeolite functional film have demonstrated a high sensitivity to ammonia with minimal cross-influences from H₂O and NO_x. Further investigations concern the application of this sensor in the real flue gas of an ordinary wood-burning stove and of combustion plants for biogenic residues with an ammonia dosage. The findings demonstrate a high degree of agreement between the NH₃ concentration measured by the sensor and an FTIR spectrometer. Furthermore, the investigation of the long-term stability of the sensor and the poisoning effects of SO₂ and HCl are of particular relevance to the laboratory measurements in this study, which show promising results. Full article

Hydrogen is gaining significant interest from researchers because of its renewable and clean nature. In this study, we explored the effects of promoters and oxygen addition on biogas reforming. The promotion of catalysts with alkaline earth metals (Ca and Mg) improved the basicity of the catalyst, leading to enhanced catalytic activity and stability. The promotion of the Ni/TiO₂ catalyst with Ca showed higher CH₄ conversion and H₂ yield compared to the bare and Mg-Ni/TiO₂ catalysts. The enhanced activity of Ca-Ni/TiO₂ could be attributed to its high dispersion, small particulate size, and strong metal–support interaction. Adding oxygen to the reactor feed improved the activity and stability of the catalyst due to the simultaneous occurrence of dry and partial oxidative reforming. The maximum CH₄ conversion and H₂ yield of 81.13 and 37.5% were obtained at 800 °C under dry reforming conditions, which increased to 96 and 57.6% under dry-oxidative reforming (O₂/CH₄ = 0.5). The CHNS analysis of the spent Ca-Ni/TiO₂ catalyst also showed carbon deposition of only 0.58% after 24 h of continuous dry-oxidative reforming compared to 25.16% under continuous dry reforming reaction. XRD analysis of the spent catalyst also confirmed the formation of carbon deposits under dry reforming. Adding oxygen to the feed resulted in the simultaneous removal of carbon species formed over the catalytic surface through gasification. These findings demonstrate that Ca promotion combined with oxygen addition significantly improves the catalyst efficiency and durability, offering a promising pathway for stable, long-term hydrogen generation. The results highlight the potential of Ca–Ni/TiO₂ catalysts for integration into biogas reforming units at an industrial scale, supporting renewable hydrogen production and carbon mitigation in future energy systems. Full article

To promote soil carbon (C) sequestration and alleviate climate change, it is crucial to understand how vegetation types affect soil organic C (SOC) storage and stability in riverine wetlands. This study investigates the characteristics of SOC fractions and storage among different vegetation types and evaluates their soil C sequestration potential. Soil samples were collected and analyzed from four vegetation types (Typha orientalis, Tamarix chinensis, Avena sativa, and Phragmites australis) in wetlands at the junction of the middle and lower reaches of the Yellow River. Soil particulate organic C, dissolved organic C, and microbial biomass C contents of Avena sativa and Phragmites australis communities were higher than those of Tamarix chinensis and Typha orientalis communities (p < 0.001). Typha orientalis communities exhibited the highest SOC stability (4.31 ± 0.38), whereas Tamarix chinensis communities showed the lowest (1.34 ± 0.17) (p < 0.001). Soil organic C storage of Avena sativa (2.81 ± 0.32 kg m⁻²) and Phragmites australis (2.53 ± 0.06 kg m⁻²) communities was higher than that of Tamarix chinensis (0.88 ± 0.06 kg m⁻²) and Typha orientalis (1.35 ± 0.13 kg m⁻²) communities (p < 0.001). Soil electrical conductivity (EC) was significantly correlated with SOC fractions of Typha orientalis and Phragmites australis communities, while soil water content and particle size composition affected SOC fractions of Avena sativa communities (p < 0.05). Soil particle size composition affected the SOC storage of Typha orientalis, Tamarix chinensis, and Avena sativa communities (p < 0.05). Soil pH, water content, and EC influenced the SOC storage of Typha orientalis, Tamarix chinensis, and Phragmites australis communities (p < 0.05). These results demonstrate that Avena sativa and Phragmites australis communities play a vital role in maintaining C sink potential and ecological function in the Yellow River wetland. Nonetheless, the Typha orientalis community had greater C sequestration in the long term due to its high SOC stability. This research suggests that the effects of vegetation types should be considered when exploring the soil C cycle in riverine wetlands. Full article

The effective removal of oxygen (O), nitrogen (N), sulfur (S), and oxide inclusions from superalloy reverts is crucial for enhancing service life and achieving cost efficiency. However, refining DZ125 superalloy presents particular challenges, as conventional processes prove ineffective against hafnium (Hf) oxides. This study introduces an innovative purification method combining droplet electron-beam melting (EBM) with centrifugal directional solidification. Through this advanced EBM technique, we successfully produced ultrapure DZ125 superalloy with nitrogen content reduced below 5 ppm and total O + N + S content below 10 ppm. Most significantly, the process nearly eliminated Hf oxides from the reverts, meeting the stringent purity standards for DZ125 superalloy. We conducted a comprehensive analysis of inclusion morphology and composition in three distinct regions: the top slag layer, final solidification zone, and interior section of the ingot processed at varying EBM power levels. Our findings reveal that MC-type carbides at the slag–crucible interface were formed. There are HfO₂, TaC, and Al₂O₃ in the final solidification zone, with notable encapsulation of HfO₂ particulates within Al₂O₃ particles; and few HfO₂ and Al₂O₃ inclusions exist in the ingot interior. It is also found that increasing EBM power from 36 kW to 46 kW significantly improved impurity removal efficiency, as evidenced by substantial reductions in both inclusion quantity and size. This enhanced purification stems from two primary mechanisms: (1) flotation of inclusions during EBM melting, facilitated by Marangoni convection, droplet stirring effects, and centrifugal forces generated by ingot rotation; and (2) decomposition of stable oxides enabled by the high-energy density characteristic of EBM and high-vacuum processing environment. This combined approach demonstrates superior capability in overcoming the limitations of traditional refining methods, particularly for challenging Hf oxide removal, while establishing an effective pathway for superalloy revert recycling. Full article

In the context of increasingly stringent vehicle emission regulations, computer-aided engineering has been indispensable for optimizing the design and the operational strategies of emission control systems. This paper proposes a new filtration model for particulate filters that enables the accurate estimation of solid particle number emissions above 10 and 23 nm in diameter (SPN10 and SPN23, respectively). The model incorporates a persistent slip factor and a linear filtration efficiency of cake layers into the unit collector model proposed by Konstandopoulos and Johnson. This enhancement captures PM escape phenomena, such as a passage through interconnected large pores in filter walls. Simulations using a 1D + 1D two-channel framework with the proposed model successfully reproduced experimental results of SPN10 and SPN23 emissions downstream of a miniature gasoline particulate filter (GPF) tested with a synthetic particle generator. The model was also able to represent the observed continuous emissions during a cake filtration mode. Additional simulations using the same model parameters showed good agreement with experimental data of SPN10 and SPN23 emissions downstream of a full-size GPF tested with a gasoline direct injection (G-DI) engine under 5 steady-state operating conditions. The simulations revealed that particles in the 10–100 nm size range dominated the downstream SPN emissions despite their high filtration efficiency, whereas particles in the 100–200 nm size range were less significant. The proposed model is expected to contribute to the GPF developments to comply with the stringent emission regulations of the upcoming Euro 7. Full article

Although wind-assisted ship propulsion (WASP) is an effective technique for reducing the emissions of merchant ships, the best fuel type for complementing WASP remains an open question. This study presents a new original life cycle assessment method for ship fuels that uses a validated ship performance prediction model and actual operation conditions for a WASP ship. As a case study, the method is used to evaluate the fuel consumption and environmental impact of different fuels for a WASP ship operating in the Baltic Sea. Using a novel in-house-developed platform for predicting ship performance under actual operation conditions using hindcast data, the engine and fuel tank were sized while accounting for fluctuating weather conditions over a year. The results showed significant variation in the required fuel tank capacity across fuel types, with liquid hydrogen requiring the largest volume, followed by LNG and ammonia. Additionally, a well-to-wake life cycle assessment revealed that dual-fuel engines using green ammonia and hydrogen exhibit the lowest global warming potential (GWP), while grey ammonia and blue hydrogen have substantially higher GWP levels. Notably, NOx, SOx, and particulate matter emissions were consistently lower for dual-fuel and liquid natural gas scenarios than for single-fuel marine diesel oil engines. These results underscore the importance of selecting both an appropriate fuel type and production method to optimize environmental performance. This study advocates for transitioning to greener fuel options derived from sustainable pathways for WASP ships to mitigate the environmental impact of maritime operations and support global climate change efforts. Full article