Search Results (2,257)

Rice cultivation accounts for roughly 10% of worldwide anthropogenic greenhouse gas emissions, making it a significant source of methane (CH₄) Despite modest observational constraints, estimates of worldwide CH₄ emissions from rice agriculture range from 18–115 Tg CH₄ yr⁻¹. CH₄ is a potent greenhouse gas, and its oxidation produces tropospheric ozone (O₃), which is harmful to public health and crop production when combined with nitrogen oxides (NOx) and sunlight. Elevated O₃ levels reduce air quality, crop productivity, and human respiratory health. This study presents an AI-driven framework that combines ensemble learning, hyperparameter optimisation (HPs), and SHAP-based explainability to enhance CH₄ emission predictions from rice paddies in India, Bangladesh, and Vietnam. The model consists of two stages: (1) a classification stage to distinguish between zero and non-zero CH₄ emissions, and (2) a regression stage to estimate emission magnitudes for non-zero situations. The framework also incorporates O₃ and asthma incidence data to assess the downstream impacts of CH₄-driven ozone formation on air quality and health outcomes. Understanding the factors that drive optimal model performance and the relative importance of features affecting model outputs is still an ongoing field of research. To address these issues, we present an integrated approach that utilises recent improvements in model optimisation and employs SHapley Additive ExPlanations (SHAP) to find the most relevant variables affecting methane (CH₄) emission forecasts. In addition, we developed a web-based artificial intelligence platform to help policymakers and stakeholders with climate strategy and sustainable agriculture by visualising methane fluxes from 2018 to 2020, ensuring practical applicability. Our findings show that ensemble learning considerably improves the accuracy of CH₄ emission prediction, minimises uncertainty, and shows the wider benefits of methane reduction for climate stability, air quality, and public health. Full article

This study investigates the viability of using Ammonia as a carbon-free fuel for heat generation in terms of both reactive Nitrogen and Carbon emissions and production cost. As a carbon-free, environmentally friendly energy carrier, Ammonia has the potential to play a significant role in the sustainable, clean energy supply of the future. However, a major drawback of the steady combustion of ammonia for process heat generation is the extremely high levels of NO_x emissions it produces. In this pilot-scale study, the experimental results show that, through the oscillating combustion of NH₃, NO_x emissions can be reduced by as much as 80%. Production costs were compared to evaluate the economic feasibility of Ammonia-based heat; the results reveal the economic challenges associated with using Ammonia compared to natural gas, even when accounting for the development of CO₂ pricing. Only in terms of Carbon Capture and Storage requirements is Ammonia-based heat economically advantageous. This study also scrutinizes the economies of the production of gray and green Ammonia. Considering CO₂ certificate costs, the cost of green ammonia would be competitive in the near future. Full article

Ground-level ozone is a critical secondary air pollutant and greenhouse gas, especially in urban oil and gas regions, where it poses severe public health and environmental risks. Urban areas in North Texas have experienced persistently elevated ozone levels over the past two decades despite emission control efforts, highlighting the need for advanced forecasting tools. This study presents a hybrid recurrent neural network (RNN) model that combines Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures to predict 8 h average ground-level ozone concentrations over a full annual cycle. The model leverages one-hour lagged ozone precursor pollutants (VOC and NO_x) and seven meteorological variables, using a novel framework designed to capture complex temporal dependencies and spatiotemporal variability in environmental data. Trained and validated on multi-year datasets from two distinctly different urban air quality monitoring sites, the model achieved high predictive accuracy (R² ≈ 0.97, IoA > 0.96), outperforming standalone LSTM and Random Forest models by 6–12%. Beyond statistical performance, the model incorporates Shapley Additive exPlanation (SHAP) analysis to provide mechanistic interpretability, revealing the dominant roles of relative humidity, temperature, solar radiation, and precursor concentrations in modulating ozone levels. These findings demonstrate the model’s effectiveness in learning the nonlinear dynamics of ozone formation, outperforming traditional statistical models, and offering a reliable tool for long-term ozone forecasting and regional air quality management. Full article

The relative wind is a significant but underexplored influencing factor on the tradeoff between propulsion efficiency and pollutant emissions for ships. In this study, full-scale measurements obtained from four voyages of the training ship of Baekkyung were used to quantify the effects of relative wind on ship propulsion efficiency and pollutant emissions. The collected navigational, engine performance, and emission data—including parameters such as shaft power, engine load, specific fuel oil consumption (SFOC), and NO_x and SO_x concentrations—were synchronized and then analyzed using statistical methods and a generalized additive model (GAM). Statistical correlation analysis and a GAM were applied to capture nonlinear relationships between variables. Compared with linear models, the GAM achieved higher predictive accuracy (R² = 0.98) and effectively identified threshold and interaction effects. The results showed that headwind conditions increased the engine load by ~12% and SFOC by 8.4 g/kWh while tailwind conditions reduced SFOC by up to 6.7 g/kWh. NO_x emissions peaked under headwind conditions and exhibited nonlinear escalation beyond a relative wind speed of 12 kn. An operational window was identified for simultaneous improvement of the propulsion efficiency and reduction in pollutant emissions under beam wind and tailwind conditions at moderate relative wind speeds of 6–10 kn and an engine load of 30–40%. These findings can serve as a guide for incorporating relative wind into operational strategies for maritime autonomous surface ships. Full article

Ammonia (NH₃) is a promising zero-carbon fuel to eliminate carbon footprint while the high autoignition temperature and low combustion rate of NH₃ remain challenging for practical implementation. Using dimethyl ether (DME) as pilot ignition fuel can substantially promote the reactivity of NH₃, thus paving the way for a widespread application of NH₃. In this study, the ignition process and nitrogen oxides (NO_x) emissions of the NH₃ liquid spray ignited by liquid DME spray were numerically investigated using Converge software. The ambient temperatures (T_amb) ranging from 900 K to 1100 K were used to mimic the in-cylinder temperature typically encountered in turbocharger engines. The effect of ammonia energy ratio (AER) and fuel injection timing was examined as well. It is found that only half of NH₃ is consumed at T_amb = 900 K while 97.4% of NH₃ is burned at T_amb = 1100 K. Nitric oxide (NO) and nitrogen dioxide (NO₂) formation also have strong correlation with T_amb and NO₂ is usually formed around the periphery of NO through these two channels HO₂ + NO = NO₂ + OH and NO + O(+M) = NO₂(+M). Extremely high nitrous oxide (N₂O, formed by NH + NO = H + N₂O) and carbon monoxide (CO) are produced with the presence of abundant unburned NH₃ at T_amb = 900 K. Additionally, increasing AER from 60% to 90% results in slightly declined combustion efficiency of NH₃ from 98.7% to 94%. NO emission has a non-monotonical relationship with AER owing to the ‘trade-off’ relationship between HNO concentration and radical pool at varying AERs. A higher AER of 95% leads to failed ignition of NH₃. Advancing DME injection not only increases combustion efficiency, but also reduces NO_x and CO emissions. Full article

This research presents a detailed Life Cycle Assessment (LCA) of 26 mm Crown cork metal closures used in glass bottle packaging, with the objective of quantifying and comparing their environmental impacts across all life cycle stages. This study adheres to ISO 14040 and ISO 14044 standards and utilizes Microsoft Excel for structuring and documenting input–output data across each phase. The LCA encompasses three primary stages: raw material production (covering iron ore extraction and steel manufacturing), manufacturing processes (including metal sheet printing, forming, and packaging of closures), and the transport phase (distribution to bottling facilities). During the Life Cycle Inventory (LCI), steel production emerged as the most environmentally burdensome phase. It accounted for the highest emissions of carbon dioxide (CO₂), carbon monoxide (CO), nitrogen oxides (NO_x), and sulphur oxides (SO_x), while emissions of heavy metals and volatile organic compounds were found to be negligible. The Life Cycle Impact Assessment (LCIA) was carried out using the Eco-Indicator 99 methodology, which organizes emissions into impact categories related to human health, ecosystem quality, and resource depletion. Final weighting revealed that steel production is the dominant contributor to overall environmental impact, followed by the manufacturing stage. In contrast, transportation exhibited the lowest relative impact. The interpretation phase confirmed these findings and emphasized steel production as the critical stage for environmental optimization. This study highlights the potential for substantial environmental improvements through the adoption of low-emission steel production technologies, particularly Electric Arc Furnace (EAF) processes that incorporate high percentages of recycled steel. Implementing such technologies could reduce CO₂ emissions by up to 68%, positioning steel production as a strategic focus for sustainability initiatives within the packaging sector. Full article

The transportation sector is responsible for over 20% of Europe’s CO₂ emissions, significantly worsening urban air quality and compromising public health. Electric vehicles (EVs)—namely BEVs and PHEVs—offer some relief by lowering noise and pollution in urban settings. Nevertheless, their effectiveness in benefiting the environment relies on the current electricity generation mix. In accordance with national energy goals, this study evaluates the environmental effects of EV adoption in Greece until 2035, utilizing a scenario-based approach grounded in the forecasts of the Greek National Energy and Climate Plan. Three different electrification pathways are examined to explore how varying levels of electric vehicle adoption and progress in decarbonizing the power sector could reduce air pollution, particularly in cities. By comparing the projected CO₂, CO, NO_x, PM₁₀, and SO₂ pollutant output from BEVs and PHEVs with those of internal combustion engine vehicles, the study highlights the significance of integrating renewable energy sources and assesses the potential for EVs to reduce emissions within Greece’s changing energy mix. Full article

The implementation of Euro 7 emission standards demands advanced real-time NO_x monitoring systems for diesel vehicles. Existing unified models inadequately capture phase-dependent emission mechanisms during cold-start, urban, and highway operation. This study develops a novel Mixture of Experts (MoE) architecture with data-driven phase classification based on aftertreatment thermal dynamics. Real-world data from a Euro 6d commercial vehicle (3247 PEMS samples) were classified into three phases, cold (<70 °C coolant temperature), hot low-speed (<90 km/h), and hot high-speed (≥90 km/h), validated through t-SNE analysis (silhouette coefficient = 0.73). The key innovation integrates thermal–kinematic domain knowledge with specialized XGBoost regressors, achieving R² = 0.918 and a 58% RMSE reduction versus unified models (RMSE = 1.825 mg/s). The framework operates within real-time constraints (1.5 ms inference latency), integrating autoencoder-based anomaly detection (95.2% sensitivity) and Model Predictive Control (11–13% NO_x reduction). This represents the first systematic phase-specific NO_x modeling framework with validated Euro 7 OBM compliance capability, providing both methodological advances in expert allocation strategies and practical solutions for next-generation emission control systems. Full article

Concentrations of oxides of nitrogen (NO_x), as the sum total of nitric oxide (NO) and nitrogen dioxide (NO₂), the individual parts, i.e., NO and NO₂, (NO_x = NO + NO₂), and wind speed and direction measurements were gathered over a thirteen-year period (2011–2023) at the Giordan Lighthouse Geosciences Observatory, located on the Island of Gozo, forming part of the Maltese Archipelago (Central Mediterranean). The atmospheric concentration measurements were recorded with a Thermo Scientific Model 42i NO_x analyser, which employs the chemiluminescence technique to detect atmospheric traces of NO_x concentrations. In this case study, an investigation was conducted to understand the wind and seasonal variabilities of the measured concentrations. The highest NO_x concentrations occurred when the prevailing wind originated from the SE, while a broad minimum was observed when the wind blew from the S–W sector. The maxima were primarily associated with land-based sources, predominantly vehicular emissions on the main island, i.e., Malta. The amplitudes for NO, NO₂, and NO_x in relation to wind direction were 63%, 125%, and 121%, respectively. Significant variabilities were observed during the autumn season. Regarding wind speed, the NO_x concentrations reached their peak during high-wind-speed events, which are associated with transboundary pollution. A secondary broad maximum was observed for wind forces between 2 and 4, while the lowest concentrations were recorded at wind force 9. The NO_x concentrations exhibited a seasonal maximum in spring and a minimum in winter, which contrasts with the findings from the Monte Cimone station in Italy. The seasonal amplitudes for NO, NO₂, and NO_x were 46%, 15%, and 17%, respectively. It is evident that NO concentrations exhibited a greater seasonal variability, whereas NO₂ concentrations demonstrated significant variability in relation to wind direction. Full article

Traffic-related air pollutants have significant impacts on urban air quality. Given the critical role of transportation infrastructure in shaping traffic congestion and vehicle emissions, understanding how road networks affect these air pollutants is particularly important in Türkiye, where rapid road expansion is a key component of transportation policy. This study examines the environmental implications of road infrastructure development in Türkiye by analyzing its impact on NO_x emissions and PM₁₀ concentrations at the provincial level from 2012 to 2022. The dynamic panel results indicate that an increase in road length—including total roads, divided roads, and asphalt roads—significantly reduces NO_x emissions, suggesting that expanded road networks may help alleviate air pollution by mitigating congestion and improving traffic flow. In contrast, no statistically significant relationship is found between road length and PM₁₀ concentrations, suggesting that particulate pollution is more strongly influenced by non-traffic sources such as industry, residential heating, or natural factors. By examining provincial road networks and differentiating between road types, this study provides novel evidence on the heterogeneous effects of road infrastructure on air quality, thereby addressing a significant gap in the existing literature and offering insights into how road infrastructure development influences environmental outcomes. Full article

To address the dual challenges of fossil fuel depletion and environmental pollution, developing clean, renewable alternative fuels is an urgent need. Biomass gas, including biomass syngas and biogas, offers significant potential as an internal combustion engine alternative fuel due to its widespread availability and carbon-neutral properties. This review summarizes research on biomass gas application in dual-fuel diesel engines. Firstly, biosyngas and biogas production methods, characteristics, and purification needs are detailed, highlighting gas composition variability as a key factor impacting engine performance. Secondly, dual-fuel diesel engine operating modes and their integration with advanced low-temperature combustion technologies are analyzed. The review focuses on how biomass gas affects combustion characteristics, engine performance, and emissions. Results indicate dual-fuel mode effectively reduces diesel consumption, emissions, while its carbon-neutrality lowers life-cycle CO₂ emissions and generally suppresses NO_x formation. However, challenges include potential BTE reduction and increased CO and HC emissions at low loads. Future research should prioritize gas quality standardization, intelligent combustion system optimization, and full-chain techno-economic evaluation to advance this technology. Overall, this review concludes that dual-fuel operation with biomass gases can achieve high diesel substitution rates, significantly reducing NO_x and particulate matter emissions. However, challenges such as decreased brake thermal efficiency and increased CO and HC emissions under low-load conditions remain. Future efforts should focus on gas composition standardization, intelligent combustion control, and system-level optimization. Full article

Tropospheric ozone (O₃) is a harmful air pollutant and a short-lived greenhouse gas. To find effective O₃ reduction strategies, it is essential to understand the sensitivity of O₃ concentrations to its precursors, nitrogen oxides (NO_x), and volatile organic compounds (VOC). This study applied the Community Multi-Scale Air Quality model (CMAQ) to assess the effects of domestic anthropogenic emissions in 2015 and 2050. The emission scenarios were based on Japan’s CO₂ reduction targets, assuming an 80% decrease by 2050. Sensitivity analysis was performed by adjusting NO_x and VOC emissions by ±10% and ±20%, respectively, and examining seasonal and regional variations in the O₃ response. The results show that O₃ levels will decrease notably in spring and summer by 2050, although concentrations will still exceed the standards in some areas. NO_x reductions lead to significant O₃ decreases, while VOC reductions show limited benefits, except in urban regions such as Kanto and Kansai. In winter, NO_x reductions may even increase O₃ levels due to weakened titration. Overall, the findings highlight the importance of prioritizing NO_x control measures for effective O₃ mitigation in Japan’s future energy transition. Full article

Gansu Province, as a core region for the development of renewables in China, has significant research value in the synergistic pathway of its power supply–demand structure and pollution and carbon emission reduction goals. This study focuses on the pollution and carbon reduction challenges faced by Gansu Province and the current situation of power supply and demand. Based on scenario-setting methods, it couples the GCAM-China model with the DPEC model to construct a pathway for pollution reduction and carbon emission reduction in Gansu’s power system and predicts the future change in pollution and carbon emission reduction. It provides important support for the sustainable development of Gansu Province. Research indicates that by significantly increasing the share of renewable energy in the short term (2025–2040)—with installed capacity growing by 1–2 times and electricity generation reaching 148.6 billion kWh—the power sector can achieve carbon neutrality and near-zero pollution emissions by 2060. And the provincial carbon emissions will be 92.8% lower than in 2020, SO₂ emissions will be 93.9% lower, and NO_x emissions will be 92.3% lower, thus the synergistic benefits of pollution reduction and carbon reduction will be significantly enhanced. Additionally, the lower costs of production, energy dispatch, and renewable energy storage will increase industrial electrification rates by about 40% between 2020 and 2040. Gansu Province should vigorously promote the transformation of its energy structure while improving the flexibility of the power system to facilitate the integration and absorption of renewable energy. Promoting the development of clean and low-carbon technologies from both supply and demand sides, facilitating the substitution of traditional fossil fuels, and providing clean, reliable, and economical power assurance for the sustainable development of Gansu Province. Full article

Nitrogen oxides (NO_x) are major atmospheric pollutants, and their escalating emissions, driven by rapid economic development and urbanization, pose a severe threat to both the ecological environment and human health. Conventional denitrification technologies are often hampered by high costs, significant energy consumption, and stringent operational conditions, making them increasingly inadequate in the face of tightening environmental regulations. In this context, photocatalytic technology, particularly systems based on graphitic carbon nitride (g-C₃N₄), has garnered significant research interest for NO_x removal due to its visible-light responsiveness, high stability, and environmental benignity. To advance the performance of g-C₃N₄, numerous modification strategies have been explored, including morphology control, elemental doping, defect engineering, and heterostructure construction. These approaches effectively broaden the light absorption range, enhance the separation efficiency of photogenerated electron-hole pairs, and improve the adsorption and conversion capacities for NO_x. Notably, constructing heterojunctions between g-C₃N₄ and other materials (e.g., metal oxides, noble metals, metal–organic frameworks (MOFs)) has proven highly effective in boosting catalytic activity and stability. Furthermore, the underlying photocatalytic mechanisms, encompassing the generation and migration pathways of charge carriers, the redox reaction pathways of NO_x, and the influence of external factors like light intensity and reaction temperature, have been extensively investigated. From an application perspective, g-C₃N₄-based photocatalysis demonstrates considerable potential in flue gas denitrification, vehicle exhaust purification, and air purification. Despite these advancements, several challenges remain, such as limited solar energy utilization, rapid charge carrier recombination, and insufficient long-term stability, which hinder large-scale implementation. Future research should focus on further optimizing the material structure, developing greener synthesis routes, enhancing catalyst stability and poison resistance, and advancing cost-effective engineering applications to facilitate the practical deployment of g-C₃N₄-based photocatalytic technology in air pollution control. Full article

Urban air pollution caused by vehicular emissions remains one of the most pressing environmental challenges, negatively affecting both public health and climate processes. In Kokshetau, Kazakhstan, where electric vehicle (EV) adoption accounts for only 0.019% of the total fleet and charging infrastructure is nearly absent, reducing transport-related emissions requires short-term and cost-effective solutions. This study proposes an integrated approach combining urban ecology principles with computational modeling to optimize traffic signal control for emission reduction. An artificial neural network (ANN) was trained using intersection-specific traffic data to predict emissions of carbon monoxide (CO), nitrogen oxides (NO_x), sulfur dioxide (SO₂), and particulate matter (PM_2.5). The ANN was incorporated into a nonlinear optimization framework to determine traffic signal timings that minimize total emissions without increasing traffic delays. The results demonstrate reductions in emissions of CO by 12.4%, NO_x by 9.8%, SO₂ by 7.6%, and PM_2.5 by 10.3% at major congestion hotspots. These findings highlight the potential of the proposed framework to improve urban air quality, reduce ecological risks, and support sustainable transport planning. The method is scalable and adaptable to other cities with similar urban and environmental characteristics, facilitating the transition toward eco-friendly mobility and integrating data-driven traffic management into broader climate and public health policies. Full article