Search Results (1,961)

Volatile organic compounds (VOCs) are key precursors of ozone (O₃) and secondary organic aerosols (SOA), among which biogenic VOCs (BVOCs) constitute the dominant natural source. However, large uncertainties remain in the magnitude, spatial distribution, and seasonal variability of BVOC emissions in China under rapidly changing vegetation and climate conditions. In this study, a refined BVOC emission inventory for China in 2023 was developed using the Model of Emissions of Gases and Aerosols from Nature (MEGAN v3.2) driven by WRF meteorological simulations and MODIS vegetation data. The estimated annual BVOC emissions reached 41.70 Tg, including 26.90 Tg isoprene, 4.84 Tg monoterpenes, 0.55 Tg sesquiterpenes, and 9.41 Tg other VOCs. The corresponding ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) were 346.12 Tg yr⁻¹ and 2137.51 Gg yr⁻¹, respectively. Emissions exhibited a pronounced south–north gradient with hotspots in Guangxi, Guangdong, and Yunnan, and peaked in summer. Broadleaf forests were identified as the dominant emission sources, followed by savannas and shrublands. Isoprene contributed most to OFP, whereas monoterpenes dominated SOAFP. Compared with previous inventories, the updated vegetation data, meteorological inputs, and refined chemical speciation improve the representation of BVOC emissions and their spatial patterns in China. These results highlight the important role of BVOCs in regional O₃ and SOA formation and provide an improved emission basis for atmospheric chemistry modeling and air-quality management. Full article

Greenhouse gas (GHG) emissions are one of the leading environmental concerns faced nowadays. The chemical looping combustion (CLC) process is one of the main processes that aim for carbon capture, utilization, and storage (CCUS), allowing the generation of a high-purity CO₂ stream that can be easily captured. Brazil has a wide variety of biomasses that could be applied to CLC, and the behavior of these biomasses can be predicted using machine learning algorithms. An artificial neural network (ANN) was created considering the biomass characteristics (proximate and ultimate analysis) and fuel reactor temperature as input data to assess their influence on CLC performance parameters (carbon capture efficiency, η_CC, and total oxygen demand, Ω_T) and gas compositions. The characteristics of five Brazilian biomasses were considered in the constructed ANN to predict their behavior on CLC performance. The ANN presented a good data fit, with R² achieving values higher than 0.973. Volatile matter played a crucial role in predicting the CLC performance parameters. Rice husks presented the smoothest results for η_CC and Ω_T, while the CO₂ composition was most affected by the eucalyptus characteristics. Experimental tests with all the biomasses should be carried out to provide a higher prediction capability of the algorithm. Full article

This study integrates in situ Quantitative Infrared Thermography (QIRT) and Building Energy Simulation (BES) to optimize the energy performance of an existing multi-story residential building in Istanbul, Türkiye. QIRT was utilized to diagnose thermal anomalies at the interfaces of uninsulated walls, the RC skeleton and fenestration junctions, revealing significant thermal bridging and air infiltration while enabling the calculation of the Temperature Index (TI) at critical interfaces. A key finding of the non-destructive diagnostic phase was the discrepancy between in situ (U_INSITU) and theoretical (U_CALC) thermal transmittance values, providing an empirical baseline for subsequent optimization. A multi-objective analysis, employing genetic algorithms (GAs), was conducted to evaluate 192 retrofit combinations, involving three insulation materials at four thicknesses and 16 glazing types. The impacts on primary energy consumption, CO₂ emissions, and 30-year global costs (per EN 15459-1:2017) were quantified under volatile economic conditions. Findings indicate that the energy-optimal solution reduces primary energy by 53% and CO₂ emissions by 51%, while the cost-optimal configuration reduces global costs by 52% relative to the reference case. The Pareto analysis reveals a robust convergence between financial and energy efficiency targets, proving that deep retrofitting is an economically imperative strategy for achieving national decarbonization goals and the 2053 net-zero vision. Full article

Ratoon rice systems often require high nitrogen (N) inputs and environmental costs. Here, we conducted field trials to evaluate the synergistic effects of different N synergists coupled with 15% N reduction on the greenhouse gas emissions, yield, and net ecosystem economic benefits (NEEB) of ratoon rice systems in central China. Five treatments were designed and implemented, including farmers’ fertilization practice (FFP), 15% N reduction (FFP-15%), and the application of humic acid (HA), 3,4-dimethylpyrazole phosphate (DMPP), and DMPP+SiO₂ (DMPP+SI) based on FFP-15%. The results showed that compared with FFP, FFP-15% significantly decreased CH₄ emissions by 36.49% (p < 0.01) and global warming potential (GWP) by 35.33% (p < 0.01) but exhibited no enhancing effect on NEEB. Relative to FFP-15% treatment, HA treatment demonstrated more consistent multi-gas mitigation effects, which significantly reduced the annual NH₃ volatilization by 12.41% (from 40.58 kg N ha⁻¹ to 46.33 kg N ha⁻¹, p < 0.01), CH₄ emissions by 20.62% (p < 0.01), and N₂O emissions by 28.50% (p < 0.01), thereby lowering the GWP by 21.12% (from 7.32 t CO₂-eq. ha⁻¹ to 9.28 t CO₂-eq. ha⁻¹, p < 0.01). Nevertheless, this environmental efficacy was accompanied by a significant grain yield penalty of 5.77% (p < 0.01), probably due to the accumulation of more nutrients in stem sheaths (rather than in grains), which reduced grain nutrient allocation and ultimately resulted in no significant enhancement of NEEB. In contrast, DMPP-based treatments did not affect NH₃ volatilization and CH₄ emissions but markedly decreased N₂O emissions (by 19.00% for DMPP treatment, p < 0.01) and enhanced the grain yield (by 4.23% for DMPP treatment and 8.34% for DMPP+SI treatment, p < 0.01) relative to FFP-15% treatment. Although DMPP+SI treatment showed no significant effect on GWP or GHGI, it significantly enhanced the NEEB by 19.74% (p < 0.01) compared with FFP-15% treatment. In conclusion, integrating DMPP with SiO₂ application under N reduction represents a feasible management strategy to advance green, low-carbon production of ratoon rice systems in central China. Full article

Araucaria araucana (Molina) K. Koch, an endemic conifer of Chile and Argentina, has been severely impacted in recent years by Araucaria Leaf Damage (ALD). Previous research has established that volatile organic compounds (VOCs) released by healthy (H) and leaf-damaged (LD) Araucaria araucana branches modulate the behavior of Sinophloeus porteri. Specifically, myrcene, the most abundant compound in healthy branches, acts as a repellent to this insect, whereas hibaene, found in high concentrations in leaf-damaged tissue, acts as an attractant. This study compared the chemical profiles of healthy and leaf-damaged branches across two distinct geographic areas: Nahuelbuta (PNN) and Villarrica (PNV) National Parks. Following VOC capture using Porapak Q and subsequent GC-MS analysis, 31 compounds were detected and 29 were identified. The results indicate that hibaene was consistently detected across health categories, whereas camphor was particularly abundant in leaf-damaged trees from PNV. Overall, the data suggest that tree health status is associated with marked changes in VOC profiles, although the present design does not allow constitutive and induced responses to be fully disentangled. Consequently, monitoring these volatile emissions represents a strategic tool for the early detection and mitigation of damage caused by pests and diseases in these forest ecosystems. Full article

Nitrogen (N) fertilization plays a key role in pasture productivity but also contributes to environmental losses, especially under tropical conditions. This study evaluated the effects of four N rates (0, 50, 75, and 100 kg N ha⁻¹) as urea on soil N dynamics, ammonia (NH₃) volatilization, nitrous oxide (N₂O) emissions, and biomass accumulation in Brachiaria brizantha cv. Piatã, cultivated in a clayey Oxisol in the Brazilian Savanna. The experiment was conducted over two pasture growth cycles during the late summer and early fall. NH₃ volatilization increased with the N rate and showed significant differences in the initial samplings of both cycles. N₂O emissions were low, strongly influenced by rainfall, and resulted in emission factors ≤ 0.3%. Soil NH₄⁺ and NO₃⁻ concentrations did not differ statistically among treatments. Biomass production increased over time on Cycle 2 but plateaued at greater doses, with no significant differences between treatments. The limited biomass response suggests physiological saturation or environmental constraints. Findings indicate that N losses and use efficiency are shaped by rainfall and plant demand. Full article

The present study was conducted to demonstrate the effects of Codium fragile extract on methane production using in vitro and in vivo experiments. An in vitro batch experiment was conducted to evaluate different inclusion levels of Codium fragile extract (0, 0.25, and 0.5% of substrate dry matter). Methane production significantly decreased in the 0.5% treatment (p < 0.05), whereas dry matter digestibility and total volatile fatty acid concentration were not significantly affected (p > 0.05). Based on the in vitro results, an in vivo feeding experiment was conducted using a 0.5% inclusion level of Codium fragile extract on Hanwoo steers. Methane emissions were significantly decreased by approximately 10% in steers fed Codium fragile extract (p < 0.05). In contrast, rumen fermentation characteristics, feed intake, average daily gain, and blood parameters were not significantly different between the treatments (p > 0.05). These results demonstrate that a dietary additive with 0.5% Codium fragile extract effectively reduced methane emissions without negatively affecting rumen fermentation and growth performance in Hanwoo steers. Full article

Commodity and carbon markets are central to natural resource allocation, energy security, and the effectiveness of carbon-pricing policies, yet their risk linkages can intensify sharply during crises. This study examines nonlinear, tail-dependent volatility spillovers across strategically important resource markets using a Quantile-on-Quantile connectedness framework. We employ weekly observed data from 3 January 2010 to 27 April 2025 for eleven futures markets spanning metals (copper, silver, gold), energy (WTI crude oil, heating oil, natural gas, gasoline), agricultural commodities (sugar, coffee, corn), and carbon emissions. Volatility is measured using GARCH-based estimates and embedded in quantile VAR dynamics to map state-contingent shock transmission across the distribution. The results indicate strong asymmetries: connectedness rises markedly in tail regimes and attains its highest levels during the COVID-19 pandemic and the Russia–Ukraine war, relative to the 2015–2016 energy market adjustment. Heating oil, gold, and natural gas frequently act as key volatility transmitters, while the carbon market shifts from a peripheral receiver to a more integrated and sometimes systemic node within the broader commodity risk network. The findings indicate that carbon-price risk propagates through resource markets in a regime-dependent manner, with implications for stress testing, tail-sensitive hedging, and the coordination of resource and climate policy under turbulent market states. Full article

Historically, the electricity sector in Greece was based on local lignite, which provided a stable and affordable base for electricity production. However, current European policy directions, including decarbonization and climate neutrality by 2050, have accelerated the transformation of traditional energy models, resulting in a gradual phasing-out of fossil fuels and an increasing integration of Renewable Energy Sources (RES). In line with EU policy priorities and in light of the new dynamics shaped by the EU Emissions Trading System (EU ETS), lignite gradually became unprofitable for the national economy, leading the Greek government to announce an accelerated lignite phase-out plan. However, the phase-out of domestic lignite, although consistent with climate objectives, rapidly increased the country’s energy dependency on natural gas and its exposure to natural gas price volatility. At the same time, increased investment in solar and wind technologies has reshaped the electricity mix; yet market design, limited system flexibility and inadequate infrastructure and storage capacity have not allowed the full utilization of RES benefits. This structural gap, in turn, raises critical questions about resilience and affordability. The paper provides evidence on these issues and offers a critical evaluation of the decarbonization pathway that has reshaped the country’s energy dependency. Full article

This study focuses on optimizing hybrid photovoltaic (PV)–wind–lithium-ion battery systems, aiming to balance lifecycle cost (LCC) minimization and power supply reliability (measured by loss of power supply probability, LPSP). A multi-algorithm optimization framework was constructed to compare the performance of Particle Swarm Optimization (PSO), Moth–Flame Optimization (MFO), Grey Wolf Optimization (GWO), and Hybrid Optimizer of PSO and GWO Merits (PSO-GWO) for off-grid power supply; additionally, a PSO-GWO was proposed to address multi-objective demands of economy, environment, and reliability for remote grid-connected power supply. Combined with system architecture design, energy management strategies, and component availability analysis, the PSO-GWO reduced 25-year LCC to $2.024 million, LPSP to 0.05, and cost of energy (COE) to $0.06254/kWh. PSO-GWO further optimized carbon emissions (CEs, operational carbon emissions only) to 2750 tons/year (14.1% lower than PSO) while maintaining LCC at $1.981 million and LPSP at 0.01. Thirty independent runs of each algorithm were conducted for statistical validation, and sensitivity analysis verified the algorithms’ robustness to PV efficiency, battery cost, wind speed fluctuations, battery price volatility, and carbon tax changes. The study also expanded the analysis to multiple climatic scenarios, providing an economical, reliable, low-carbon solution with strong generalizability. Full article

Industrial volatile organic compound (VOC) emissions from large-scale petroleum storage represent a persistent environmental challenge, particularly in agricultural perimeters where atmospheric “breathing” cycles drive localized soil loading. This study investigates the thermodynamic and spatial relationship between gasoline storage emissions and chemical contamination in the Constanta South terminal area using a multi-layered analytical approach. By integrating gas chromatography (GC-MS) headspace analysis with an artificial intelligence (AI) framework utilizing high-order polynomial regression, we quantified the source–path–receptor dynamics across a thermal gradient (12 °C to 70 °C). The results reveal a non-linear surge in VOC emissions at temperatures exceeding 37 °C, characterized by a shift toward medium-weight hydrocarbons (C4–C6) that act as carriers for heavier aromatics. The AI risk model identified a significant spatial gradient, identifying a 500 m “critical zone” where the Hazard Quotient (HQ) is elevated, necessitating technological upgrades like Vapor Recovery Units (VRUs) to mitigate ecological risks. Full article

Industrial-scale Anaerobic Digestion (AD) is a key technology for the energy recovery of agro-industrial and municipal waste and for the mitigation of greenhouse gas emissions; however, the actual operational performance of industrial biodigesters continues to show significant discrepancies with respect to the theoretical values reported in the scientific literature. In this context, there is still a lack of systematic analysis to identify which operating parameters are consistently monitored in industrial settings and which remain insufficiently explored, particularly those that describe the overall state of the digestion environment. To address this gap, a systematic literature review was conducted in the Scopus database for the period 2000–2026, complemented by a bibliometric analysis using VOSviewer software v1.6.18. 3. After applying inclusion criteria focused exclusively on industrial-scale and pilot systems, 1327 documents corresponding to the category of operating parameters were selected and analyzed using keyword co-occurrence networks and evaluation of occurrence frequencies and total link intensities. The analysis shows a marked concentration of the literature on a small set of classic parameters, highlighting pH (154 occurrences, 3667 link intensities), temperature (147 occurrences, 3255 link intensities), and ammonia (131 occurrences, 2824 link intensities) as the most recurrent variables in the industrial operation of anaerobic digesters. Complementarily, parameters such as chemical oxygen demand, total and volatile solids, and hydrogen sulfide have progressively increased their presence since 2015, mainly associated with effluent quality assessment, nutrient recovery, and overall process sustainability. In contrast, variables that integrate the state of the environment, such as electrical conductivity, oxidation-reduction potential, and the rheological properties of digestate, appear in less than $5\%$ of the studies analyzed, despite their ability to integrate information on stability, buffer capacity, and overall operating conditions. Taken together, these findings highlight an imbalance between the intensive use of traditional parameters and the limited incorporation of integrative indicators in industrial monitoring, suggesting that their systematic inclusion, together with the development of soft sensors and predictive models, could contribute to improving operational control and reducing the gap between the theoretical performance and actual behavior of industrial biodigesters. Full article

Accurate freeway Improvements in traffic state prediction accuracy and enhanced stability enable more proactive traffic control and demand management strategies, thereby reducing congestion spillover effects, unnecessary acceleration–deceleration cycles, and the resulting fuel consumption and emissions. Yet, this remains challenging due to the interplay between deterministic traffic flow mechanisms and stochastic disturbances. Purely data-driven models suffer from error accumulation under out-of-distribution conditions, while physics-based models lack flexibility in capturing nonlinear deviations. This paper proposes MDURP, a physics-constrained residual learning framework that reformulates prediction as a residual-space learning problem. A calibrated Cell Transmission Model generates a physically admissible baseline; deep learning models are then restricted to learning the residuals. Wavelet decomposition and GARCH volatility modeling address the multi-scale and heteroskedastic characteristics of these residuals. Experimental results demonstrate that MDURP consistently outperforms baseline models, reducing MAE by an average of 6.8%, RMSE by an average of 4%. The framework also suppresses long-term error accumulation, with MAPE escalation slowing from 0.79% to 0.58% per step. These gains confirm that anchoring deep learning within a physics-defined residual space enhances both accuracy and stability. Full article

Volatile organic compounds (VOCs, dominated by xylene, toluene, and benzene) and paint mist emissions from ship painting represent a major environmental and health concern, posing a critical bottleneck to the green transformation of the shipbuilding industry. To tackle this challenge, this study presents an integrated recovery system designed specifically for ship automatic-spraying robots. Guided by the synergistic principle of “air-curtain containment, multi-stage adsorption, and negative-pressure recovery,” the system features a modular design that ensures full compatibility with the robots’ spraying trajectory without operational interference. Core adsorption materials, namely glass fiber filter cotton and honeycomb activated carbon fiber, were selected to suit the high-humidity and high-pollutant-concentration environment typical of ship painting. An appropriately matched axial flow fan maintains stable negative pressure throughout the system. Furthermore, the design integrates an air curtain isolation subsystem and an automated control subsystem, enabling coordinated operation and real-time adjustment. Using ANSYS Fluent, geometric and flow field simulation models were established to analyze airflow distribution and pollutant adsorption behavior, which led to the optimization of key structural and material parameters. Field experiments conducted in shipyard environments demonstrated the system’s superior performance: it achieved a VOC removal efficiency of 88.4% and a paint mist capture efficiency of 85.7% under optimal working conditions, with a maximum simulated paint mist capture efficiency of 86.2%. The system maintained stable performance under complex vertical and overhead spraying conditions, with an efficiency attenuation of less than 1.5%, and its outlet emissions fully complied with the mandatory limits specified in the Emission Standard of Air Pollutants for the Shipbuilding Industry (GB 30981.2-2025). The relative error between experimental data and simulation results is less than 2%, confirming the reliability and practicality of the proposed system. This research provides an efficient and adaptable pollution control solution for green shipbuilding and offers valuable technical insights for the sustainable upgrading of automated painting processes in heavy industries. Full article

Reducing enteric methane emissions from ruminants has emerged as a critical environmental priority in the face of global climate change, given the substantial contribution of methane to agricultural greenhouse gas outputs. This study evaluated the potential of spearmint essential oil (SEO) to reduce methane production and enhance energy utilization efficiency using an in vitro rumen fermentation system. The experiment comprised a control (CON, no additive), three SEO doses (L-SEO: 100 mg/L; M-SEO: 200 mg/L; H-SEO: 400 mg/L), and a commercial essential oil blend (AGL: 150 mg/L). Results indicated that M-SEO and H-SEO significantly reduced methane production at 24 h from 58.11 mL/g DM in CON to 47.93 and 46.58 mL/g DM, respectively (p < 0.001), corresponding to reductions of 17.5% and 19.8%. Furthermore, M-SEO increased total volatile fatty acid concentration from 48.41 to 58.10 mmol/L and elevated the molar proportion of propionate, while significantly enhancing microbial crude protein production (p < 0.001). Microbial community analysis revealed that M-SEO increased bacterial alpha-diversity (Shannon index) (p = 0.001) and significantly enriched specific functional guilds, particularly the propionate-producing genus Succiniclasticum and the butyrate-producing genus Butyrivibrio. Interestingly, the abundance of dominant methanogens (Methanobrevibacter) was not reduced, suggesting a metabolic inhibition mechanism rather than a biocidal effect. Functional prediction analysis further supported this, indicating a downregulation of pathways associated with methanogenesis, including key enzymes such as methyl-coenzyme M reductase. In conclusion, SEO supplementation at 200 mg/L effectively reduced methane production by redirecting metabolic hydrogen toward propionate formation, without affecting overall fermentation. Therefore, the current study indicated that SEO could serve as a sustainable feed additive for mitigating enteric methane emissions in ruminants. Full article