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Search Results (1,817)

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Keywords = methane (CH4)

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18 pages, 455 KiB  
Article
Effects of Monensin, Calcareous Algae, and Essential Oils on Performance, Carcass Traits, and Methane Emissions Across Different Breeds of Feedlot-Finished Beef Cattle
by Pedro Guerreiro, Diogo F. A. Costa, Arnaldo C. Limede, Guilhermo F. S. Congio, Murillo A. P. Meschiatti, Priscila A. Bernardes and Flavio A. Portela Santos
Ruminants 2025, 5(1), 2; https://doi.org/10.3390/ruminants5010002 - 8 Jan 2025
Abstract
With the growing use of crossbred cattle in Brazilian feedlots and increasing pressure to reduce antibiotic use as growth promoters, this study examines the impact of three feed additives—monensin (MON), monensin with Lithothamnium calcareum (LCM), and a blend of essential oils (BEO)—on the [...] Read more.
With the growing use of crossbred cattle in Brazilian feedlots and increasing pressure to reduce antibiotic use as growth promoters, this study examines the impact of three feed additives—monensin (MON), monensin with Lithothamnium calcareum (LCM), and a blend of essential oils (BEO)—on the performance of Nellore (NEL) and crossbred (CROSS) cattle. A total of 90 Nellore and 90 crossbred bulls were assigned to a completely randomized block design with a 2 × 3 factorial design for 112 days, and all received the same diet with varying additives. Their methane (CH4) emissions were estimated. All data were analyzed using the emmeans package of R software (version 4.4.1). Crossbred cattle outperformed Nellore in average daily gain (ADG), hot carcass weight (HCW), and dry matter intake (DMI), though feed efficiency remained unaffected. Across additives, no significant differences were observed in ADG, HCW, or dressing percentage. However, LCM had a lower DMI than the BEO, while MON showed better feed efficiency than the BEO. A breed-by-additive interaction trend was noted for DMI as a percentage of body weight (DMI%BW), with Nellore bulls on LCM diets showing the lowest DMI%BW. Crossbreeds had greater net energy (NE) requirements for maintenance (NEm) and gain (NEg), and MON-fed animals had greater NEm and NEg than the BEO. Crossbred bulls had greater daily methane (CH4) emissions than Nellore bulls. Animals on the BEO had greater daily CH4 emissions and greater g CH4/kg metabolic BW than LCM bulls. In conclusion, the addition of Lithothamnium calcareum to monensin did not enhance performance compared to monensin alone. Monensin outperformed the BEO in feed efficiency and nutrient utilization. Full article
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<p>Interaction between breed and different feed additives on dry matter intake expressed as percentage of body weight (DMI%BW).</p>
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14 pages, 3248 KiB  
Article
Molecular Dynamics Simulation of CO2-ECBM Under Different Moisture Contents
by Xiaoyu Cheng, Xuanping Gong, Cheng Cheng, Quangui Li and Ziqiang Li
Energies 2025, 18(2), 239; https://doi.org/10.3390/en18020239 - 7 Jan 2025
Viewed by 384
Abstract
The interactions among water molecules, coal beds, and gases during the process of coal bed methane mining are highly complex. The water and methane (CH4)/carbon dioxide (CO2) molecules compete for adsorption and undergo a series of reactions that affect [...] Read more.
The interactions among water molecules, coal beds, and gases during the process of coal bed methane mining are highly complex. The water and methane (CH4)/carbon dioxide (CO2) molecules compete for adsorption and undergo a series of reactions that affect gas diffusion. In this study, Monte Carlo and molecular dynamics methods were used to investigate the microscopic mechanism of CH4/CO2 competitive adsorption and diffusion during CO2-enhanced coal bed methane mining (ECBM) under different moisture contents, and the geological storage potential of CO2 was predicted. The results showed that when the CO2 and water binding sites were independent of each other, the water molecules changed the electrostatic potential around the coal molecules, resulting in enhanced CO2 adsorption performance, as verified by the surface electrostatic potential. When the water molecules formed a water molecule layer, the adsorption capacity of the secondary adsorption sites provided was larger than that of the surface of the coal molecules, so the CO2 molecules were preferentially adsorbed on the secondary adsorption sites. However, the number of secondary adsorption sites available was not as large as that on the surface of the coal molecules. The interaction energies revealed that when the displacement effect of CH4 in the process of CO2-ECBM and the sequestration effect of CO2 were considered comprehensively, the best CO2 sequestration effect and a good CH4 displacement effect were obtained at a 3% moisture content. The worst CO2 sequestration effect was found at a 5% moisture content. After CO2 injection, the main adsorption layer of CH4 shifted from X = 5 and X = 9 to X = 8.7 and X = 12.5, respectively, and obvious detachment and diffusion occurred. The distribution of the molecular motion and diffusion coefficient revealed the considerable displacement and dispersion of the gas molecules. The distribution of the gas molecular velocity and diffusion coefficient indicated that a 3% moisture content was the ideal condition for CO2 displacement of CH4, and the CO2 sequestration effect was good. Full article
(This article belongs to the Section B: Energy and Environment)
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<p>Macromolecular planar modeling.</p>
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<p>Molecular modeling of coals with different moisture contents.</p>
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<p>Flow chart of the gas injection displacement simulation.</p>
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<p>CH<sub>4</sub>/CO<sub>2</sub> adsorption and heat of adsorption curves at different moisture contents.</p>
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<p>Electrostatic potential of the CO<sub>2</sub>/water–COAL surface.</p>
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<p>Schematic of water molecule layer adsorption in coals at different moisture contents.</p>
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<p>Interaction energy curves of gas and coal molecules.</p>
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<p>Radial distribution function of CH<sub>4</sub>/CO<sub>2</sub>-COAL for the displacement process.</p>
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<p>Velocity distribution curve.</p>
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<p>MSD and diffusion coefficient curves of the coal model at different moisture contents.</p>
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22 pages, 1979 KiB  
Review
Methods of Capture and Transformation of Carbon Dioxide (CO2) with Macrocycles
by Edilma Sanabria, Mauricio Maldonado, Carlos Matiz, Ana C. F. Ribeiro and Miguel A. Esteso
Processes 2025, 13(1), 117; https://doi.org/10.3390/pr13010117 - 4 Jan 2025
Viewed by 801
Abstract
Rapid industrialization and the indiscriminate use of fossil fuels have generated an impact that is affecting the climate worldwide. Among the substances that are causing climate change are several gases such as carbon dioxide (CO2), methane (CH4), nitrous oxide [...] Read more.
Rapid industrialization and the indiscriminate use of fossil fuels have generated an impact that is affecting the climate worldwide. Among the substances that are causing climate change are several gases such as carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and sulphur hexafluoride (SF6), among others. Particularly, carbon dioxide is one of the substances that has attracted the most attention from researchers, as it is responsible for more than three quarters of greenhouse gases. Because of this, many efforts have been directed towards the capture of CO2, its separation, adsorption and transformation into products that are less harmful to the environment or that even have added value in the industry. For this purpose, the use of different types of macrocycles has been explored mainly in the last 5 years. This review seeks to present the advances that have occurred in recent years in the capture and transformation of CO2 by different methods, to finally focus on the capture and transformation through macrocycle systems such as azacompounds, heterometallic macrocycles, calixpyrrols, modified cyclodextrins and metallic porphyrins, among others. Full article
(This article belongs to the Section Environmental and Green Processes)
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<p>Forms of representations of the CO<sub>2</sub> molecule.</p>
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<p>Main methods of capturing and transforming carbon dioxide.</p>
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<p>Some macrocyclic systems used in carbon dioxide fixation.</p>
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<p>Alpha-, beta- and gamma-cyclodextrins.</p>
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<p>Structure of porphyrins and metalloporphyrins.</p>
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<p>Participation of metallo-porphyrins in CO<sub>2</sub> transformation.</p>
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<p>Structure of calixarenes, resorcinarenes and pyrogallolarenes.</p>
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<p>Participation of calixarene-type systems in CO<sub>2</sub> transformation.</p>
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<p>Synthesis gas and Fisher–Tropsch process.</p>
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25 pages, 4952 KiB  
Article
Influence of Oxygen Carrier on the Autothermicity of a Chemical-Looping Reforming Process for Hydrogen Production
by Juliana López van der Horst, Maria Florencia Volpe Giangiordano, Felipe Suarez, Federico M. Perez, Martín N. Gatti, Gerardo F. Santori and Francisco Pompeo
Reactions 2025, 6(1), 5; https://doi.org/10.3390/reactions6010005 - 4 Jan 2025
Viewed by 405
Abstract
The chemical-looping reforming (CLR) of methane for hydrogen production employs a solid oxygen carrier (OC) and combines endothermic and exothermic stages, allowing for potential autothermal operation. This study conducted a thermodynamic analysis using Gibbs free energy minimization and energy balances to assess the [...] Read more.
The chemical-looping reforming (CLR) of methane for hydrogen production employs a solid oxygen carrier (OC) and combines endothermic and exothermic stages, allowing for potential autothermal operation. This study conducted a thermodynamic analysis using Gibbs free energy minimization and energy balances to assess the behavior of WO3, MnWO4, and NiWO4 as OCs in the CLR process. The effects of CH4:OC ratios and reactor temperatures on equilibrium composition and the energy performance were examined. The results demonstrated that elevated reduction temperatures promote OC conversion and the formation of more reduced solid products. Molar ratios above stoichiometric prevent carbon formation, whereas stoichiometric ratios result in higher H2 yield, achieving 98% at 1000 °C. However, these conditions do not support autothermal operation, which requires CH4:OC molar ratios above stoichiometric. Additionally, lower oxidation temperatures are preferred regardless of the OC, due to the lower heat needed to preheat the air, which has a greater effect on the net heat. For the reduction temperature, its effect depends on the type of OC analyzed. The maximum H2 yield obtained under autothermal operation was 88% for the three OCs, at 875 °C for MnWO4 and 775 °C for both WO3 and NiWO4. Full article
(This article belongs to the Special Issue Hydrogen Production and Storage, 3rd Edition)
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<p>Conversion of the oxygen carrier as a function of reduction temperature, parameterized for different mole amounts of (<b>a</b>) WO<sub>3</sub>; (<b>b</b>) MnWO<sub>4</sub>; (<b>c</b>) NiWO<sub>4</sub>.</p>
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<p>Selectivity towards solid products as a function of reduction temperature for 1.5 moles of (<b>a</b>) WO<sub>3</sub>; (<b>b</b>) MnWO<sub>4</sub>; (<b>c</b>) NiWO<sub>4</sub>.</p>
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<p>Carbon yield as a function of reduction temperature and mole amounts of (<b>a</b>) WO<sub>3</sub>; (<b>b</b>) MnWO<sub>4</sub>; (<b>c</b>) NiWO<sub>4</sub>.</p>
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<p>H<sub>2</sub> yield as a function of reduction temperature, parameterized for different mole amounts of (<b>a</b>) WO<sub>3</sub>; (<b>b</b>) MnWO<sub>4</sub>; (<b>c</b>) NiWO<sub>4</sub>.</p>
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<p>Solid product distribution for regeneration of the depleted OCs as a function of O<sub>2</sub> excess/defect for (<b>a</b>) WO<sub>3</sub>; (<b>b</b>) MnWO<sub>4</sub>; (<b>c</b>) NiWO<sub>4</sub>.</p>
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<p>Heat required (Q<sub>red</sub>) in the reduction reactor as a function of reduction and oxidation temperatures (T<sub>red</sub> and T<sub>ox</sub>, respectively), parameterized for different mole amounts of (<b>a</b>) WO<sub>3</sub>; (<b>b</b>) MnWO<sub>4</sub>; (<b>c</b>) NiWO<sub>4</sub>.</p>
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<p>Heat released (Q<sub>ox</sub>) in the oxidation reactor as a function of reduction and oxidation temperatures (T<sub>red</sub> and T<sub>ox</sub>, respectively), parameterized for different mole amounts of (<b>a</b>) WO<sub>3</sub>; (<b>b</b>) MnWO<sub>4</sub>; (<b>c</b>) NiWO<sub>4</sub>.</p>
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<p>Net heat (Q<sub>CLR</sub>) as a function of reduction and oxidation temperatures (T<sub>red</sub> and T<sub>ox</sub>, respectively), parameterized for different mole amounts of (<b>a</b>) WO<sub>3</sub>; (<b>b</b>) MnWO<sub>4</sub>; (<b>c</b>) NiWO<sub>4</sub>.</p>
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<p>Chemical-looping reforming configuration. Stream 1: CH<sub>4</sub> (25 °C); stream 2: CH<sub>4</sub> (reduction temperature); stream 3: gaseous products from reduction reactor (reduction temperature); stream 4: depleted OC (reduction temperature); stream 5: regenerated OC (oxidation temperature); stream 6: air (25 °C); stream 7: air (oxidation temperature); stream 8: depleted air from oxidation reactor (oxidation temperature).</p>
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16 pages, 3069 KiB  
Article
MOF(CuBDC)-Microcantilever IR Spectroscopy for Methane Sensing with High Sensitivity and Selectivity
by Seungwan Seo, Seok Bin Kwon and Yangkyu Park
Chemosensors 2025, 13(1), 8; https://doi.org/10.3390/chemosensors13010008 - 3 Jan 2025
Viewed by 433
Abstract
Methane, a greenhouse gas with 21 times the global warming potential of carbon dioxide, is increasingly subject to stringent emission regulations, driving the demand for high-performance methane sensors. This study proposes a novel IR spectroscopy technique based on a CuBDC-integrated microcantilever (CuBDC-microcantilever IR [...] Read more.
Methane, a greenhouse gas with 21 times the global warming potential of carbon dioxide, is increasingly subject to stringent emission regulations, driving the demand for high-performance methane sensors. This study proposes a novel IR spectroscopy technique based on a CuBDC-integrated microcantilever (CuBDC-microcantilever IR spectroscopy) for CH4 sensing, offering exceptional sensitivity and selectivity. The metal-organic framework (MOF) CuBDC was synthesized on the microcantilever using a drop-and-dry method facilitated by an intense pulsed light technique. Characterization via scanning electron microscopy, X-ray diffraction, and Fourier transform infrared spectroscopy confirmed the successful formation of CuBDC on the microcantilever. The CuBDC-microcantilever IR spectroscopy demonstrated a significantly enhanced sensitivity, with a differential amplitude at the CH4 characteristic peak approximately 13 times higher than that of a conventional Si microcantilever. Moreover, the limit of detection was determined to be as low as 14.05 ppm. The clear separation of the CH4 characteristic peak from the water and acetone vapor peaks also emphasized the sensor’s high selectivity. These findings highlight the superior sensitivity and selectivity of the proposed sensor, positioning it as a promising platform for CH4 detection in industrial and environmental applications. Full article
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<p>Fabrication process of CuBDC microcantilever.</p>
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<p>Experimental setup for measuring IR spectra of CH<sub>4</sub>.</p>
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<p>SEM images illustrating the conversion process from Cu to Cu(OH)<sub>2</sub> over time: (<b>a</b>) 10 min, (<b>b</b>) 20 min, and (<b>c</b>) 30 min. Cu(OH)<sub>2</sub> nanowires with a larger surface area were formed after a 30 min conversion. The first and second rows show images taken at lower and higher magnifications, respectively. Only three of the eight microcantilevers are displayed. The images were taken from a top-view perspective. However, during the fixation of the microcantilever to the chip holder, it was not perfectly horizontal, which may give the appearance of an inclination angle.</p>
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<p>SEM images depicting the conversion process from Cu(OH)<sub>2</sub> to CuBDC as a function of flash repetition: (<b>a</b>) 0 repetitions, (<b>b</b>) 90 repetitions, (<b>c</b>) 120 repetitions, and (<b>d</b>) 160 repetitions. CuBDC crystals were formed after 160 repetitions.</p>
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<p>The XRD patterns comparing the fabricated CuBDC, Cu(OH)<sub>2</sub>, and Cu.</p>
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<p>The crystal structure of the CuBDC (CCDC-687690).</p>
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<p>A FTIR analysis of the fabricated CuBDC.</p>
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<p>SEM images of the fabricated CuBDC microcantilever: (<b>a</b>) overall view and (<b>b</b>) magnified view.</p>
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<p>A comparison of the IR responses between the CuBDC and Si microcantilevers at a CH<sub>4</sub> concentration of 240 ppm.</p>
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<p>IR spectrum of CH<sub>4</sub> gas and acetone vapor mixture.</p>
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<p>IR spectra of various CH<sub>4</sub> concentrations: 80, 160, and 240 ppm.</p>
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<p>The peak amplitudes in the IR spectra of the CuBDC microcantilever as a function of CH<sub>4</sub> concentration. The error bars indicate the standard deviation derived from three measurements.</p>
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14 pages, 280 KiB  
Review
A Comprehensive Review of the Usefulness of Prebiotics, Probiotics, and Postbiotics in the Diagnosis and Treatment of Small Intestine Bacterial Overgrowth
by Adrian Martyniak, Magdalena Wójcicka, Iwona Rogatko, Tomasz Piskorz and Przemysław J. Tomasik
Microorganisms 2025, 13(1), 57; https://doi.org/10.3390/microorganisms13010057 - 1 Jan 2025
Viewed by 468
Abstract
Small intestinal bacterial overgrowth (SIBO) is a disorder characterized by the excessive growth of bacteria in the small intestine. Bacterial overgrowth disrupts the bacterial balance and can lead to abdominal pain, weight loss, and gastrointestinal symptoms, including bloating, diarrhea, and malabsorption. SIBO is [...] Read more.
Small intestinal bacterial overgrowth (SIBO) is a disorder characterized by the excessive growth of bacteria in the small intestine. Bacterial overgrowth disrupts the bacterial balance and can lead to abdominal pain, weight loss, and gastrointestinal symptoms, including bloating, diarrhea, and malabsorption. SIBO is widespread in the population. There are two main methods for diagnosing SIBO: breath tests and bacterial culture. The most commonly used method is a breath test, which enables the division of SIBO into the following three types: hydrogen-dominant (H-SIBO), methane-dominant (CH4-SIBO), and hydrogen/methane-dominant (H/CH4-SIBO). This comprehensive review aims to present the current knowledge on the use of prebiotics, probiotics, and postbiotics in the context of SIBO. For this purpose, medical databases such as MEDLINE (PubMed) and Scopus were analyzed using specific keywords and their combinations. This review is based on research studies no older than 10 years old and those using only human models. In summary, clinical studies have shown that the efficacy of SIBO therapy can be increased by combining antibiotics with probiotics, especially in vulnerable patients such as children and pregnant women. The further development of diagnostic methods, such as point of care testing (POCT) and portable devices, and a better understanding of the mechanisms of biotics action are needed to treat SIBO more effectively and improve the quality of life of patients. Full article
(This article belongs to the Section Gut Microbiota)
18 pages, 3626 KiB  
Article
Effect of Organic Nitrogen Supply on the Kinetics and Quality of Anaerobic Digestion of Less Nitrogenous Substrates: Case of Anaerobic Co-Digestion (AcoD) of Cassava Effluent and Chicken Droppings as a Nitrogen Source
by Haro Kayaba, Nourou Abdel Anziph Sergel Khalid, Sandwidi Sayouba, Compaore Abdoulaye, Palm Sie Auguste, Sessouma Oumou, Ouedraogo Ibrahim Kourita, Sinon Souleymane, Tubreoumya Guy Christian, Bere Antoine, Daho Tizane and Sanogo Oumar
Fuels 2025, 6(1), 2; https://doi.org/10.3390/fuels6010002 - 30 Dec 2024
Viewed by 545
Abstract
This study aims to explore anaerobic co-digestion (AcoD) of cassava (EUM) and poultry (FP) effluents using one inoculum/substrate ratio (30%) and three EUM vs. FP substrate composition ratios (25:75, 50:50, and 75:25). The AcoD process was therefore designed for 20 L batch digesters, [...] Read more.
This study aims to explore anaerobic co-digestion (AcoD) of cassava (EUM) and poultry (FP) effluents using one inoculum/substrate ratio (30%) and three EUM vs. FP substrate composition ratios (25:75, 50:50, and 75:25). The AcoD process was therefore designed for 20 L batch digesters, under mesophilic conditions, with less than 5% total solids for 66 days. The results showed that EUMs were highly resistant to degradation, while FPs were the most easily degradable. Kinetic analysis indicated specific organic matter (MO) reduction rates of 0.28% per day for EUM and 0.76% per day for FP. EUM alone produced 45.47 mL/g MO, while the 50:50 substrate produced 1184.60 mL/g MOV. The main factors contributing to EUM inefficiency were the inability to tame acidic conditions and the accumulation of volatile fatty acids. AcoD produced 23 to 50 times more methane than EUM alone, 2 to 5 times more than FP alone, and 2 to 4 times more than inoculum. As a result, the AcoD of both types of waste had a qualitative and quantitative effect on biogas production. CH4 content increased from around 2 to 75%, depending on the amount of organic nitrogen added. The addition of nitrogen by AcoD, even under mesophilic conditions, improves the kinetics and quality of anaerobic digestion of low-nitrogen substrates. Its impact on thermophilic and psychrophilic conditions needs to be verified. Full article
(This article belongs to the Special Issue Biomass Conversion to Biofuels)
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<p>Schematic diagram of AD system: 1: cassava effluent; 2: chicken droppings; 3: 25%EUM + 75%FP; 4: 50%EUM + 50%FP; 5: 75% EUM + 25% FP and 6: inoculum.</p>
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<p>Daily methane production from various reactors.</p>
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<p>Kinetic evolution of anaerobic digestion from different reactors: (<b>a</b>): chicken droppings; (<b>b</b>): cassava effluent; (<b>c</b>): inoculum; (<b>d</b>): 75% EUM + 25% FP; (<b>e</b>): 50%EUM + 50%FP; and (<b>f</b>): 25%EUM + 75%FP.</p>
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<p>Kinetic evolution of anaerobic digestion from different reactors: (<b>a</b>): chicken droppings; (<b>b</b>): cassava effluent; (<b>c</b>): inoculum; (<b>d</b>): 75% EUM + 25% FP; (<b>e</b>): 50%EUM + 50%FP; and (<b>f</b>): 25%EUM + 75%FP.</p>
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<p>Kinetic evolution of anaerobic digestion from different reactors: (<b>a</b>): chicken droppings; (<b>b</b>): cassava effluent; (<b>c</b>): inoculum; (<b>d</b>): 75% EUM + 25% FP; (<b>e</b>): 50%EUM + 50%FP; and (<b>f</b>): 25%EUM + 75%FP.</p>
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<p>Cumulated methane production from various reactors.</p>
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<p>Effect of organic nitrogen addition on methane production in different reactors: FP: poultry effluents and EUM: cassava effluents.</p>
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20 pages, 3762 KiB  
Article
The Characteristics of Hydrodeoxygenation of Biomass Pyrolysis Oil over Alumina-Supported NiMo Catalysts
by Dong-Jin Seo, Jong Beom Lee, Yu-Jin Kim, Hye-Ryeong Cho, So-Yeon Kim, Ga-Eun Kim, Young-Duk Park, Geon-Hee Kim, Jung-Chul An, Kyeongseok Oh and Joo-Il Park
Catalysts 2025, 15(1), 6; https://doi.org/10.3390/catal15010006 - 24 Dec 2024
Viewed by 374
Abstract
The hydrodeoxygenation (HDO) of biomass pyrolysis oil (BPO) was evaluated in the presence of two commercial alumina-supported transition metal catalysts, NiMo/alumina-1 (NM1) and NiMo/alumina-2 (NM2). The study explored two characteristic aspects: how HDO reaction conditions affected the oxygen content, density, and boiling point [...] Read more.
The hydrodeoxygenation (HDO) of biomass pyrolysis oil (BPO) was evaluated in the presence of two commercial alumina-supported transition metal catalysts, NiMo/alumina-1 (NM1) and NiMo/alumina-2 (NM2). The study explored two characteristic aspects: how HDO reaction conditions affected the oxygen content, density, and boiling point distribution of BPO with varying temperature and HDO reaction time, and the roles of catalysts. Characterizations of HDO-treated oils included elemental analysis, GC-MS, SIMDIS, 13C NMR, and 1H NMR, and characterizations of catalysts included NH3-TPD, XRF, and TPO-MS analysis. The results show that both NM1 and NM2 catalysts removed oxygenated compounds effectively, which led to decreases in density and shifts toward higher boiling point distributions of BPO. Compared to the NM1 catalyst, NM2 had a higher acidity and enhanced HDO activity. The best HDO reaction performance was achieved in the presence of the NM2 catalyst at 300 °C. Furthermore, HDO reactions showed a significant amount of CO2, CH4, C2H6, and C3H8, which suggests that HDO reactions proceeded via a series of reactions of decarboxylation, water–gas shift, and methanation. In addition, hydrocarbon fraction tests suggested a favorable potential for the blending of HDO-treated biomass pyrolysis oil (HDO-BPO) with petroleum-derived fractions. Full article
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<p>NH<sub>3</sub>-TPD curves for NM1 and NM2 catalysts.</p>
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<p>Chemical analyses after HDO treatments in the presence of NM1 and NM2 catalysts using (<b>a</b>) <sup>1</sup>H NMR spectroscopy and (<b>b</b>) <sup>13</sup>C NMR spectroscopy.</p>
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<p>Physical properties of HDO-BPO; (<b>a</b>) density data and (<b>b</b>) boiling point distribution.</p>
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<p>Schematic illustration to show how hydrogen bonds of either smaller molecules or larger molecules affect density reduction.</p>
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<p>LCO solubility data of HDO-BPO.</p>
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<p>TPO profiles for released CO<sub>2</sub> of spent NM1 and spent NM2 (SA: strongly acidic carboxyls, WA: weakly acidic carboxyls, CA: carboxylic anhydrides, LD and LC: lactones).</p>
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<p>Possible pathways to produce 3-pentadecyl phenol: HDO reaction and hydrogenation of 3-methyl phenol, with ring-opening conversion of methylcyclohexane to alkyl compounds (a) methyl-phenol → 4-methylphenyl ester acetic acid, (b) 4-methylphenyl ester acetic acid → 3-pentadecyl-phenol, (c) methyl-phenol → 3-pentadecyl-phenol.</p>
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13 pages, 4207 KiB  
Proceeding Paper
Methane Dynamics in Inner Mongolia: Unveiling Spatial and Temporal Variations and Driving Factors
by Sirui Yan, Yichun Xie, Ge Han, Xiaoliang Meng and Ziwei Li
Proceedings 2024, 110(1), 29; https://doi.org/10.3390/proceedings2024110029 - 23 Dec 2024
Viewed by 279
Abstract
Methane (CH4), the second-largest greenhouse gas contributing to global warming, has a high warming potential despite its short atmospheric lifespan. Inner Mongolia, due to its high carbon and energy consumption industries, faces significant methane emission challenges. This study uses TROPOMI satellite [...] Read more.
Methane (CH4), the second-largest greenhouse gas contributing to global warming, has a high warming potential despite its short atmospheric lifespan. Inner Mongolia, due to its high carbon and energy consumption industries, faces significant methane emission challenges. This study uses TROPOMI satellite data (February 2019 to December 2022) to analyze the long-term trends and spatial distribution of methane in Inner Mongolia. The results indicate significant spatial heterogeneity in the methane concentration distribution in Inner Mongolia, China. Higher methane concentrations are observed in the southeastern regions, whereas the central regions exhibit relatively lower concentrations. Temporally, the methane concentrations show an increasing trend with seasonal peaks from late August to early September. Using multiple stepwise regression and geographically weighted regression (GWR) methods, the study identifies the key factors influencing methane concentrations. Increased precipitation and soil temperature, along with intensified human activity, contribute to higher methane levels, while rising surface temperatures and increased vegetation suppress methane concentrations. The GWR model provides a better fit compared to the traditional methods, especially in regions with higher methane levels. This research offers insights for developing strategies to mitigate methane emissions and supports China’s emission control targets. Full article
(This article belongs to the Proceedings of The 31st International Conference on Geoinformatics)
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<p>Study area: Inner Mongolia.</p>
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<p>Cold and hot spots (based on average methane data from February 2019 to December 2022).</p>
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<p>Annual average distributions of CH<sub>4</sub> from 2019 to 2022. (<b>a</b>) Includes the annual average distribution of CH<sub>4</sub> in 2019; (<b>b</b>) includes the annual average distribution of CH<sub>4</sub> in 2020; (<b>c</b>) includes the annual average distribution of CH<sub>4</sub> in 2021; (<b>d</b>) includes the annual average distribution of CH<sub>4</sub> in 2022.</p>
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<p>Monthly average temporal variations regarding CH4 from 2019 to 2022.</p>
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<p>Monthly average temporal variations regarding CH<sub>4</sub> in (<b>a</b>) 2019, (<b>b</b>) 2020, (<b>c</b>) 2021, and (<b>d</b>) 2022.</p>
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<p>R<sup>2</sup> distributions of GWR model in (<b>a</b>) 2019, (<b>b</b>) 2020, (<b>c</b>) 2021, and (<b>d</b>) 2022.</p>
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16 pages, 4967 KiB  
Article
Effects of Solid Dairy Manure Application on Greenhouse Gas Emissions and Corn Yield in the Upper Midwest, USA
by Eric Young and Jessica Sherman
Sustainability 2024, 16(24), 11171; https://doi.org/10.3390/su162411171 - 20 Dec 2024
Viewed by 414
Abstract
Dairy manure is an important nitrogen (N) source for crops, but its role in greenhouse gas (GHG) emissions and farm sustainability is not fully understood. We evaluated the effects of application of two dairy manure sources (bedded pack heifer, BP, and separated dairy [...] Read more.
Dairy manure is an important nitrogen (N) source for crops, but its role in greenhouse gas (GHG) emissions and farm sustainability is not fully understood. We evaluated the effects of application of two dairy manure sources (bedded pack heifer, BP, and separated dairy solids, SDS) on corn silage yield and GHG emissions (carbon dioxide, CO2; methane, CH4; nitrous oxide, N2O) compared to a urea-fertilizer-only control (80 kg N ha−1 yr−1). The BP and SDS were applied at 18.4 and 19.4 Mg dry matter ha−1 in fall 2020 in the final year of ryegrass production. No-till corn was planted from 2021 to 2023, and GHG emissions were measured each season (from May to November). The results showed significantly greater CO2-C emissions for BP in 2021 and no differences in 2022 or 2023. A small N2O-N emission increase for BP occurred in the spring after application; however, seasonal fluxes were low or negative. Mean CH4-C emissions ranged from 2 to 7 kg ha−1 yr−1 with no treatment differences. Lack of soil aeration appeared to be an important factor affecting seasonal N2O-N and CH4-C emissions. The results suggest that GHG models should account for field-level nutrient management factors in addition to soil aeration status. Full article
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<p>Select soil properties from samples taken in fall 2020 before manure application. Average soil pH (<b>a</b>), Bray-1 extractable phosphorus (<b>b</b>), Bray 1 extractable potassium (<b>c</b>), soil organic matter content (<b>d</b>), total nitrogen (<b>e</b>), and total carbon (<b>f</b>) at three depth intervals (0–5 cm, 5–15 cm, and 15–30 cm). Error bars are one standard error of the mean of four replicated plots. Note: These were baseline soil analyses prior to manure application to assess consistency. Dairy heifer bedded pack manure designated plots = BP; separated dairy manure solids designated plots = SDS; control (fertilizer only) = CON.</p>
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<p>Dry matter solids and nutrients applied from dairy heifer bedded pack manure (BP) and separated dairy manure solids (SDS). Solids = manure solids (Mg ha<sup>−1</sup>); N = total nitrogen (kg ha<sup>−1</sup>); P = total phosphorus (kg ha<sup>−1</sup>); K = potassium (kg ha<sup>−1</sup>); S = sulfur (kg ha<sup>−1</sup>); Amm-N = ammonium-nitrogen (kg ha<sup>−1</sup>).</p>
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<p>Corn silage dry matter yield in 2021, 2022, and 2023 along with mean soil inorganic nitrogen concentration (ammonium-N + nitrate-N) taken from control plots when corn was approximately at the V5 growth stage. Means with different lowercase letters differ at <span class="html-italic">p</span> ≤ 0.05. Dairy heifer bedded pack manure = BP; separated dairy manure solids = SDS; control (fertilizer only) = CON.</p>
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<p>Mean carbon dioxide–carbon (CO<sub>2</sub>-C) emissions for each study year. Means notated with different lowercase letters for an event differ <span class="html-italic">(p</span> ≤ 0.05). Means without letters do not differ (<span class="html-italic">p</span> &gt; 0.05). Error bars are one standard error of the mean of four replicated plots. Dairy heifer bedded pack manure = BP; separated dairy manure solids = SDS; control (fertilizer only) = CON. FTIR breakdown denotes the time during which the FTIR instrument was under repair, and field measurements were not taken.</p>
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<p>Cumulative carbon dioxide–carbon (CO<sub>2</sub>-C) (<b>a</b>), nitrous oxide–nitrogen (N<sub>2</sub>O-N) (<b>b</b>), and methane–carbon (CH<sub>4</sub>-C) (<b>c</b>) emissions in each study year. Means notated with different lowercase letters differ (<span class="html-italic">p</span> ≤ 0.05). Error bars are one standard error of the mean of four replicated plots. Dairy heifer bedded pack manure = BP; separated dairy manure solids = SDS; control (fertilizer only) = CON.</p>
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<p>Mean nitrous oxide–nitrogen (N<sub>2</sub>O-N) and methane–carbon (CH<sub>4</sub>-C) emissions for 2020 and 2021. Means notated with different lowercase letters for an event differ (<span class="html-italic">p</span> ≤ 0.05). Means without letters do not differ (<span class="html-italic">p</span> &gt; 0.05). Error bars are one standard error of the mean of four replicated plots. Dairy heifer bedded pack manure = BP; separated dairy manure solids = SDS; control (fertilizer only) = CON. FTIR breakdown denotes the time during which the FTIR instrument was under repair, and field measurements were not taken.</p>
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<p>Mean nitrous oxide–nitrogen (N<sub>2</sub>O-N) and methane–carbon (CH<sub>4</sub>-C) emissions for 2022 and 2023. Means notated with different lowercase letters for an event differ (<span class="html-italic">p</span> ≤ 0.05). Means without letters do not differ (<span class="html-italic">p</span> &gt; 0.05). Error bars are one standard error of the mean of four replicated plots. Dairy heifer bedded pack manure = BP; separated dairy manure solids = SDS; control (fertilizer only) = CON.</p>
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<p>Changes in plot soil moisture content and temperature for 2020 and 2021. Means notated with different lowercase letters for an event differ (<span class="html-italic">p</span> ≤ 0.05). Means without letters do not differ (<span class="html-italic">p</span> &gt; 0.05). Error bars are one standard error of the mean of four replicated plots. Dairy heifer bedded pack manure = BP; separated dairy manure solids = SDS; control (fertilizer only) = CON. FTIR breakdown denotes the time during which the FTIR instrument was under repair, and field measurements were not taken.</p>
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<p>Changes in plot soil moisture content and temperature for 2020 and 2021. Means notated with different lowercase letters for an event differ (<span class="html-italic">p</span> ≤ 0.05). Means without letters do not differ (<span class="html-italic">p</span> &gt; 0.05). Error bars are one standard error of the mean of four replicated plots. Dairy heifer bedded pack manure = BP; separated dairy manure solids = SDS; control (fertilizer only) = CON.</p>
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<p>Daily total precipitation and temperature at the study site for 2020–2023.</p>
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19 pages, 3765 KiB  
Article
Integrating Satellite Observations and Hydrological Models to Unravel Large TROPOMI Methane Emissions in South Sudan Wetlands
by Yousef A. Y. Albuhaisi, Ype van der Velde, Sudhanshu Pandey and Sander Houweling
Remote Sens. 2024, 16(24), 4744; https://doi.org/10.3390/rs16244744 - 19 Dec 2024
Viewed by 383
Abstract
This study presents a comprehensive investigation of Methane (CH4) emissions in the wetlands of South Sudan, employing an integrated approach that combines TROPOMI satellite data, river altimetry, and hydrological model outputs. TROPOMI data show a strong increase in CH4 concentrations [...] Read more.
This study presents a comprehensive investigation of Methane (CH4) emissions in the wetlands of South Sudan, employing an integrated approach that combines TROPOMI satellite data, river altimetry, and hydrological model outputs. TROPOMI data show a strong increase in CH4 concentrations over the Sudd wetlands from 2018 to 2022. We quantify CH4 emissions using these data. We find a twofold emission increase from 2018 to 2019 (9.2 ± 2.4 Tg yr−1) to 2020 to 2022 (16.3 ± 3.3 Tg yr−1). River altimetry data analysis elucidates the interconnected dynamics of river systems and CH4 emissions. We identify correlations and temporal alignments across South Sudan wetlands catchments. Our findings indicate a clear signature of ENSO driving the wetland dynamics and CH4 emissions in the Sudd by altering precipitation patterns, hydrology, and temperature, leading to variations in anaerobic conditions conducive to CH4 production. Significant correlations are found between CH4 emissions and PCR-GLOBWB-simulated soil moisture dynamics, groundwater recharge, and surface water parameters within specific catchments, underscoring the importance of these parameters on the catchment scale. Lagged correlations were found between hydrological parameters and CH4 emissions, particularly with PCR-GLOBWB-simulated capillary rise. These correlations shed light on the temporal dynamics of this poorly studied and quantified source of CH4. Our findings contribute to the current knowledge of wetland CH4 emissions and highlight the urgency of addressing the complex interplay between hydrology and carbon dynamics in these ecosystems that play a critical role in the global CH4 budget. Full article
(This article belongs to the Section Environmental Remote Sensing)
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<p>(<b>a</b>) South Sudan study region. Data layers from Stamen Terrain-USA/OSM and OpenStreetMap Humanitarian Data Model. (<b>b</b>) South Sudan river streams within South Sudan borders. The red dots are Hydroweb river altimetry measurement points used in the study.</p>
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<p>The TROPOMI XCH<sub>4</sub> annual average enhancement over the South Sudan Wetlands Region (SSWR) from 2018 to 2022 at 0.1° × 0.1° resolution. The SSWR, indicated by the black rectangle (latitude 5–10° N and longitude 28–34.5° E), encompasses the area of interest.</p>
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<p>Monthly CH<sub>4</sub> emissions for the South Sudan Wetlands Region (SSWR) derived from TROPOMI data from 2018 to 2022. The Y-axis represents CH<sub>4</sub> emissions anomalies in Tg CH<sub>4</sub>. The anomalies in CH<sub>4</sub> emissions are calculated as the deviation from the multi-year monthly mean, standardized by dividing the difference by the standard deviation (SD) of the observed values for that particular month across all years. Positive values indicate higher-than-average emissions, while negative values represent lower-than-average emissions.</p>
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<p>The TROPOMI-inferred CH<sub>4</sub> enhancements (black), river altimetry (pink), GRACE equivalent water thickness (green), and ENSO index (blue and red) for the South Sudan Wetland Region (SSWR) highlighting the interconnected dynamics. The anomalies in CH<sub>4</sub> emissions are calculated as the deviation from the multi-year monthly mean, standardized by dividing the difference by the standard deviation (SD) of the observed values for that particular month across all years. Positive values indicate higher-than-average emissions, while negative values represent lower-than-average emissions.</p>
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<p>(<b>a</b>) South Sudan Wetlands Region (SSWR) catchments, and (<b>b</b>) TROPOMI CH<sub>4</sub> emission in comparison to river altimetry measurements per catchment.</p>
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<p>Correlation between PCR-GLOBWB sub-surface parameters anomalies and TROPOMI CH₄ emission anomalies.</p>
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<p>Correlation between PCR-GLOBWB surface parameters anomalies and TROPOMI CH₄ emission anomalies.</p>
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<p>Correlation between PCR-GLOBWB river parameters anomalies and TROPOMI CH₄ emission anomalies.</p>
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<p>Comparison of normalized TROPOMI CH₄ emissions from the SSWR (black) alongside normalized water levels of Lake Victoria (green). The bars indicate the phases of the El Niño Southern Oscillation (ENSO), with El Niño events represented in blue and La Niña events in red, highlighting potential correlations between climatic phenomena and CH₄ emissions.</p>
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15 pages, 3355 KiB  
Article
Methane Emission of Italian Mediterranean Buffaloes Measured Using a Laser Detector During a Lactation Cycle
by David Meo Zilio, Miriam Iacurto, Francesco Cenci and Roberto Steri
Animals 2024, 14(24), 3652; https://doi.org/10.3390/ani14243652 - 18 Dec 2024
Viewed by 404
Abstract
In Italy, the number of farmed dairy buffaloes rose up to approximately 436,000 heads in 2023 (+22% in the last 15 years), a fourfold increase compared to the 1980s, due to the growing market interest in mozzarella cheese. The increased demand for mozzarella [...] Read more.
In Italy, the number of farmed dairy buffaloes rose up to approximately 436,000 heads in 2023 (+22% in the last 15 years), a fourfold increase compared to the 1980s, due to the growing market interest in mozzarella cheese. The increased demand for mozzarella cheese, in turn, requires higher production, which can result in increased methane emission from the sector. Therefore, it is necessary to establish mitigation and selection schemes for low-emission strategies. The current study aimed to highlight sources of variation in methane emission from lactating Italian Mediterranean buffaloes measured using a laser methane detector in order to identify practical and methodological aspects to consider when designing experiments focused on methane emission evaluation. Methane (CH4), exhaled from 60 cows, was recorded twice a day during milking, over two weeks per month for at least three months throughout a whole lactation cycle. The animal (individual), days in milk, parity, month, operator, milking entry order, and milking session effects were significant for methane emission (p < 0.0001). Our results showed that laser methane detector may be used as a rapid tool for methane emission studies and highlighted which factors can account for individual measures. This instrument is easy to use, fast, versatile, and not too expensive. These characteristics make it suitable for large-scale herd screening and monitoring. Full article
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<p>Daily exhalation profile during the morning (1–120 s) and afternoon (121–240 s) milking session.</p>
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<p>Distribution of CH<sub>4</sub> emissions expressed as ppm*m (<b>a</b>) or as LogCH<sub>4</sub> (<b>b</b>) <span class="html-italic">vs</span> normal distribution (blue line).</p>
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<p>Estimated least means squares for DIM class (filled dots) and second-degree polynomial fit (dashed line).</p>
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<p>Fourth degree polynomial (dashed line) fitted on least square means (filled dots) for the fixed factor DIM.</p>
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<p>Distribution of solutions for the random effect ID in methane emission.</p>
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<p>Estimated least means squares for parity on methane emission.</p>
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<p>Parity × DIM interaction.</p>
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<p>Monthly methane emission within the observed period.</p>
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<p>Variation in methane emission according to the milking post and milking line.</p>
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24 pages, 2793 KiB  
Article
CO2-Assisted Oxidative Dehydrogenation of Propane to Propylene over Modified SiO2 Based Catalysts
by Alexandra Florou, Aliki Kokka, Georgios Bampos and Paraskevi Panagiotopoulou
Catalysts 2024, 14(12), 933; https://doi.org/10.3390/catal14120933 - 18 Dec 2024
Viewed by 529
Abstract
The oxidative dehydrogenation of propane with CO2 (CO2-ODP) was investigated over different metal oxides MxOy (M: Ca, Sn, Cr, Ga) supported on a SiO2 surface. Catalysts were characterized employing nitrogen adsorption/desorption, X-ray diffraction (XRD), CO2 [...] Read more.
The oxidative dehydrogenation of propane with CO2 (CO2-ODP) was investigated over different metal oxides MxOy (M: Ca, Sn, Cr, Ga) supported on a SiO2 surface. Catalysts were characterized employing nitrogen adsorption/desorption, X-ray diffraction (XRD), CO2 temperature programmed desorption (CO2-TPD) and pyridine adsorption/desorption experiments in order to identify their physicochemical properties and correlate them with their activity and selectivity for the CO2-ODP reaction. The effect of operating reaction conditions on catalytic performance was also examined, aiming to improve the propylene yield and suppress side reactions. Surface acidity and basicity were found to be affected by the nature of MxOy, which in turn affected the conversion of propane to propylene, which was in all cases higher compared to that of bare SiO2. Propane conversion, reaction rate and selectivities towards propylene and carbon monoxide were maximized for the Ga- and Cr-containing catalysts characterized by moderate surface basicity, which were also able to limit the undesired reactions leading to ethylene and methane byproducts. High surface acidity was found to be beneficial for the CO2-ODP reaction, which, however, should not be excessive to ensure high catalytic activity. The silica-supported Ga2O3 catalyst exhibited sufficient stability with time and better than that of the most active Cr2O3-SiO2 catalyst. Decreasing the weight gas hourly space velocity resulted in a significant improvement in both propane conversion and propylene yield as well as a suppression of undesired product formation. Increasing CO2 concentration in the feed did not practically affect propane conversion, while led to a decrease in propylene yield. The ratio of propylene to ethylene selectivity was optimized for CO2:C3H8 = 5:1 and space velocity of 6000 mL g−1 h−1, most possibly due to facilitation of the C–H bond cleavage against that of the C–C bond. Results of the present study provided evidence that the efficient conversion of propane to propylene is feasible over silica-based composite metal oxides, provided that catalyst characteristics have been optimized and reaction conditions have been properly selected. Full article
(This article belongs to the Special Issue Feature Papers in "Industrial Catalysis" Section, 2nd Edition)
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Graphical abstract
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<p>CO<sub>2</sub>-TPD profiles obtained from SiO<sub>2</sub>-based catalysts. Experimental conditions: mass of catalyst: 0.15 g; heating rate <span class="html-italic">β</span> = 10 °C /min; total flow = 40 cm<sup>3</sup> min<sup>−1</sup>.</p>
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<p>DRIFT spectra obtained from (<b>a</b>) SiO<sub>2</sub>, (<b>b</b>) 10%Cr<sub>2</sub>O<sub>3</sub>-SiO<sub>2</sub> and (<b>c</b>) 10%Ga<sub>2</sub>O<sub>3</sub>-SiO<sub>2</sub> catalysts following adsorption of pyridine at 25 °C for 120 min and subsequent stepwise heating at the indicated temperatures under He flow (a: 25 °C; b: 100 °C; c: 150 °C; d: 200 °C; e: 250 °C; f: 300 °C; g: 350 °C; h: 400 °C; i: 450 °C).</p>
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<p>Effect of reaction temperature on the (<b>a</b>) conversion of propane and (<b>b</b>) propylene yield obtained over SiO<sub>2</sub> and 10%M<sub>x</sub>O<sub>y</sub>-SiO<sub>2</sub> catalysts. Experimental conditions: particle diameter: 0.15 &lt; d<sub>p</sub> &lt; 0.25 mm; CO<sub>2</sub>:C<sub>3</sub>H<sub>8</sub> = 5:1; WGHSV = 6000 h<sup>−1</sup>.</p>
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<p>Selectivities towards reaction products as a function of reaction temperature obtained over (<b>a</b>) SiO<sub>2</sub>, (<b>b</b>) 10%CaO-SiO<sub>2</sub>, (<b>c</b>) 10%SnO<sub>2</sub>-SiO<sub>2</sub>, (<b>d</b>) 10%Ga<sub>2</sub>O<sub>3</sub>-SiO<sub>2</sub> and (<b>e</b>) 10%Cr<sub>2</sub>O<sub>3</sub>-SiO<sub>2</sub> catalysts. Experimental conditions: same as in <a href="#catalysts-14-00933-f003" class="html-fig">Figure 3</a>.</p>
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<p>Propane conversion, reaction rate and product selectivities at 610 °C as a function of the total amount of desorbed CO<sub>2</sub> during CO<sub>2</sub>-TPD experiments for SiO<sub>2</sub> and 10%M<sub>x</sub>O<sub>y</sub>-SiO<sub>2</sub> catalysts.</p>
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<p>Effect of reaction time on the conversions of C<sub>3</sub>H<sub>8</sub> and CO<sub>2</sub> (solid symbols), and selectivity towards C<sub>3</sub>H<sub>6</sub> (open symbols) at 660 °C over 10%Cr<sub>2</sub>O<sub>3</sub>-SiO<sub>2</sub> and 10%Ga<sub>2</sub>O<sub>3</sub>-SiO<sub>2</sub> catalysts. Experimental conditions: same as in <a href="#catalysts-14-00933-f003" class="html-fig">Figure 3</a>.</p>
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<p>Effect of WGHSV on the (<b>a</b>) conversion of C<sub>3</sub>H<sub>8</sub> and (<b>b</b>) yield of C<sub>3</sub>H<sub>6</sub> over 10%Ga<sub>2</sub>O<sub>3</sub>-SiO<sub>2</sub> catalyst using a CO<sub>2</sub>:C<sub>3</sub>H<sub>8</sub> = 5:1.</p>
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<p>Effect of WGHSV on the selectivities towards reaction products over 10%Ga<sub>2</sub>O<sub>3</sub>-SiO<sub>2</sub> catalyst at (<b>a</b>) 600 and (<b>b</b>) 700 °C using a CO<sub>2</sub>:C<sub>3</sub>H<sub>8</sub> = 5:1.</p>
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<p>Effect of CO<sub>2</sub>:C<sub>3</sub>H<sub>8</sub> molar ratio on the (<b>a</b>) conversion of C<sub>3</sub>H<sub>8</sub> and (<b>b</b>) yield of C<sub>3</sub>H<sub>6</sub> over 10%Ga<sub>2</sub>O<sub>3</sub>-SiO<sub>2</sub> catalyst using a WGHSV = 6000 h<sup>−1</sup>.</p>
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<p>Effect of CO<sub>2</sub>:C<sub>3</sub>H<sub>8</sub> molar ratio on the selectivities towards reaction products over 10%Ga<sub>2</sub>O<sub>3</sub>-SiO<sub>2</sub> catalyst at (<b>a</b>) 600 and (<b>b</b>) 700 °C using a WGHSV = 6000 h<sup>−1</sup>.</p>
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16 pages, 3229 KiB  
Article
Analysis of CH4 and N2O Fluxes in the Dry Season: Influence of Soils and Vegetation Types in the Pantanal
by Gabriela Cugler, Viviane Figueiredo, Vincent Gauci, Tainá Stauffer, Roberta Bittencourt Peixoto, Sunitha Rao Pangala and Alex Enrich-Prast
Forests 2024, 15(12), 2224; https://doi.org/10.3390/f15122224 - 17 Dec 2024
Viewed by 447
Abstract
This study examines CH4 and N2O fluxes during the dry season in two distinct areas of the Pantanal: Barranco Alto Farm (BAF), dominated by grasslands, and Passo da Lontra (PL), a forested region. As climate change increases the occurrence of [...] Read more.
This study examines CH4 and N2O fluxes during the dry season in two distinct areas of the Pantanal: Barranco Alto Farm (BAF), dominated by grasslands, and Passo da Lontra (PL), a forested region. As climate change increases the occurrence of droughts, understanding greenhouse gas (GHG) fluxes in tropical wetlands during dry periods is crucial. Using static chambers, CH4 and N2O emissions were measured from soils and tree stems in both regions, with additional measurements from grass in BAF. Contrary to expectations, PL—characterized by clayey soils—had sandy mud samples that retained less water, promoting oxic conditions and methane uptake, making it a CH4 sink. Meanwhile, BAF’s sandy, well-drained soils exhibited minimal CH4 fluxes, with negligible methane uptake or emissions. N2O fluxes were generally higher in BAF, particularly from tree stems, indicating significant interactions between soil type, moisture, and vegetation. These findings highlight the pivotal roles of soil texture and aeration in GHG emissions, suggesting that well-drained, sandy soils in tropical wetlands may not always enhance methane oxidation. This underscores the importance of continuous GHG monitoring in the Pantanal to refine climate change mitigation strategies. Full article
(This article belongs to the Section Forest Meteorology and Climate Change)
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<p>Location of Pantanal sampling sites. (<b>A</b>) The top-left map highlights the Pantanal biome (grey) within Brazil’s borders. (<b>B</b>) A zoomed-in view of the southern Pantanal shows the sampling sites: Passo da Lontra (PL, triangle) in the Miranda microregion and Barranco Alto Farm (BAF, circle) in the Aquidauana microregion. (<b>C</b>) A detailed map of the PL site near Medalha Lake and the Rio Miranda. (<b>D</b>) A detailed map of the BAF site near the Aquidauana River, with surrounding water bodies in light blue. The shapefiles for the Pantanal boundaries and hydrography were obtained from Terrabrasilis (INPE, 2023), while the map of Brazil and municipality boundaries were sourced from IBGE (2023).</p>
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<p>Photographs showing the chambers used to measure CH<sub>4</sub> and N<sub>2</sub>O fluxes at Fazenda Barranco Alto and Passo do Lontra. Opaque PVC static chambers were used for measuring fluxes from grass and soil, while semi-rigid static chambers were used for tree stem measurements. Source: Photographs are taken by collaborators Pernilha Eriksson and Louise Larsson and are the property of the research group.</p>
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<p>Box plot illustrating CH<sub>4</sub> fluxes (µg C-CH<sub>4</sub> m<sup>−2</sup> d<sup>−1</sup>) measured across compartments and sites. The BAF site includes CH<sub>4</sub> emissions from tree stems, soil, and grass, while the PL site includes emissions from tree stems and soil. Each box represents the interquartile range (IQR), with whiskers extending to the minimum and maximum values, including the outliers (black circles). The Kolmogorov–Smirnov test indicates that the distribution of all data is nonparametric (<span class="html-italic">p</span> &lt; 0.05). Different letters represent statistically significant differences according to Kruskal–Wallis test with Dunn’s post hoc test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Box plot illustrating N<sub>2</sub>O fluxes (µg N-N<sub>2</sub>O m<sup>−2</sup> d<sup>−1</sup>) measured from tree steams, exposed soil, and grasses across PL and BAF study sites. The BAF site includes N<sub>2</sub>O emissions from tree stems, soil, and grass, while the PL site includes emissions from tree stems and soil. Each box represents the interquartile range (IQR), with whiskers extending to the minimum and maximum values, including the outliers (black circles). Letter (a) above the boxes represents no statistically significant differences according to the Kruskal–Wallis test with Dunn’s post hoc test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Boxplots illustrating CH<sub>4</sub> and N<sub>2</sub>O fluxes at Passo da Lontra (PL) and Barranco Alto Farm (BAF) across three areas (Area 1: closest to the lake, Area 3: furthest). Panels (<b>A</b>,<b>B</b>) depict CH<sub>4</sub> fluxes, while panels (<b>C</b>,<b>D</b>) show N<sub>2</sub>O fluxes. Each box represents the interquartile range (IQR), with whiskers extending to minimum and maximum non-outlier values. The letter (a) above the boxes represents no statistically significant differences according to the Kruskal–Wallis test with Dunn’s post hoc test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Relationship between tree stem diameter (cm) and GHG fluxes. (<b>A</b>) Represent the CH<sub>4</sub> (μg CH<sub>4</sub> m<sup>−2</sup> d<sup>−1</sup>). (<b>B</b>) Represent the N<sub>2</sub>O (μg N<sub>2</sub>O m<sup>−2</sup> d<sup>−1</sup>). The blue lines show the non-parametric trend (LOWESS) of fluxes with increasing stem diameter.</p>
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18 pages, 1899 KiB  
Review
Methane Production Mechanism and Control Strategies for Sewers: A Critical Review
by Feng Hou, Shuai Liu, Wan-Xin Yin, Li-Li Gan, Hong-Tao Pang, Jia-Qiang Lv, Ying Liu, Ai-Jie Wang and Hong-Cheng Wang
Water 2024, 16(24), 3618; https://doi.org/10.3390/w16243618 - 16 Dec 2024
Viewed by 605
Abstract
Methane (CH4) emissions from urban sewer systems represent a significant contributor to greenhouse gases, driven by anaerobic decomposition processes. This review elucidates the mechanisms underlying CH4 production in sewers, which are influenced by environmental factors such as the COD/SO4 [...] Read more.
Methane (CH4) emissions from urban sewer systems represent a significant contributor to greenhouse gases, driven by anaerobic decomposition processes. This review elucidates the mechanisms underlying CH4 production in sewers, which are influenced by environmental factors such as the COD/SO42− ratio, temperature, dissolved oxygen, pH, flow rate, and hydraulic retention time. We critically evaluated the effectiveness of empirical, mechanistic, and machine learning (ML) models in predicting CH4 emissions, highlighting the limitations of each. This review further examines control strategies, including oxygen injection, iron salt dosing, and nitrate application, emphasizing the importance of balancing CH4 reduction with the operational efficiency of wastewater treatment plants (WWTPs). An integrated approach combining mechanistic and data-driven models is advocated to enhance prediction accuracy and optimize CH4 management across urban sewer systems. Full article
(This article belongs to the Section Urban Water Management)
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<p>Biochemical reactions in sewer systems.</p>
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<p>Study on factors affecting methane production in sewers. (<b>a</b>) Statistical correlations between both sewer CH<sub>4</sub> and CO<sub>2</sub> emissions and overlying sewage COD in a field survey [<a href="#B24-water-16-03618" class="html-bibr">24</a>]. (<b>b</b>) Comparison of CH<sub>4</sub> emissions at the end of sewage pipe networks between summer and winter [<a href="#B27-water-16-03618" class="html-bibr">27</a>]. (<b>c</b>) Changes in dissolved oxygen in biofilms with biofilm thickness, COD = 400 mg/L [<a href="#B28-water-16-03618" class="html-bibr">28</a>]. (<b>d</b>) H<sub>2</sub>S production, CH<sub>4</sub> production, and cell viability, where sewer biofilms were subjected to pH levels of 10.5, 11.5, and 12.5, respectively, for 6 hb [<a href="#B29-water-16-03618" class="html-bibr">29</a>]. (<b>e</b>) Variation in biomass density at different wall shear stresses; COD = 400 mg/L (F means wall shear stress, Pa) [<a href="#B28-water-16-03618" class="html-bibr">28</a>].</p>
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<p>Sewage network-related model. (<b>a</b>) Model defects. (<b>b</b>) Gas generation mechanism [<a href="#B9-water-16-03618" class="html-bibr">9</a>,<a href="#B53-water-16-03618" class="html-bibr">53</a>]. (<b>c</b>) Organic matter transformation in a sewage network [<a href="#B54-water-16-03618" class="html-bibr">54</a>]. (<b>d</b>) Machine learning model with different principles [<a href="#B7-water-16-03618" class="html-bibr">7</a>].</p>
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<p>Transformation of ferric salt in an urban water system [<a href="#B67-water-16-03618" class="html-bibr">67</a>].</p>
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