Greenhouse gases emissions and global climate change: Examining the influence of CO2, CH4, and N2O

Nanoporous carboxymethyl cellulose aerogels with enhanced thermal insulation and mechanical toughness

This study presents a facile method to fabricate nanoporous carboxymethyl cellulose (CMC) aerogels with exceptional thermal insulation properties through metal ion cross-linking and supercritical carbon dioxide (scCO₂) drying. The as-prepared aerogels exhibit remarkable characteristics, including low density (as low as 0.15 g cm-3), high specific surface area (up to 303 m2 g-1), high compression strength (stress of 17.31 MPa at 80 % strain), and low thermal conductivity (as low as 21.7 mW (m K)-1). The effects of varying CMC concentrations on the density, specific surface area, morphology, and thermal insulation properties of the aerogels were systematically investigated. This work highlights the potential for tailoring CMC aerogel properties by adjusting the initial CMC concentration, offering new opportunities for developing cost-effective thermal insulation materials.

Machine learning-based prediction of ambient CO2 and CH4 concentrations with high temporal resolution in Seoul metropolitan area

Machine learning has the potential to support the growing need for high-resolution greenhouse gas monitoring in urban and industrial environments, where deploying extensive sensor networks is often limited by cost and operational challenges. This study presents a novel approach for estimating greenhouse gas (GHG) concentrations using routinely collected air quality and meteorological data from existing monitoring stations. Focusing on the Seoul metropolitan area in the Republic of Korea, we developed and evaluated three machine learning models - Random Forest, Long Short-Term Memory (LSTM), and an ensemble learning approach - to predict CO2 and CH4 concentrations without relying on additional GHG monitoring equipment. Among these, the ensemble learning model outperformed the individual models, consistently achieving lower error metrics, even in data-limited scenarios. Feature importance analysis identifies NO2, CO, O3, and temperature as key predictors of CO2 and CH4 level variations, highlighting the influence of combustion-related pollutants and photochemical processes. Cross-validation results confirm the model's out-of-sample capabilities; however, local factors, such as traffic density, industrial activities, and meteorology, can affect performance. Consequently, model retraining or transfer learning may be required when applying the model to new locations with comparable emission profiles or atmospheric conditions. These findings emphasize the importance of localized context in model application while also demonstrating the broader applicability of the approach. By utilizing data already available through urban monitoring networks, this study offers a scalable and cost-effective strategy to support high-resolution GHG monitoring and inform targeted climate policies in complex urban-industrial regions.

Comprehensive Oxidation Mechanism of n-Butylamine and 2-Butylamine by H and OH Radicals: Insights into Reactivity

This study presents the accurate thermal rate constants for a series of hydrogen abstraction reactions involving 1- and 2-butylamine and key radicals H and OH. The potential energy surface resulting from these reactions was examined by using the M08-HX/ma-TZVP level of theory. The rate coefficients were calculated within the multistructural canonical variational theory with small-curvature tunneling correction (MS-CVT/SCT). Multistructural effects and the torsional anharmonicity corrections were evaluated through the rovibrational partition function calculated with the multistructural method based on a coupled torsional potential (MS-T). Our results demonstrated an influence of the position of the amino functional group on the kinetics. The gradual decrease in barrier heights was observed with increasing distance between the amino functional group and the reaction site. The calculated branching ratios demonstrated that the H-abstraction by the H radicals at the α-site is favored. In reactions involving OH radicals, the channel at the N-site shows a greater proportion due to its increased multistructural torsional anharmonicity and a reduced variational effect of other sites.

Recent progress of the electrocatalytic CO2 reduction reaction using porous materials

The electrocatalytic reduction of CO2 is widely recognized as a promising strategy to reduce carbon emissions. However, a huge gap remains between the current state of electrocatalytic CO2 reduction reaction (CO2RR) technology and its practical implementation at an industrial scale. Thus, there is growing interest in developing electrocatalysts that offer high activity, selectivity, and stability. Crystalline porous materials such as metal-organic frameworks (MOFs), covalent organic frameworks (COFs), and porous carbons have emerged as highly effective catalysts for electrocatalytic CO2RRs. Furthermore, advancements in nanoscale characterization and computational modeling have enabled a deeper understanding of the structure and activity relationships in these materials, highlighting how characteristics of the materials influence the selectivity, catalytic activity, and mass transport efficiency for the electrocatalytic CO2RR. In this review, we summarize the fundamentals of electrocatalytic CO2RRs, highlighting the role of porous materials such as MOFs, COFs, and porous carbon. We also discuss recent trends in the selective formation of different CO2RR products, including CO, HCOOH, CH4, and C2+ products. Key advancements in material design are presented along with challenges and future perspectives.

Spatiotemporal variations of precipitation and driving forces during wheat maturation season in Henan Province

Since entering the twenty-first century, global warming has continued to escalate, and the frequency of rainfall occurrence during the wheat maturity period has increased significantly, which has seriously threatened the yield of wheat. In this study, based on the rainfall data and a variety of remote correlation factors during the wheat maturation period in Henan Province from 2000 to 2022, we comprehensively explored the spatial and temporal characteristics of wheat maturation rain and its drivers in Henan Province by using the Pettitt method, the Morlet wavelet method, the center of gravity model, and cross-wavelets. The results show that: (1) the wheat maturation rain in Henan Province shows an upward trend, the mutation point is mainly concentrated in 2013, and the cycle change is characterized by a small scale; (2) The multi-year averages of total rainfall, maximum daily rainfall and rainfall intensity in the southern part of Henan Province during the wheat-yellow period were all the highest, and the number of rainfall days also showed a significant upward trend; (3) In 2013 wheat maturation rain in Henan Province, the center of gravity of daily rainfall was mainly concentrated in northwestern Henan Province, and the typical rainfall event mainly experienced five processes of occurrence-intensification-dissipation-re-intensification-dissipation; (4) Total rainfall, maximum daily rainfall and rainfall intensity had the highest correlation with sunspot, and overall sunspot had the greatest impact on wheat maturation rain. By analyzing the spatial and temporal characteristics of wheat maturation rain and further revealing its driving mechanism, it is of great significance to understand the ability of wheat to adapt to climate change and ensure food security.

Unravelling spatiotemporal heterogeneity of wildfire carbon dioxide emissions in Southeast Asia: based on a high-resolution inventory

Wildfire carbon dioxide emissions (FCE) profoundly impact the climate and environment. Land use, meteorology, and other factors contribute to the spatial heterogeneity of FCE. High-resolution emission inventories enhance our understanding of the spatiotemporal characteristics of emissions, providing critical data for climate change and environmental studies. In this study, we developed a 500-m resolution FCE inventory by integrating burned area and fire radiative energy methods. Our integrated method combines the strengths of both the burned area and fire radiative energy methods, enhancing the accuracy of FCE estimation. The results show that the method improves the overall FCE estimate by 70.2 % compared to that of the burned area method alone. Based on the high-resolution inventory and the spatiotemporal cube technique, we identified the spatiotemporal heterogeneity of FCE and high-resolution multi-modal hotspot distributions. These include the El Niño-driven intra-annual 'double peaks' observed in Southeast Asia (SEA) in 2015 and 2019 and the seasonal variability of emissions between Mainland and Equatorial SEA. Forest fires, concentrated in Myanmar and Laos, are the largest source of FCE in SEA. Meanwhile, sporadic hotspots dominate in SEA, reflecting intermittent meteorological and anthropogenic influences. Moreover, by combining this inventory with a database of carbon dioxide from fossil fuel consumption, we reveal the structural dynamics of carbon dioxide emissions, highlighting the critical role of FCE in achieving regional carbon neutrality. This study offers significant insights into the spatiotemporal dynamics of FCE and provides actionable pathways for mitigation strategies and sustainable wildfire management.

Influence of the calcination conditions of the support on the activity of ruthenium-encapsulated porous hollow silica sphere catalysts for hydrogenation of carbon dioxide into formic acid

The present study investigated the influence of the calcination conditions of porous hollow silica spheres on the activity of a ruthenium-encapsulated porous hollow silica sphere catalyst for hydrogenation of carbon dioxide into formic acid. The hollow spheres were prepared at various calcination temperatures in air or in an argon flow. The amount of residual carbon content in the ruthenium-encapsulated hollow silica sphere catalysts increased with a decrease in the calcination temperature of the hollow silica sphere supports in air. Energy dispersive X-ray spectroscopy (EDS) and thermogravimetric (TG) analyses revealed that cetyltrimethylammonium bromide (CTAB) preferentially decomposed at calcination temperatures of up to 673 K, and most of the CTAB and carbon templates decomposed with the collapse of the hollow sphere catalyst particles in the catalysts calcined at 873 K. Moreover, the highest amounts of residual CTAB and carbon templates were found in the catalysts calcined in the argon flow. Differential thermal analysis (DTA), transmission electron microscopy (TEM), nitrogen sorption and X-ray diffraction (XRD) measurements showed that active ruthenium species were highly dispersed in the hollow spheres calcined in air, while a small amount of active ruthenium species with low dispersion were supported on the hollow spheres calcined in the argon flow. The catalyst calcined at 473 K exhibited the highest turnover number (TON) for formic acid formation (350 mol-HCOOH per mol-Ru), suggesting that the catalysts exhibited high activity not only owing to the high dispersion of the active species but also owing to the effective conduction of reaction heat by residual carbon species originating from CTAB in the nanospaces of the hollow spheres' shells.

Controlled Doping Sites to Enhance Charge Transfer of ZnO for Ultrarapid Methane Sensing

Charge transfer in metal oxide semiconductor-based sensitive materials plays a crucial role in gas sensing. Doping engineering is an effective approach to optimize the electrical properties of oxides for enhanced sensing performance. However, the underlying mechanism by which doping sites affect gas sensing performance remains not well-known. In this study, Al-doped ZnO nanorods with controlled doping sites (interstitial- and substitutional-dominant) were successfully fabricated via a mild (80 °C) and facile (30 min) liquid-phase route for CH4 sensing. Direct atomic-scale observations showed that the doping Al site in ZnO shifted with increasing doping amount from interstitial-dominant to substitutional-dominant. The density functional theory results revealed that interstitially Al-doped ZnO realized more free ZnO electrons than pristine and substitutionally Al-doped ZnO, facilitating the generation of chemisorbed oxygen for CH4 activation and electron transfer during the sensing process and thus resulting in ultrarapid CH4 sensing with response and recovery times of 3.8 and 5.0 s, respectively.

Zero-discharge, self-sustained 3D-printed microbial electrolysis cell for biohydrogen production: a review

Microbial fuel cell (MFC) and microbial electrolysis cell (MEC) technologies have been used recently in bench-scale bioenergy (electricity) generation, biohydrogen (H2) production, biosensing, and wastewater treatment. There are still a lot of obstacles to overcome in terms of commercialization and industrial settling. These difficulties include lengthy start-up times, intricate reactor designs for managing large reaction volumes, and expensive and time-consuming large-scale system fabrication procedures. Interestingly, combining three-dimensional (3D) printing with MFC and MEC technology appears to be a workable and promising way to get past these obstacles. Moreover, a rapid start-up with no delays in the current generation using MFC and MEC is possible with 3D printed bio-anodes. Furthermore, H2 can be generated from wastewater by powering a stacked MFC and MEC-coupled with electrochemical capacitor (ECC) system using 3D printing technology. To the best of the author's knowledge, this review paper is the first to explicitly highlight the use of 3D printing in creating a stacked MFC-ECC-MEC system in conjunction with a photobioreactor (PBR) to produce significant quantities of H2 and carbon dioxide (CO2) can be utilized for algae production. A notable feature of 3D printing technology is its reliable production capabilities, enabling MFC-ECC-MEC-PBR systems to be expanded by setting up numerous stacks of MFC-ECC-MEC-PBR units devoid of material waste and human error. The present review attempts to provide an update on the current status of the 3D printing application, that is meant to propel the MFC-ECC-MEC-PBR system forward.

The double-edged sword effects of land use optimization based on dual carbon goals: A perspective from landscape ecological risk

Carbon neutrality has emerged as a global consensus in response to climate change, and land use/land cover (LULC) optimization is considered a crucial strategy for achieving this goal. In this context, China has proposed a dual carbon strategy of peaking emissions by 2030 and achieving neutrality by 2060, which provides a policy framework for LULC optimization. However, existing studies on dual carbon goal-oriented LULC optimization often overlook potential ecological risks while emphasizing benefits and fail to fully explore the possible "double-edged sword effect" of LULC optimization. To address this gap, a multi-scenario, multi-objective integrated LULC optimization model was developed in this study, considering both 2030 carbon peaking and 2060 carbon neutrality. Landscape ecological risk (LER) was introduced as an additional evaluation dimension. Furthermore, an integrated three-dimensional (LER, carbon emissions, and carbon storage) evaluation system was established to identify key areas in Shanxi Province to achieve dual carbon goals. The results show that, compared to the natural development (ND) scenario, the optimized scenarios better balance economic and ecological value, reduce carbon emissions, and increase carbon storage, with the carbon neutrality (CN) scenario performing the best. However, LULC optimization also leads to an increase in the LER, particularly in the CN scenario. Furthermore, through the three-dimensional evaluation system, positive and negative areas for achieving the dual carbon goals in Shanxi Province were identified, including the discovery of a "Negative Belt" in the region. The findings emphasize the necessity of integrating policy considerations in the design of multi-objective and multi-scenario frameworks for LULC optimization research and highlight the importance of considering the unique perspective of LER.

Electrocatalytic CO2 Reduction Empowered by 2D Hexagonal Transition Metal Borides

Electrocatalysis holds immense promise for producing high-value chemicals and fuels through the carbon dioxide reduction reaction (CO2RR), advancing global sustainability and carbon neutrality. However, conventional electrocatalysts based on transition metals are often limited by significant overpotentials. Since the discovery of the first hexagonal MAB (h-MAB) phase, Ti2InB2, and its 2D derivative in 2019, 2D hexagonal transition metal borides (h-MBenes) have emerged as promising candidates for various electrochemical applications. This study presents the first theoretical investigation into the CO2RR catalytic properties of pristine h-MBenes (h-MB) and their ─O (h-MBO) and ─OH (h-MBOH) terminated counterparts, focusing on metals such as Sc, Ti, V, Zr, Nb, Hf, and Ta. These results reveal while h-MB and h-MBO exhibit poor catalytic performance due to overly strong or weak interactions with CO2, h-MBOH shows great promise. Notably, ScBOH, TiBOH, and ZrBOH display exceptionally low limiting potentials (UL) of -0.46, -0.53, and -0.64 V, respectively. These findings uncover the unique role of ─OH in tuning the electronic properties of h-MBenes, thereby optimizing intermediate adsorption, which prevents excessive binding and enhances catalytic efficiency. This research offers valuable insights into the potential of h-MBenes as highly efficient CO2RR catalysts, underscoring their versatility and significant prospects for electrochemical applications.

Physics-informed neural networks for enhanced reference evapotranspiration estimation in Morocco: Balancing semi-physical models and deep learning

Reference evapotranspiration (ETo) is essential for agricultural water management, crop productivity, and irrigation systems. The Penman-Monteith (PM) equation is the standard method for estimating ETo, but its data-intensive nature makes it impractical, especially in situations where the cost of full standardized weather station is prohibitive, maintenance is inadequate, or data quality and continuity are compromised. To overcome those limitations, various semi-physical (SP) and empirical models with limited weather parameters were developed. In this context, artificial intelligence methods for ETo estimation are gaining more attention, balancing simplicity, minimal data requirements, and high accuracy. However, their data-driven nature raises concerns regarding explainability, trustworthiness, adherence to bio-physical laws, and reliability in operational settings. To address this issue, this paper, inspired by the emerging field of Physics-Informed Neural Networks (PINNs), evaluates the integration of SP models into the loss function during the learning process. The new residual loss combines two losses -the data-driven loss and the loss from SP- through a θ parameter, allowing for a convex combination. In-situ agrometeorological data were collected at four automatic weather stations in Tensift Watershed in Morocco, including air temperature (Ta), solar radiation (Rs), relative humidity (RH), and wind speed (Ws). The study integrates Priestley-Taylor (PT), Makkink (MK), Hargreaves-Samani (HS), and Abtew (AB), under four scenarios of data availability levels: (1) Ta, Rs and RH; (2) Ta and Rs; (3) only Ta; and (4) only Rs. The investigation begins with quality-controlling the data and studying the driving factors of ETo. Next, the SP models were calibrated using the CMA-ES optimization algorithm. The proposed PINN was trained and evaluated, first, for the equal contribution scenario (θ = 0.5) and then for θ in the interval [0, 1] with a step of 0.2, thus analyzing the impact of θ on the PINN performance. For the equal contribution, the results showed that the integration had improved the PINN performance in all scenarios in terms of the RMSE and R2, surpassing the fully data-driven model (θ = 0) and the baseline model (θ = 1). Additionally, for all θ within the interval [0.2, 0.8], the PINN required less training to reach optimal values. Finally, the optimal θ values were determined for each scenario using CMA-ES and were 0.258, 0.771, 0.7226 and 0.169 for PT, MK, HS and AB, respectively. While PINNs demonstrated a promising approach for accurate ETo estimation and consequently improved water resource management, the study also represents a step towards implementing controlled, trustworthy, and physics-informed AI in environmental science.

Will terrestrial biomes survive in the face of greenhouse gas emissions spillover: Insights from G20 countries

The emission of greenhouse gases (GHGs) presents localized concerns with far-reaching global repercussions, as the impacts of climate change extend beyond geographical boundaries. Despite global efforts, the cumulative effect of these endeavors falls short of the emission reduction benchmarks set by the Paris Agreement. Within this context, the study employs a time-varying parameter vector autoregressive frequency connectedness measure to examine GHG emission spillovers among G20 countries from 1971 to 2020. This method enables the analysis of connectedness intensity across both short and long time horizons. The findings reveal the time-varying nature of GHG emissions, with long-run connectedness contributing significantly more to total connectedness than short-term connectedness. The overall emission landscape remained largely unchanged until the Paris Agreement, with only slight declines observed later, including during the COVID-19 period. GHG spillovers notably impact terrestrial biome protection initiatives in G20 countries, particularly at lower quantiles. At the same time, temperature changes affect these initiatives primarily within the interquartile range, not at the extreme frequencies. Additionally, the spillover effects are asymmetric between large and smaller economies. The findings will be important for redefining GHG emission protocol policies and actionable standards for G20 countries.

Soil Microbial Carbon Use Efficiency in Natural Terrestrial Ecosystems

Soil microbial carbon use efficiency (CUE) is the ratio of carbon allocated to microbial growth to that taken up by microorganisms. Soil microbial CUE affects terrestrial ecosystem processes such as greenhouse gas emissions, carbon turnover, and sequestration, which is an important indicator of changes in the terrestrial carbon cycle. Firstly, we summarized the three methods of soil microbial CUE, stoichiometric modeling, 13C glucose tracing, and 18O water tracing, and compared the advantages and limitations of the three methods. Then, we analyzed the single or combined effects of different environmental factors on soil microbial CUE in grassland ecosystems, forest ecosystems, and wetland ecosystems. Finally, we suggested that future research should focus on the following aspects: the influence of management patterns on CUE (such as grazing and the prohibition of grazing in grassland ecosystems, forest gap, and thinning in forest ecosystems); effects of the strategies of microorganisms for adapting to environmental changes on CUE; effects of anaerobic metabolic pathways, especially in wetland ecosystems; and effects of microbial taxonomic level. This study contributes to the investigation of the microbial mechanisms of carbon cycling in terrestrial ecosystems to mitigate the impacts of climate change.

Network analyses unraveled the complex interactions in the rumen microbiota associated with methane emission in dairy cattle

BACKGROUND

Methane emissions from livestock, particularly from dairy cattle, represent a significant source of greenhouse gas, contributing to the global climate crisis. Understanding the complex interactions within the rumen microbiota that influence methane emissions is crucial for developing effective mitigation strategies.

RESULTS

This study employed Weighted Gene Co-expression Network Analysis to investigate the complex interactions within the rumen microbiota that influence methane emissions. By integrating extensive rumen microbiota sequencing data with precise methane emission measurements in 750 Holstein dairy cattle, our research identified distinct microbial communities and their associations with methane production. Key findings revealed that the blue module from network analysis was significantly correlated (0.45) with methane emissions. In this module, taxa included the genera Prevotella and Methanobrevibactor, along with species such as Prevotella brevis, Prevotella ruminicola, Prevotella baroniae, Prevotella bryantii, Lachnobacterium bovis, and Methanomassiliicoccus luminyensis are the key components to drive the complex networks. However, the absence of metagenomics sequencing is difficult to reveal the deeper taxa level and functional profiles.

CONCLUSIONS

The application of Weighted Gene Co-expression Network Analysis provided a comprehensive understanding of the microbiota-methane emission relationship, serving as an innovative approach for microbiota-phenotype association studies in cattle. Our findings underscore the importance of microbiota-trait and microbiota-microbiota associations related to methane emission in dairy cattle, contributing to a systematic understanding of methane production in cattle. This research offers key information on microbial management for mitigating environmental impact on the cattle population.

Sugarcane Extract (Polygain™) Supplementation Reduces Enteric Methane Emission in Dairy Calves

Polygain™ (PG), a polyphenolic extract from sugarcane, has recently been identified as a potential additive to reduce methane (CH4) emissions in livestock. This experiment examined the effects of PG on the enteric CH4 emission from Holstein Friesian weaned calves. Calves were allocated to annual pasture grazing and received supplementary pellets (200 g/calf/day; Barastoc calf-rearer cubes-Ridley Corporation). The experimental design followed was a completely randomized design (CRD), comprising 24 female calves (4-5 months old) allocated to two equal groups; control (standard pellets) vs. treatment (pellets formulated by adding PG to control pellets to deliver 10 g PG/calf/day). Experimental diets were fed for three months between August and November 2023, including a two-week adaptation period. Calves were weighed at the start and at the end of the study. A GreenFeed (C-Lock Pvt Ltd.) emission monitoring unit (GEM) was used to measure GHG emissions from the experimental calves in their groups in a 2-day rotational cycle. During a visit to the GEM, the calves were encouraged to enter an enclosed area or individual feeding stall where enteric CH4, CO2, O2, H2, and H2S measurements were taken. The results indicated a significant effect of PG supplementation on enteric methane emission in calves, with a lower production of CH4 in calves supplemented with PG (26.66 ± 2.06 g/day) as compared to the control group (35.28 ± 1.39 g/day, p < 0.001). The CO2/O2 ratio in the treatment (235 ± 14) and control groups (183 ± 9.6) differed significantly (p < 0.001). Overall, PG supplementation (10 g/calf/day) reduced their average methane emission per day and did not adversely affect the growth and development of experimental calves, confirming its useful anti-methanogenic potential.

Toward a greener vision: A review on advancing sustainability in ophthalmology

The growing environmental impact from healthcare sector necessitates the adoption of sustainable strategies to reuse, recycle, reduce waste, lower carbon emissions, etc. In ophthalmology, surgical waste poses a significant environmental challenge, particularly due to the high volume of surgeries, along with single-use instruments, packaging materials and disposable surgical supplies. Examples of practical strategies to reduce surgical waste include adopting reusable surgical instruments when safe and feasible, minimizing unnecessary packaging and optimizing operating room protocols, e.g., multidose topical drops on multiple patients. An education regarding sustainability for medical personnel can further decrease waste production in the long term. Collaboration between healthcare providers, manufacturers and policymakers is essential to developing and integrating sustainability into ophthalmic practice. By implementing these strategies, ophthalmologists can contribute to a more environmentally responsible healthcare system without compromising patient safety.

Impact of salinity stress on shifting microbial community and regulating N2O and CO2 dynamics in alkaline wetlands

Increasingly severe soil salinization in alkaline wetland due to elevated water evaporation under climate warming affected biogeochemical cycling processes, further threatening ecosystem imbalance and global greenhouse gas (GHG) budget. To reveal the underlying relationship between microbial dynamics, nitrous oxide (N2O) and carbon dioxide (CO2) characteristics under salinity stress in alkaline wetland, a 40-day microcosm experiment was conducted using soil collected from Zhalong wetland in northern China. The physiochemical properties, bacterial community, N2O and CO2 emissions were observed in responses to different salinity gradients (0%, 0.1%, 0.3%, 0.6%, 1.0%). The results showed that 1.0% salinity significantly increased cumulative N2O emissions by 578.5% and decreased cumulative CO2 emissions by 58.8% (p < 0.05). Increased nutrients (TOC, NO3--N) and decreased pH induced by salinity significantly regulated N2O (p < 0.05) and CO2 emissions (p < 0.01). Salinity led to significant loss of bacterial community diversity and strongly altered key bacteria related to C and N cycling. The salinity-sensitive taxa Gaiella and higher abundances of NorB than NosZ facilitated incomplete denitrification process, contributing to N2O emissions. Moreover, restrained genes involved in multiple CO2 production such as organics decomposition (glxk), microbial respiration (coxC) and methane oxidation (pmoA, pmoB) enabled alkaline wetland a CO2 sink under salinity stress. This study can provide new insights into salinity on microbial responses and GHG budgets in alkaline wetlands under the increasingly severe salinization trend.

Is economic freedom a panacea for environmental sustainability? Global and regional evidence

Environmental degradation continues to escalate globally, significantly threatening the world economy. The need to design and implement appropriate policies to address environmental degradation (CO2 emissions) has resulted in many studies examining the socioeconomic and institutional factors that affect CO2 emissions. While previous studies have examined various factors influencing CO2 emissions, there is limited evidence on how economic institutions impact it. This study explores the influence of economic institutions on CO2 emissions using panel data from 119 countries from 2000 to 2021 using the two-stage GMM and Lewbel 2SLS techniques as the analytical approaches. The results reveal that economic institutions effectively reduce CO2 emissions globally by 0.9%. The results further indicate that economic institutions are limited in reducing CO2 emissions in the lower quantiles, but in the middle quantiles, economic institutions have a moderate effect in reducing CO2 emissions. However, at the higher quantiles, economic institutions significantly reduce CO2 emissions. Regionally, the effects of economic institutions on CO2 emissions are heterogeneous. The results show that economic institutions significantly reduce CO2 emissions in Europe and Central Asia, Middle East and North Africa, East Asia and Pacific, Latin American and Caribbean, and North America but increase CO2 emissions in South Asia and Sub-Saharan Africa. These conclusions hold even after addressing endogeneity with the Lewbel 2SLS approach. The study recommends that policies promoting economic institutions are essential to mitigate CO2 emissions in achieving SDGs 7 and 13.

Evaluating site selection for optimal photovoltaic installations and CO₂ emission reduction in selected districts of khyber pakhtunkhwa

As the global market for renewable energy solutions expands, geospatial analysis is becoming crucial for optimizing solar potential. The current study assesses the suitability of installing PV solar system in the Mardan, Peshawar, and Nowshera districts in Pakistan using a multi-criteria decision-making (MCDM) approach. Analysis of different parameters, such as topography, land use and land cover (LULC), solar radiation and land surface temperature (LST), were performed to find the appropriate locations for solar in their respective regions. The study employed binary classification and weighted overlay methods to detect patterns of spatial suitability. Peshawar showed maximum ability with 859.8 km² categorized as favorable with a projected annual power output capacity of 67.77 trillion kWh and a decrease in CO₂ emission of 2.78 billion metric tons. Mardan closely followed the suitable area with 828.4 km² with energy generation of 39.74 trillion kWh/year and reduction of CO₂ emissions by 1.63 billion metric tons. Nowshera has an appropriate area of 503.0 km², for energy output of 670.06 billion kWh, and CO₂ reduction of 27.46 million metric tons. These results underline the importance of combining geospatial and meteorological data for accurate planning of solar energy systems. By highlighting location-specific features, including topography and solar irradiance illustrates the importance of tailoring energy outputs and environmental impacts to local contexts. These insights help guide policymakers in driving renewable energy projects, and support Pakistan's sustainable development and climate targets.

Chemical kinetic study of lean-burn and NO/N2O behaviors on ethanol/ammonia cracked gas

Ammonia (NH3) holds promise as a carbon-free fuel. Blending it with highly reactive fuels could efficiently alleviate issues such as slow burning rates and narrow flammability ranges. Ethanol (C2H5OH) offers the advantage of carbon neutrality and has a high-octane rating. Ammonia cracking is an efficient way to on-line hydrogen generation, mitigating storage and transportation concerns. This work aims to research the combustion of ammonia/ethanol blends with ammonia cracking. To achieve highly accurate predictions for these fuel blends, a detailed combustion chemical kinetic mechanism with 126 species and 1453 reactions for NH3, C2H5OH, H2, and their blends was developed. This mechanism underwent extensive validation against experimental measurements reported in the literature, including laminar burning velocity (LBV) at initial temperatures ranging from 298 to 473 K, pressures from 1 to 5 atm, and equivalence ratios from 0.5 to 1.6, as well as ignition delay time (IDT) at initial temperatures from 820 to 2500 K, pressures from 1.2 to 60 atm and equivalence ratios from 0.3 to 1. Additionally, a simplified mechanism containing 73 species and 449 reactions was developed using a couple of mechanism reduction methods. The effect of adding ethanol on LBV was investigated under laminar burning conditions, and the decoupling of thermal, chemical, and transport effects was studied. Considering the complexity of fuel composition for the blends, a modified Metghalchi-Kech power law formula was performed, which exhibits a highly linear correlation with LBVs of the blended fuels when coupled with the sum of maximum mole fractions for OH/O/H/NH2. Furthermore, NO and N2O behaviors were also investigated for various fuel blends and initial conditions. It was found that the a higher NH3 cracking ratio tends to help to reduce the NO generation, and thermal NO is higher at elevated initial temperature and pressure. N2O is easily produced under lean-burn condition.

Bimetallic PdPt nanoparticle-incorporated PEDOT:PSS/guar gum-blended membranes for enhanced CO2 separation

To address the escalating demand for efficient CO2 separation technologies, we introduce novel membranes utilizing natural polymer guar gum (GG), conjugate polymer (poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)) PEDOT:PSS, and bimetallic PdPt nanoparticles. Bimetallic PdPt nanoparticles were synthesized using the wet chemical method and characterized using X-ray photoelectron spectroscopy (XPS) and transmission electron microscopy (TEM) techniques. The morphologies, chemical bonds, functional groups, and mechanical properties of the fabricated membranes were characterized using various techniques. Through meticulous fabrication and characterization, the binary blended membranes demonstrated enhanced homogeneity and smoothness in their structure, attributed to the interaction between the polymers, and superior CO2 permeability due to the amphiphilic nature of the PEDOT:PSS polymer. Gas separation experiments performed using H2, N2, and CO2 gases confirmed that the 20% PEDOT:PSS/GG blended membranes showed the best performance with sufficient mechanical properties. Moreover, the results demonstrated an increase of 172% in CO2 permeability and 138% in CO2/H2 selectivity, respectively. Furthermore, integrating bimetallic PdPt nanoparticles provided an additional 197% increase in CO2/H2 selectivity, owing to the unique catalytic activities of noble metal nanoparticles. The study not only underscores the transformative potential of polymer blending and noble metal engineering, but also highlights the significance of using natural polymers for sustainable environmental solutions.

Seven critical challenges in synthetic one-carbon assimilation and their potential solutions

Synthetic C1 assimilation holds the promise of facilitating carbon capture while mitigating greenhouse gas emissions, yet practical implementation in microbial hosts remains relatively limited. Despite substantial progress in pathway design and prototyping, most efforts stay at the proof-of-concept stage, with frequent failures observed even under in vitro conditions. This review identifies seven major barriers constraining the deployment of synthetic C1 metabolism in microorganisms and proposes targeted strategies for overcoming these issues. A primary limitation is the low catalytic activity of carbon-fixing enzymes, particularly carboxylases, which restricts the overall pathway performance. In parallel, challenges in expressing multiple heterologous genes-especially those encoding metal-dependent or oxygen-sensitive enzymes-further hinder pathway functionality. At the systems level, synthetic C1 pathways often exhibit poor flux distribution, limited integration with the host metabolism, accumulation of toxic intermediates, and disruptions in redox and energy balance. These factors collectively reduce biomass formation and compromise product yields in biotechnological setups. Overcoming these interconnected challenges is essential for moving synthetic C1 assimilation beyond conceptual stages and enabling its application in scalable, efficient bioprocesses towards a circular bioeconomy.

Engineering Au single-atom sites embedded in TiO2 nanostructures for boosting photocatalytic methane oxidation

Photocatalytic methane oxidation under mild conditions using single-atom catalysts remains an advanced technology. In this work, gold single atoms (Au SAs) were introduced onto TiO2 nanostructures using a simple method. The resulting performance demonstrated effective conversion of methane into H2 and C2 products at room temperature. The as-synthesized Au SA/TiO2 exhibited a high hydrogen production rate of 2190 μmol g-1, with selectivity reaching up to 58% under optimized conditions. The methane oxidation mechanism was investigated, revealing a methyl radical pathway for generating value-added chemicals. This research provides a strategy for photocatalytic methane conversion over single-atom-supported photocatalysts.

Climate change, air pollution and chronic respiratory diseases: understanding risk factors and the need for adaptive strategies

Under the background of climate change, the escalating air pollution and extreme weather events have been identified as risk factors for chronic respiratory diseases (CRD), causing serious public health burden worldwide. This review aims to summarize the effects of changed atmospheric environment caused by climate change on CRD. Results indicated an increased risk of CRD (mainly COPD, asthma) associated with environmental factors, such as air pollutants, adverse meteorological conditions, extreme temperatures, sandstorms, wildfire, and atmospheric allergens. Furthermore, this association can be modified by factors such as socioeconomic status, adaptability, individual behavior, medical services. Potential pathophysiological mechanisms linking climate change and increased risk of CRD involved pulmonary inflammation, immune disorders, oxidative stress. Notably, the elderly, children, impoverished groups and people in regions with limited adaptability are more sensitive to respiratory health risks caused by climate change. This review provides a reference for understanding risk factors of CRD in the context of climate change, and calls for the necessity of adaptive strategies. Further interdisciplinary research and global collaboration are needed in the future to enhance adaptability and address climate health inequality.

Carbon Storage Response to Land Use/Land Cover Changes and SSP-RCP Scenarios Simulation: A Case Study in Yunnan Province, China

Changes in terrestrial ecosystem carbon storage (CS) affect the global carbon cycle, thereby influencing global climate change. Land use/land cover (LULC) shifts are key drivers of CS changes, making it crucial to predict their impact on CS for low-carbon development. Most studies model future LULC by adjusting change proportions, leading to overly subjective simulations. We integrated the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, the Patch-generating Land Use Simulation (PLUS) model, and the Land Use Harmonization 2 (LUH2) dataset to simulate future LULC in Yunnan under different SSP-RCP scenarios of climate and economic development. Within the new PLUS-InVEST-LUH2 framework, we systematically analyzed LULC alterations and their effects on CS from 1980 to 2040. Results demonstrated that: (1) Forestland had the highest CS, whereas built-up land and water showed minimal levels. Western areas boast higher CS, while the east has lower. From 1980 to 2020, CS continuously decreased by 29.55 Tg. In the wake of population increase and economic advancement, the area of built-up land expanded by 2.75 times. Built-up land encroaches on other land categories and is a key cause of the reduction in CS. (2) From 2020 to 2040, mainly due to an increase in forestland, CS rose to 3934.65 Tg under the SSP1-2.6 scenario, whereas under the SSP2-4.5 scenario, primarily due to a reduction in forestland and grassland areas, CS declined to 3800.86 Tg. (3) Forestland is the primary contributor to CS, whereas the ongoing enlargement of built-up land is causing a sustained decline in CS. Scenario simulations indicate that future LULC changes under different scenarios will have a significant impact on CS in Yunnan. Under a green sustainable development pathway, Yunnan can exhibit significant carbon sink potential. Overall, this research offers a scientific reference for optimizing land management and sustainable development in Yunnan, aiding China's "double carbon" goals.

Co-Hydrothermal Carbonization of Goose Feather and Pine Sawdust: A Promising Strategy for Disposal of Sports Waste and the Robust Improvement of the Supercapacitor Characteristics of Pyrolytic Nanoporous Carbon

Discarded sports waste faces bottlenecks in application due to inadequate disposal measures, and there is often a neglect of enhancing resource utilization efficiency and minimizing environmental impact. In this study, nanoporous biochar was prepared through co-hydrothermal carbonization (co-HTC) and pyrolytic activation by using mixed goose feathers and heavy-metals-contaminated pine sawdust. Comprehensive characterization demonstrated that the prepared M-3-25 (Biochar derived from mixed feedstocks (25 mg/g Cu in pine sawdust) at 700 °C with activator ratios of 3) possesses a high specific surface area 2501.08 m2 g-1 and abundant heteroatomic (N, O, and Cu), exhibiting an outstanding physicochemical structure and ultrahigh electrochemical performance. Compared to nanocarbon from a single feedstock, M-3-25 showed an ultrahigh capacitance of 587.14 F g-1 at 1 A g-1, high energy density of 42.16 Wh kg-1, and only 8.61% capacitance loss after enduring 10,000 cycles at a current density of 10 A g-1, positioning M-3-25 at the forefront of previously known biomass-derived nanoporous carbon supercapacitors. This research not only introduces a promising countermeasure for the disposal of sports waste but also provides superior biochar electrode materials with robust supercapacitor characteristics.

Combined Reaction System for NH3 Decomposition and CO2 Methanation Using Hydrogen Permeable Membrane Reactor in 1D Model Analysis

In a previous study, we developed an integrated reaction system combining NH3 decomposition and CO2 methanation within a membrane reactor, significantly enhancing reactor performance through efficient H2 separation. Ru/Ba/γ-Al2O3 and Ru/ZrO2 were employed as catalysts for each reaction. To ensure the accuracy and reliability of our results, they were validated through 1D models using FlexPDE Professional Version 7.21/W64 software. Key parameters such as reactor arrangement, catalyst bed positioning, overall heat transfer coefficient, rate constants, and H2 permeance were investigated to optimize system efficiency. The study revealed that positioning the NH3 decomposition on the shell side and CO2 methanation on the tube side resulted in a better performance. Additionally, shifting the methanation catalyst bed downward by approximately one-eighth (10 mm from 80 mm) achieves the highest CO2 conversion. A sensitivity analysis identified the rate constant of the NH3 decomposition catalyst and the H2 permeance of the membrane as the most influential factors in enhancing CO2 conversion. This highlights the priority of improving membrane H2 permeance and catalytic activity for NH3 decomposition to maximize system efficiency.

How does technological innovation moderate the environmental impacts of economic growth, natural resource rents and trade openness?

The objective of this study is to unravel the linear impacts of economic growth, technological innovation, natural resource rents and trade openness on carbon emissions in Malaysia during 1980-2021. It also unveils the moderating role of technological innovation on the impacts of economic growth, natural resource rents and trade openness on carbon emissions. It further analyses the nonlinear relationship between technological innovation and carbon emissions. It estimates the parameters with the Autoregressive Distributed Lag model technique. The results of the linear model reveal that economic growth, natural resource rents and trade openness contributes to carbon emissions while technological innovation mitigates carbon emissions. The disaggregated analysis of natural resource rents indicates that oil rents, natural gas rents and coal rents intensify carbon emissions while mineral rents and forest rents do not contribute to carbon emissions. The disaggregated analysis of trade openness shows that exports worsen carbon emissions while imports have tenuous effect. The disaggregated analysis of technological innovation indicates that innovation by non-residents mitigate carbon emissions while innovation by residents do not alleviate carbon emissions. Moreover, evidence from the interaction model reveals that technological innovation can favourably mitigate the adverse impacts of economic growth and trade openness on carbon emissions albeit it cannot alleviate the impact of natural resource rents on carbon emissions. Besides, the nonlinear model indicates a U-shaped relationship between technological innovation and carbon emissions. Unlike previous studies that typically focused on the direct impacts of these variables, this study unravels the impacts of the disaggregated components as well as provides insights into the moderating and nonlinear effects of technological innovation on carbon emissions. The implication of this study is that efforts to achieve a carbon-neutral economy should consider the direct and indirect impacts of economic growth, technological innovation, natural resource rents and trade openness. It is recommended for Malaysia to encourage technological innovation in her quest to abate the adverse environmental impacts of economic activities.

The role of large reservoirs in drought and flood disaster risk mitigation: A case of the Yellow River Basin

The acceleration of water cycle processes in the context of global warming will exacerbate the frequency and intensity of extreme events and predispose to drought and flood disasters (DFD). The Yellow River Basin (YRB) is one of the basins with significant and sensitive impacts of climate change, comprehensive assessment and prediction of its DFD risk are of great significance for ecological protection and high-quality development. This study first constructed an evaluation index system for drought disaster risk and flood disaster risk based on hazard, vulnerability, exposure and the role of large reservoirs. Secondly, the weights of each evaluation index are established by the analytic hierarchy process. Finally, based on the four-factor theory of disasters, an evaluation model of DFD risk indicators is established. The impact of large reservoirs on DFD risk in the YRB is analyzed with emphasis. The results show that from 1990 to 2020, the drought disaster risk in the YRB is mainly distributed in the source area of the Yellow River and the northwest region (11.26-15.79 %), and the flood disaster risk is mainly distributed in the middle and lower reaches (30.04-31.29 %). Compared to scenarios without considering large reservoirs, the area at risk of high drought and high flood is reduced by 45.45 %, 44.22 % and 31.29 % in 2000, 2010 and 2020, respectively. Large reservoirs in the YRB play an important role in mitigating DFD risk, but their role is weakened with the enhancement of the emission scenario. Under the influence of different scenario models, the DFD risk in the YRB in 2030 and 2060 will increase, and the area of high drought and high flood risk in the middle and upper reaches of the basin will increase by 0.26-25.15 %. Therefore, the YRB should play the role of large reservoirs in DFD risk defense in its actions to cope with future climate change, while improving non-engineering measures such as early warning and emergency management systems to mitigate the impacts of disasters.

Uncovering leveraging and hindering factors in socio-ecological interactions: Agricultural production in the Yellow River Basin as an example

Agricultural production and sustainable human livelihoods in large river basins are threatened by climate change, human activities, and resource constraints. However, due to the complexity of socio-ecological interactions and agricultural sustainability, current studies are still limited by a priori knowledge and systematic analyses, as well as by the lack of quantification and identification of key factors and valuable pathway structures for agricultural production activities. Here, we combined observation-based causal inference and network analysis to quantify and assess the complex interactions in agricultural production in the Yellow River Basin (YRB) based on data from 12 factors relevant to agriculture over 40 years. We quantitatively assessed the leveraging and hindering roles of the factors in the interacting network system and provided managers with optimization priorities and possible causal pathways to achieve sustainable agriculture in the basin. For example, the fruit yield and income of rural households were identified as leveraging factors that positively affect the agricultural economy. Groundwater was seen as a hindering factor in dampening the negative impacts of the system, highlighting the importance of preventing groundwater depletion. Moreover, the findings suggest that spatially diverse causal interaction structures exist in the YRB and have shaped a variety of distinctive agricultural development modes. Our research ideas and results highlight both systemic considerations and the amplifying or dampening role of factors in interaction pathways, providing valuable quantitative insights into the management and intervention of sustainable agriculture in large river basins. Owing to replaceable and extensible network models, the methodology has the potential to be utilized in a variety of study areas and topics with complex socio-ecological interactions.

Sugarcane/Soybean Intercropping with Reduced Nitrogen Application Synergistically Increases Plant Carbon Fixation and Soil Organic Carbon Sequestration

Sugarcane/soybean intercropping and reduced nitrogen (N) application as an important sustainable agricultural pattern can increase crop primary productivity and improve soil ecological functions, thereby affecting soil organic carbon (SOC) input and turnover. To explore the potential mechanism of sugarcane/soybean intercropping affecting SOC sequestration, a two-factor long-term field experiment was carried out, which included planting pattern (sugarcane monocropping (MS), sugarcane/soybean 1:1 intercropping (SB1), and sugarcane/soybean 1:2 intercropping (SB2)) and nitrogen addition levels (reduced N application (N1: 300 kg·hm-2) and conventional N application (N2: 525 kg·hm-2)). The results showed that the shoot and root C fixation in the sugarcane/soybean intercropping system were significantly higher than those in the sugarcane monocropping system during the whole growth period of sugarcane, and the N application level had no significant effect on the C fixation of plants in the intercropping system. Sugarcane/soybean intercropping also increased the contents of total organic C (TOC), labile organic C fraction [microbial biomass C (MBC) and dissolved organic C (DOC)] in the soil during the growth period of sugarcane, and this effect was more obvious at the N1 level. We further analyzed the relationship between plant C sequestration and SOC fraction content using regression equations and found that both plant shoot and root C sequestration were significantly correlated with TOC, MBC, and DOC content. This suggests that sugarcane/soybean intercropping increases the amount of C input to the soil by improving crop shoot and root C sequestration, which then promotes the content of each SOC fraction. The results of this study indicate that sugarcane/soybean intercropping and reduced N application patterns can synergistically improve plant and soil C fixation, which is of great significance for improving crop yields, increasing soil fertility, and reducing greenhouse gas emissions from agricultural fields.

Coupling eco-environmental quality and ecosystem services to delineate priority ecological reserves-A case study in the Yellow River Basin

Priority ecological reserves (PER) aim to protect areas with significant ecological value and crucial ecological functions, optimizing resource allocation to maximize the benefits of ecological conservation. However, most previous studies have considered only ecosystem services (ESs) in delineating PER, neglecting eco-environmental quality (EEQ). This study used the Remote Sensing-based Ecological Index (RSEI) to represent EEQ and combined it with ESs to delineate PER at the county scale in the Yellow River Basin (YRB). Additionally, it employed Multiscale Geographically Weighted Regression to identify the driving factors influencing the ESs and EEQ of PER. The results showed that: (1) From 2000 to 2020, both RSEI and the Comprehensive ESs (CES) in the YRB exhibited a fluctuating upward trend; (2) Three types of PER were extracted, with ESs reserve mainly distributed in the upstream region, EEQ reserve primarily in the middle and lower reaches, and integrated ecological reserve mainly in the midstream region, all dominated by vegetation land-use types; (3) Within the extracted PER, RSEI was mainly influenced by soil, aspect, population (pop), PM2.5, temperature (tmp), and potential evapotranspiration (pet), while CES was affected by soil, pop, PM2.5, slope, tmp, precipitation, and pet. To enhance the EEQ and ESs of the YRB, it was recommended to incorporate at least 105,379 km2 into the existing protected areas in the YRB. These areas should be subdivided based on their ecological status, with specific management measures for different types of PER. This study provides recommendations for environmental protection and land planning in the YRB, actively responding to current policies on high-quality development and ecological environmental protection in the YRB.