Estimation of methane greenhouse gas emissions from livestock in Egypt during 1989 to 2021.

This study investigates methane emissions from the livestock sector, representing by enteric fermentation and manure management, in Egypt from 1989 to 2021, focusing on spatial and temporal variations at the governorate level. Utilizing IPCC guidelines and emission factors, methane emissions were estimated for dairy and non-dairy cattle, buffalo, sheep and goat, poultry, and other livestock categories. Results reveal fluctuating emission patterns over the study period, with notable declines in certain governorates such as Kafr El-Sheikh and Red Sea, attributed to reductions in livestock populations. However, increasing trends were observed overall, driven by population growth in other regions. Hotspots of methane emissions were identified in delta governorates like Behera and Sharkia, as well as agriculturally rich regions including Menia and Suhag. While livestock populations varied between regions, factors such as water availability, climatic conditions, and farming practices influenced distribution. Notably, cluster analysis did not reveal regional clustering among governorates, suggesting emissions changes were not dependent on specific geographic or climatic boundaries. Manure management accounted for only 5-6% of total emissions, with emissions at their lowest in the last three years due to population declines. Despite the highest livestock populations being sheep and goats, emissions from enteric fermentation and manure management were highest from buffalo and cattle. This study underscores the importance of accurate data collection and adherence to IPCC recommendations for estimating GHG emissions, enabling the development of targeted mitigation strategies to address climate change challenges in the livestock sector.

Ultrasonication as anaerobic digestion pretreatment to improve sewage sludge methane production: Performance and microbial characterization.

A large amount of sludge is inevitably produced during sewage treatment. Ultrasonication (US) as anaerobic digestion (AD) pretreatment was implemented on different sludges and its effects on batch and semi-continuous AD performance were investigated. US was effective in sludge SCOD increase, size decrease, and CH4 production in the subsequent AD, and these effects were enhanced with an elevated specific energy input. As indicated by semi-continuous AD experiments, the mean daily CH4 production of US-pretreated A2O-, A2O-MBR-, and AO-AO-sludge were 176.9, 119.8, and 141.7 NmL/g-VSadded, which were 35.1%, 32.1% and 78.2% higher than methane production of their respective raw sludge. The US of A2O-sludge achieved preferable US effects and CH4 production due to its high organic content and weak sludge structure stability. In response to US-pretreated sludge, a more diverse microbial community was observed in AD. The US-AD system showed negative net energy; however, it exhibited other positive effects, e.g., lower required sludge retention time and less residual total solids for disposal. US is a feasible option prior to AD to improve anaerobic bioconversion and CH4 yield although further studies are necessary to advance it in practice.

Changing climatic controls on the greenhouse gas balance of thermokarst bogs during succession after permafrost thaw.

Permafrost thaw in northern peatlands causes collapse of permafrost peat plateaus and thermokarst bog development, with potential impacts on atmospheric greenhouse gas exchange. Here, we measured methane and carbon dioxide fluxes over 3 years (including winters) using static chambers along two permafrost thaw transects in northwestern Canada, spanning young (~30 years since thaw), intermediate and mature thermokarst bogs (~200 years since thaw). Young bogs were wetter, warmer and had more hydrophilic vegetation than mature bogs. Methane emissions increased with wetness and soil temperature (40 cm depth) and modelled annual estimates were greatest in the young bog during the warmest year and lowest in the mature bog during the coolest year (21 and 7 g C-CH4 m-2 year-1, respectively). The dominant control on net ecosystem exchange (NEE) in the mature bog (between +20 and -54 g C-CO2 m-2 year-1) was soil temperature (5 cm), causing net CO2 loss due to higher ecosystem respiration (ER) in warmer years. In contrast, wetness controlled NEE in the young and intermediate bogs (between +55 and -95 g C-CO2 m-2 year-1), where years with periodic inundation at the beginning of the growing season caused greater reduction in gross primary productivity than in ER leading to CO2 loss. Winter fluxes (November-April) represented 16% of annual ER and 38% of annual CH4 emissions. Our study found NEE of thermokarst bogs to be close to neutral and rules out large CO2 losses under current conditions. However, high CH4 emissions after thaw caused a positive net radiative forcing effect. While wet conditions favouring high CH4 emissions only persist for the initial young bog period, we showed that continued climate warming with increased ER, and thus, CO2 losses from the mature bog can cause net positive radiative forcing which would last for centuries after permafrost thaw.

Seasonal and trend variation of methane concentration over two provinces of South Africa using Sentinel-5p data.

South Africa faces the urgency to comprehensively understand and manage its methane (CH4) emissions. The primary aim of this study is to compare CH4 concentrations between Eastern Cape and Mpumalanga regions dominated by cattle farming and coal mining industries, respectively. CH4 concentration trends were analyzed for the period 2019 to 2023 using satellite data. Trend analysis revealed significant increasing trends in CH4 concentrations in both provinces, supported by Mann-Kendall tests that rejected the null hypothesis of no trend (Eastern Cape: p-value = 8.9018e-08 and Mpumalanga: p-value = 2.4650e-10). The Eastern Cape, a leading cattle farming province, exhibited cyclical patterns and increasing CH4 concentrations, while Mpumalanga, a major coal mining province, displayed similar increasing trends with sharper concentration points. The results show seasonal variations in CH4 concentrations in the Eastern Cape and Mpumalanga provinces. High CH4 concentrations are observed in the northwestern region during the December-January-February (DJF) season, while lower concentrations are observed in the March-April-May (MAM) and June-July-August (JJA) seasons in the Eastern Cape province. In the Mpumalanga province, there is a dominance of high CH4 concentrations in southwestern regions and moderately low concentrations in the northeastern regions, observed consistently across all seasons. The study also showed an increasing CH4 concentration trend from 2019 to 2023 for both provinces. The study highlights the urgent need to address CH4 emissions from both cattle farming and coal mining activities to mitigate environmental impacts and promote sustainable development. Utilizing geographic information system (GIS) and remote sensing technologies, policymakers and stakeholders can identify and address the sources of CH4 emissions more effectively, thereby contributing to environmental conservation and sustainable resource management.

Assessing the Progress of the Performance of Continuous Monitoring Solutions under a Single-Blind Controlled Testing Protocol.

The recent regulatory spotlight on continuous monitoring (CM) solutions and the rapid development of CM solutions have demanded the characterization of solution performance through regular, rigorous testing using consensus test protocols. This study is the second known implementation of such a protocol involving single-blind controlled testing of 9 CM solutions. Controlled releases of rates (6-7100 g) CH4/h over durations (0.4-10.2 h) under a wind speed range of (0.7-9.9 m/s) were conducted for 11 weeks. Results showed that 4 solutions achieved method detection limits (DL90s) within the tested emission rate range, with all 4 solutions having both the lowest DL90s (3.9 [3.0, 5.5] kg CH4/h to 6.2 [3.7, 16.7] kg CH4/h) and false positive rates (6.9-13.2%), indicating efforts at balancing low sensitivity with a low false positive rate. These results are likely best-case scenario estimates since the test center represents a near-ideal upstream field natural gas operation condition. Quantification results showed wide individual estimate uncertainties, with emissions underestimation and overestimation by factors up to >14 and 42, respectively. Three solutions had >80% of their estimates within a quantification factor of 3 for controlled releases in the ranges of [0.1-1] kg CH4/h and > 1 kg CH4/h. Relative to the study by Bell et al., current solutions performance, as a group, generally improved, primarily due to solutions from the study by Bell et al. that were retested. This result highlights the importance of regular quality testing to the advancement of CM solutions for effective emissions mitigation.

Autochthonous organic matter input in reservoirs: Limited methane oxidation in sediments fails to suppress methane emission.

The interception of rivers leads to the accumulation of substantial organic matter in reservoirs, exerting a significant influence on greenhouse gas emissions. The diverse imported organic matter, coupled with sedimentary heterogeneity and intricate microbial processes, gives rise to seasonal variations in methane emissions from reservoirs. In this study, sediment cores were supplemented with terrestrial or autochthonous carbon to emulate reservoir carbon input across different seasons, thereby investigating methane emission potential and associated microbial mechanisms within the sediment cores. Results demonstrated that autochthonous organic matter enhanced sediment organic content, thereby providing more substrates for the methanogenic process and fostering the proliferation of methanogens (with a relative abundance of 47.17 % to 60.66 %). Notably, the dominant genera of Methanosaeta, Methanosarcina, and Candidatus Methanomethylicus were boost on the surface layer of sediment. Concurrently, the introduction of autochthonous organic carbon spurred an increase in methane-oxidizing microbe, reaching up to 5.59 %, with Methylobacter and Candidatus Methanoperedens as the predominant species, which led to a downward migration of the functional microbial group in the sediment. Under the priming impact of autochthonous carbon, however, the methane oxidation probably doesn't consume the substantial methane produced in sediment. Consequently, the sediment functions as a hotspot for methane release into the overlying water, highlighting the necessity to include summer as critical periods for integrated assessments, particularly during algae bloom.

Automated machine learning-aided prediction and interpretation of gaseous by-products from the hydrothermal liquefaction of biomass.

Hydrothermal liquefaction (HTL) is a thermochemical conversion technology that produces bio-oil from wet biomass without drying. However, by-product gases will inevitably be produced, and their formation is unclear. Therefore, an automated machine learning (AutoML) approach, automatically training without human intervention, was used to aid in predicting gaseous production and interpreting the formation mechanisms of four gases (CO2, CH4, CO, and H2). Specifically, four accurate optimal single-target models based on AutoML were developed with elemental compositions and HTL conditions as inputs for four gases. Herein, the gradient boosting machine (GBM) performed excellently with train R2 ≥ 0.99 and test R2 ≥ 0.80. Then, the screened GBM algorithm-based ML multi-target models (maximum average test R2 = 0.89 and RMSE = 0.39) were built to predict four gases simultaneously. Results indicated that biomass carbon, solid content, pressure, and biomass hydrogen were the top four factors for gas production from HTL of biomass. This study proposed an AutoML-aided prediction and interpretation framework, which could provide new insight for rapid prediction and revelation of gaseous compositions from the HTL process.

Anaerobic digestion of herbal waste: a waste to energy option.

Herbal waste produced during the manufacturing of herbal products is a potential feedstock for anaerobic digestion due to high amount of organic matter that can be transformed into biogas as an energy resource. Therefore, the present study was undertaken to convert herbal waste produced during the manufacturing of common of Ayurveda products into biogas through anaerobic digestion process using batch test study under controlled mesophilic temperature conditions of 35 °C with food to inoculum ratio of 0.75. The maximum biomethane potential (BMP) of 0.90 (gCH4COD/g CODfed) and sludge activity of 0.70 (gCH4-CD/gVSS) was exhibited by WS herbal waste owing to its high chemical oxygen demand (COD) of 4 g/g and better solubilization potential of the organic matter showing change in volatile suspended solids (ΔVSS) of 79%. On the other hand, the waste derived from the TA herb, exhibited the least biogas yield of 0.55 (gCH4COD/g CODfed) and sludge activity of 0.40 (gCH4-CD/gVSS), albeit with higher organic matter present. This was due to the possible hindrance of waste solubilization by the presence of lignin. The waste derived from VVL and PE showed intermediate BMP and sludge activity. The methane generation rate constant (k), a key indicator of the biodegradation potential, was also evaluated. The k values showed similar trend as of BMP values ranging from 0.081 to 0.15 d-1 thus indicating the influence of presence of lignin and the change in ΔVSS. The present study proves anaerobic digestion to be an alternative treatment method to be a milestone for management of herbal wastes and can be successfully implemented on real-scale systems.

IoT-based monitoring system and air quality prediction using machine learning for a healthy environment in Cameroon.

This paper is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is an air quality assessor and therefore a means of achieving the Sustainable Development Goals (SDGs), in particular, SDG 3.9 (substantial reduction of the health impacts of hazardous substances) and SDG 11.6 (reduction of negative impacts on cities and populations). AQI quantifies and informs the public about air pollutants and their adverse effects on public health. The proposed air quality monitoring device is low-cost and operates in real-time. It consists of a hardware unit that detects various pollutants to assess air quality as well as other airborne particles such as carbon dioxide (CO2), methane (CH4), volatile organic compounds (VOCs), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter with an aerodynamic diameter of 2.5 microns or less (PM2.5). To predict air quality, the device was deployed from November 1, 2022, to February 4, 2023, in certain bauxite-rich areas of Adamawa and certain volcanic sites in western Cameroon. Therefore, machine learning algorithm models, namely, multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), XGBoost (XGB), and K-nearest neighbors (KNN) were applied to analyze the collected concentrations and predict the future state of air quality. The performance of these models was evaluated using mean absolute error (MAE), coefficient of determination (R-square), and root mean square error (RMSE). The obtained data in this study show that these pollutants are present in selected localities albeit to different extents. Moreover, the AQI values obtained range from 10 to 530, with a mean of 132.380 ± 63.705, corresponding to moderate air quality state but may induce an adverse effect on sensitive members of the population. This study revealed that XGB regression performed better in air quality forecasting with the highest R-squared (test score of 0.9991 and train score of 0.9999) and lowest RMSE (test score of 1.5748 and train score of 0. 0073) and MAE (test score of 0.0872 and train score of 0.0020), while the KNN model had the worst prediction (lowest R-squared and highest RMSE and MAE). This embryonic work is a prototype for projects in Cameroon as measurements are underway for a national spread over a longer period of time.

The challenge of estimating global termite methane emissions.

Methane is a powerful greenhouse gas, more potent than carbon dioxide, and emitted from a variety of natural sources including wetlands, permafrost, mammalian guts and termites. As increases in global temperatures continue to break records, quantifying the magnitudes of key methane sources has never been more pertinent. Over the last 40 years, the contribution of termites to the global methane budget has been subject to much debate. The most recent estimates of termite emissions range between 9 and 15 Tg CH4 year-1, approximately 4% of emissions from natural sources (excluding wetlands). However, we argue that the current approach for estimating termite contributions to the global methane budget is flawed. Key parameters, namely termite methane emissions from soil, deadwood, living tree stems, epigeal mounds and arboreal nests, are largely ignored in global estimates. This omission occurs because data are lacking and research objectives, crucially, neglect variation in termite ecology. Furthermore, inconsistencies in data collection methods prohibit the pooling of data required to compute global estimates. Here, we summarise the advances made over the last 40 years and illustrate how different aspects of termite ecology can influence the termite contribution to global methane emissions. Additionally, we highlight technological advances that may help researchers investigate termite methane emissions on a larger scale. Finally, we consider dynamic feedback mechanisms of climate warming and land-use change on termite methane emissions. We conclude that ultimately the global contribution of termites to atmospheric methane remains unknown and thus present an alternative framework for estimating their emissions. To significantly improve estimates, we outline outstanding questions to guide future research efforts.

Unveiling the impact of soil methane sink on atmospheric methane concentrations in 2020.

In 2020, anthropogenic methane (CH4) emissions decreased due to COVID-19 containment policies, but there was a substantial increase in the concentration of atmospheric CH4. Previous research suggested that this abnormal increase was linked to higher wetland CH4 emissions and a decrease in the atmospheric CH4 sink. However, the impact of changes in the soil CH4 sink remained unknown. To address this, we utilized a process-based model to quantify alterations in the soil CH4 sink of terrestrial ecosystems between 2019 and 2020. By implementing the model with various datasets, we consistently observed an increase in the global soil CH4 sink, reaching up to 0.35 ± 0.06 Tg in 2020 compared to 2019. This increase was primarily attributed to warmer soil temperatures in northern high latitudes. Our results emphasize the importance of considering the CH4 sink in terrestrial ecosystems, as neglecting this component can lead to an underestimation of both emission increases and reductions in atmospheric CH4 sink capacity. Furthermore, these findings highlight the potential role of increased soil warmth in terrestrial ecosystems in slowing the growth of CH4 concentrations in the atmosphere.

Dose-dependent effects of sodium dodecyl sulfate and hydrogen peroxide treatments on methane emission from pig manure during storage.

Mitigation of methane (CH4) emissions from slurry pits within pig barns can be achieved through treatment of residual slurry left after frequent flushing of the slurry pits. In this study, dosages of additives such as sodium dodecyl sulfate (SDS) and hydrogen peroxide (H2O2) were optimized to achieve reduction in CH4 emissions from residual pig slurry during storage. In addition, the effects on emissions when both the treatments were combined and the effects of SDS treatment on slurry acidified with sulfuric acid (H2SO4) were studied in order to reduce CH4 and ammonia (NH3) emissions from residual pig slurry storage. A maximum of 98% and 70% reduction in CH4 emissions were achieved with SDS and H2O2 treatments, respectively. The combination of SDS and H2O2 did not increase efficiency in reducing CH4 emissions compared to SDS treatment alone. Whereas the application of SDS to slurry acidified with H2SO4 (pH 6.2) increased the CH4 mitigation efficiency by 15-30% compared to treating slurry with only SDS. The combined treatment (SDS + H2SO4) reduced NH3 emissions by 20% compared to treating slurry with H2SO4 (pH 6.2) alone. Hereby, combined treatment (SDS + H2SO4) can reduce both CH4 and NH3 emissions, with a reduced amount of chemicals required for the treatment. Hence, application of SDS at concentrations <2 g kg-1 to acidified slurry is recommended to treat residual pig manure in pig barns.

Changing the order and ratio of substrate filling reduced CH4 and N2O emissions from the aerated constructed wetlands.

Constructed wetlands (CWs) have been used to enhance pollutant removal by filling several types of material as substrates. However, research on substrate filling order remains still limited, particularly regarding the effects of greenhouse gas (GHG) emissions. In this study, six CWs were constructed using zeolite and ferric­carbon micro-electrolysis (Fe-C) fillers to evaluate the effect of changing the filling order and ratio on pollutant removal, GHGs emissions, and associated microbial structure. The results showed that the order of substrate filling significantly impacted pollutant removal performance on CWs. Specifically, CWs filled with zeolite in the top layer exhibited superior NH4+-N removal compared to those filled in the lower layer. Moreover, the highest NH4+-N removal (95.0 % ± 1.9 %) was observed in CWs with a zeolite to Fe-C volume ratio of 8:2 (CWZe-1). Moreover, zeolite-filled at the top had lower GHGs emissions, with the lowest CH4 (0.22 ± 0.10 mg m-2 h-1) and N2O (167.03 ± 61.40 µg m-2 h-1) fluxes in the CWZe-1. In addition, it is worth noting that N2O is the major contributor to integrated global warming potential (GWP) in the six CWs, accounting for 81.7 %-90.8 %. The upper layer of CWs filled with zeolite exhibited higher abundances of nirK, nirS and nosZ genes. The order in which the substrate was filled affected the microbial community structure and the upper layer of CWs filled with zeolite had higher relative abundance of nitrifying genera (Nitrobacter, Nitrosomonas) and denitrifying genera (Zoogloea, Denitratisoma). Additionally, N2O emission was reduced by approximately 41.2 %-64.4 % when the location of the aeration of the CWs was changed from the bottom to the middle. This study showed that both the order of filling the substrate and the aeration position significantly affected the GHGs emissions from CWs, and that CWs had lower GHGs emissions when zeolites were filled in the upper layer and the aeration position was in the middle.

Development of a Smartwatch with Gas and Environmental Sensors for Air Quality Monitoring.

In recent years, there has been a growing interest in developing portable and personal devices for measuring air quality and surrounding pollutants, partly due to the need for ventilation in the aftermath of COVID-19 situation. Moreover, the monitoring of hazardous chemical agents is a focus for ensuring compliance with safety standards and is an indispensable component in safeguarding human welfare. Air quality measurement is conducted by public institutions with high precision but costly equipment, which requires constant calibration and maintenance by highly qualified personnel for its proper operation. Such devices, used as reference stations, have a low spatial resolution since, due to their high cost, they are usually located in a few fixed places in the city or region to be studied. However, they also have a low temporal resolution, providing few samples per hour. To overcome these drawbacks and to provide people with personalized and up-to-date air quality information, a personal device (smartwatch) based on MEMS gas sensors has been developed. The methodology followed to validate the performance of the prototype was as follows: firstly, the detection capability was tested by measuring carbon dioxide and methane at different concentrations, resulting in low detection limits; secondly, several experiments were performed to test the discrimination capability against gases such as toluene, xylene, and ethylbenzene. principal component analysis of the data showed good separation and discrimination between the gases measured.

A critical review on characterization, human health risk assessment and mitigation of malodorous gaseous emission during the composting process.

Composting has emerged as a suitable method to convert or transform organic waste including manure, green waste, and food waste into valuable products with several advantages, such as high efficiency, cost feasibility, and being environmentally friendly. However, volatile organic compounds (VOCs), mainly malodorous gases, are the major concern and challenges to overcome in facilitating composting. Ammonia (NH3) and volatile sulfur compounds (VSCs), including hydrogen sulfide (H2S), and methyl mercaptan (CH4S), primarily contributed to the malodorous gases emission during the entire composting process due to their low olfactory threshold. These compounds are mainly emitted at the thermophilic phase, accounting for over 70% of total gas emissions during the whole process, whereas methane (CH4) and nitrous oxide (N2O) are commonly detected during the mesophilic and cooling phases. Therefore, the human health risk assessment of malodorous gases using various indexes such as ECi (maximum exposure concentration for an individual volatile compound EC), HR (non-carcinogenic risk), and CR (carcinogenic risk) has been evaluated and discussed. Also, several strategies such as maintaining optimal operating conditions, and adding bulking agents and additives (e.g., biochar and zeolite) to reduce malodorous emissions have been pointed out and highlighted. Biochar has specific adsorption properties such as high surface area and high porosity and contains various functional groups that can adsorb up to 60%-70% of malodorous gases emitted from composting. Notably, biofiltration emerged as a resilient and cost-effective technique, achieving up to 90% reduction in malodorous gases at the end-of-pipe. This study offers a comprehensive insight into the characterization of malodorous emissions during composting. Additionally, it emphasizes the need to address these issues on a larger scale and provides a promising outlook for future research.

Technological Maturity of Aircraft-Based Methane Sensing for Greenhouse Gas Mitigation.

Methane is a major contributor to anthropogenic greenhouse gas emissions. Identifying large sources of methane, particularly from the oil and gas sectors, will be essential for mitigating climate change. Aircraft-based methane sensing platforms can rapidly detect and quantify methane point-source emissions across large geographic regions, and play an increasingly important role in industrial methane management and greenhouse gas inventory. We independently evaluate the performance of five major methane-sensing aircraft platforms: Carbon Mapper, GHGSat-AV, Insight M, MethaneAIR, and Scientific Aviation. Over a 6 week period, we released metered gas for over 700 single-blind measurements across all five platforms to evaluate their ability to detect and quantify emissions that range from 1 to over 1,500 kg(CH4)/h. Aircraft consistently quantified releases above 10 kg(CH4)/h, and GHGSat-AV and Insight M detected emissions below 5 kg(CH4)/h. Fully blinded quantification estimates for platforms using downward-facing imaging spectrometers have parity slopes ranging from 0.76 to 1.13, with R2 values of 0.61 to 0.93; the platform using continuous air sampling has a parity slope of 0.5 (R2 = 0.93). Results demonstrate that aircraft-based methane sensing has matured since previous studies and is ready for an increasingly important role in environmental policy and regulation.

Estimated greenhouse gas emissions in the Mbalmayo thermal power plant between 2016-2020 using the genetic-Gaussian algorithm coupling.

The greenhouse gas (GHG) emissions inventories in our context result from the production of electricity from fuel oil at the Mbalmayo thermal power plant between 2016 and 2020. Our study area is located in the Central Cameroon region. The empirical method of the second level of industrialisation was applied to estimate GHG emissions and the application of the genetic algorithm-Gaussian (GA-Gaussian) coupling method was used to optimise the estimation of GHG emissions. Our work is of an experimental nature and aims to estimate the quantities of GHG produced by the Mbalmayo thermal power plant during its operation. The search for the best objective function using genetic algorithms is designed to bring us closer to the best concentration, and the Gaussian model is used to estimate the concentration level. The results obtained show that the average monthly emissions in kilograms (kg) of GHGs from the Mbalmayo thermal power plant are: 526 kg for carbon dioxide (CO2), 971.41 kg for methane (CH4) and 309.41 kg for nitrous oxide (N2O), for an average monthly production of 6058.12 kWh of energy. Evaluation of the stack height shows that increasing the stack height helps to reduce local GHG concentrations. According to the Cameroonian standards published in 2021, the limit concentrations of GHGs remain below 30 mg/m3 for CO2 and 200 µg/m3 for N2O, while for CH4 we reach the limit value of 60 µg/m3. These results will enable the authorities to take appropriate measures to reduce GHG concentrations.

Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables.

Concerns about methane (CH4) emissions from rice, a staple sustaining over 3.5 billion people globally, are heightened due to its status as the second-largest contributor to greenhouse gases, driving climate change. Accurate quantification of CH4 emissions from rice fields is crucial for understanding gas concentrations. Leveraging technological advancements, we present a groundbreaking solution that integrates machine learning and remote sensing data, challenging traditional closed chamber methods. To achieve this, our methodology involves extensive data collection using drones equipped with a Micasense Altum camera and ground sensors, effectively reducing reliance on labor-intensive and costly field sampling. In this experimental project, our research delves into the intricate relationship between environmental variables, such as soil conditions and weather patterns, and CH4 emissions. We achieved remarkable results by utilizing unmanned aerial vehicles (UAV) and evaluating over 20 regression models, emphasizing an R2 value of 0.98 and 0.95 for the training and testing data, respectively. This outcome designates the random forest regressor as the most suitable model with superior predictive capabilities. Notably, phosphorus, GRVI median, and cumulative soil and water temperature emerged as the model's fittest variables for predicting these values. Our findings underscore an innovative, cost-effective, and efficient alternative for quantifying CH4 emissions, marking a significant advancement in the technology-driven approach to evaluating rice growth parameters and vegetation indices, providing valuable insights for advancing gas emissions studies in rice paddies.

Recent advances on greenhouse gas emissions from wetlands: Mechanism, global warming potential, and environmental drivers.

Greenhouse gas (GHG) emissions from wetlands have exacerbated global warming, attracting worldwide attention. However, the research process and development trends in this field remain unknown. Herein, 1865 papers related to wetlands GHG emissions published from January 2000 to December 2023 were selected, and CiteSpace and VOSviewer were used for bibliometric analysis to visually analyze the publications distribution, research authors, organizations and countries, core journal and keywords, and discussed the research progress, trends and hotspots in the fields. Over the past 24 years, the research has gone through three phases: the "embryonic" stage (2000-2006), the accumulation stage (2007-2014), and the acceleration stage (2015-2023). China has played a pivotal role in this domain, publishing the most papers and working closely with the United States, United Kingdom, Canada, Germany, and Australia. In addition, this study synthesized 311 field observations from 123 publications to analyze the variability in GHG emissions and their driving factors in four different types of natural wetlands. The results suggested that the average carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) fluxes in different wetlands were significantly different. River wetlands exhibited the highest GHG fluxes, while marsh wetlands demonstrated greater global warming potential (GWP). The average CO2, CH4 and N2O fluxes were 60.41 mg m-2·h-1, 2.52 mg m-2·h-1 and 0.05 mg m-2·h-1, respectively. The GWP of Chinese natural wetlands was estimated as 648.72 Tg·CO2-eq·yr-1, and CH4 contributed the largest warming effect, accounting for 57.43%. Correlation analysis showed that geographical location, climate factors, and soil conditions collectively regulated GHG emissions from wetlands. The findings provide a new perspective on sustainable wetland management and reducing GHG emissions.