Simultaneous methane mitigation and nitrogen removal by denitrifying anaerobic methane oxidation in lake sediments.

Methane (CH4) is a potent greenhouse gas, with lake ecosystems significantly contributing to its global emissions. Denitrifying anaerobic methane oxidation (DAMO) process, mediated by NC10 bacteria and ANME-2d archaea, links global carbon and nitrogen cycles. However, their potential roles in mitigating methane emissions and removing nitrogen from lake ecosystems remain unclear. This study explored the spatial variations in activities of nitrite- and nitrate-DAMO and their functional microbes in Changdanghu Lake sediments (Jiangsu Province, China). The results showed that although the average abundance of ANME-2d archaea (5.0 × 106 copies g-1) was significantly higher than that of NC10 bacteria (2.1 × 106 copies g-1), the average potential rates of nitrite-DAMO (4.59 nmol 13CO2 g-1 d-1) and nitrate-DAMO (5.01 nmol 13CO2 g-1 d-1) showed no significant difference across all sampling sites. It is estimated that nitrite- and nitrate-DAMO consumed approximately 6.46 and 7.05 mg CH4 m-2 d-1, respectively, which accordingly achieved 15.07-24.95 mg m-2 d-1 nitrogen removal from the studied lake sediments. Statistical analyses found that nitrite- and nitrate-DAMO activities were both significantly related to sediment nitrate contents and ANME-2d archaeal abundance. In addition, NC10 bacterial and ANME-2d archaeal community compositions showed significant correlations with sediment organic carbon content and water depth. Overall, this study underscores the dual roles of nitrite- and nitrate-DAMO processes in CH4 mitigation and nitrogen elimination and their key environmental impact factors (sediment organic carbon and inorganic nitrogen contents, and water depth) in shallow lake, enhancing the understanding of carbon and nitrogen cycles in freshwater aquatic ecosystems.

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.

Sludge degradation, nutrient removal and reduction of greenhouse gas emission by a Chironomus-Azolla wastewater treatment cascade.

Wastewater treatment plants (WWTPs) are a point source of nutrients, emit greenhouse gases (GHGs), and produce large volumes of excess sludge. The use of aquatic organisms may be an alternative to the technical post-treatment of WWTP effluent, as they play an important role in nutrient dynamics and carbon balance in natural ecosystems. The aim of this study was therefore to assess the performance of an experimental wastewater-treatment cascade of bioturbating macroinvertebrates and floating plants in terms of sludge degradation, nutrient removal and lowering GHG emission. To this end, a full-factorial experiment was designed, using a recirculating cascade with a WWTP sludge compartment with or without bioturbating Chironomus riparius larvae, and an effluent container with or without the floating plant Azolla filiculoides, resulting in four treatments. To calculate the nitrogen (N), phosphorus (P) and carbon (C) mass balance of this system, the N, P and C concentrations in the effluent, biomass production, and sludge degradation, as well as the N, P and C content of all compartments in the cascade were measured during the 26-day experiment. The presence of Chironomus led to an increased sludge degradation of 44% compared to 25% in the control, a 1.4 times decreased transport of P from the sludge and a 2.4 times increased transport of N out of the sludge, either into Chironomus biomass or into the water column. Furthermore, Chironomus activity decreased methane emissions by 92%. The presence of Azolla resulted in a 15% lower P concentration in the effluent than in the control treatment, and a CO2 uptake of 1.13 kg ha-1 day-1. These additive effects of Chironomus and Azolla resulted in an almost two times higher sludge degradation, and an almost two times lower P concentration in the effluent. This is the first study that shows that a bio-based cascade can strongly reduce GHG and P emissions simultaneously during the combined polishing of wastewater sludge and effluent, benefitting from the additive effects of the presence of both macrophytes and invertebrates. In addition to the microbial based treatment steps already employed on WWTPs, the integration of higher organisms in the treatment process expands the WWTP based ecosystem and allows for the inclusion of macroinvertebrate and macrophyte mediated processes. Applying macroinvertebrate-plant cascades may therefore be a promising tool to tackle the present and future challenges of WWTPs.

Underestimation of Thermogenic Methane Emissions in New York City.

Recent studies have shown that methane emissions are underestimated by inventories in many US urban areas. This has important implications for climate change mitigation policy at the city, state, and national levels. Uncertainty in both the spatial distribution and sectoral allocation of urban emissions can limit the ability of policy makers to develop appropriately focused emission reduction strategies. Top-down emission estimates based on atmospheric greenhouse gas measurements can help to improve inventories and inform policy decisions. This study presents a new high-resolution (0.02 × 0.02°) methane emission inventory for New York City and its surrounding area, constructed using the latest activity data, emission factors, and spatial proxies. The new high-resolution inventory estimates of methane emissions for the New York-Newark urban area are 1.3 times larger than those for the gridded Environmental Protection Agency inventory. We used aircraft mole fraction measurements from nine research flights to optimize the high-resolution inventory emissions within a Bayesian inversion. These sectorally optimized emissions show that the high-resolution inventory still significantly underestimates methane emissions within the New York-Newark urban area, primarily because it underestimates emissions from thermogenic sources (by a factor of 2.3). This suggests that there remains a gap in our process-based understanding of urban methane emissions.

Contrasting impacts of fertilization on topsoil and subsoil greenhouse gas fluxes in a thinned Chinese fir plantation.

Globally, forest soils are considered as important sources and sinks of greenhouse gases (GHGs). However, most studies on forest soil GHG fluxes are confined to the topsoils (above 20 cm soil depths), with only very limited information being available regarding these fluxes in the subsoils (below 20 cm soil depths), especially in managed forests. This limits deeper understanding of the relative contributions of different soil depths to GHG fluxes and global warming potential (GWP). Here, we used a concentration gradient-based method to comprehensively investigate the effects of thinning intensity (15% vs. 35%) and nutrient addition (no fertilizer vs. NPK fertilizers) on soil GHG fluxes from the 0-40 cm soil layers at 10 cm depth intervals in a Chinese fir (Cunninghamia lanceolata) plantation. Results showed that forest soils were the sources of CO2 and N2O, but the sinks of CH4. Soil GHG fluxes decreased with increasing soil depth, with the 0-20 cm soil layers identified as the dominant producers of CO2 and N2O and consumers of CH4. Thinning intensity did not significantly affect soil GHG fluxes. However, fertilization significantly increased CO2 and N2O emissions and CH4 uptake at 0-20 cm soil layers, but decreased them at 20-40 cm soil layers. This is because fertilization alleviated microbial N limitation and decreased water filled pore space (WFPS) in topsoils, while it increased WFPS in subsoils, ultimately suggesting that soil WFPS and N availability (especially NH4+-N) were the predominant regulators of GHG fluxes along soil profiles. Generally, there were positive interactive effects of thinning and fertilization on soil GHG fluxes. Moreover, the 35% thinning intensity without fertilization had the lowest GWP among all treatments. Overall, our results suggest that fertilization may not only cause depth-dependent effects on GHG fluxes within soil profiles, but also impede efforts to mitigate climate change by promoting GHG emissions in managed forest plantations.

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.

Ration particle size has different effects on digestive but not production parameters in higher-yielding (Holstein) compared to lower-yielding (Girolando) cows.

The aim of the study was to evaluate the effect of total mixed ration particle size (length) and breed of cow on intake dynamics, animal performance and CH4 emissions, comparing high yielding Holstein and low yielding Girolando cows. The experimental design was 2 × 2 Latin Square arranged as a crossover factorial scheme with two diets (short particle size, SPS and long particle size, LPS) and the two breed compositions. The design comprised two periods of 26 d each, where all data collection was performed at cow level. No influence of the particle size occurred for the passage rate, neutral detergent fiber digestibility, performance and milk composition, methane emissions or ruminal fermentation parameters. Girolando cows had greater dry matter intake (DMI) when fed SPS, while Holsteins had the same (P < 0.05). Girolando cows had lower dry matter digestibility when fed LPS compared to SPS, while Holsteins had the opposite effect (P < 0.05). Also, the digestibility of crude protein and non-fibrous carbohydrates decreased in Girolando cows fed LPS, but not in Holsteins (P < 0.05). Girolando cows reduced DMI by 10.6% when fed LPS diet (P < 0.05). Girolando had an increased eating rate (+24 g of DM/min; P < 0.05) compared to Holstein cows, but Holstein cows had a lower CH4 intensity (by 29.7%: P < 0.05). Girolando cows increased the dry matter intake when fed a diet with short particle size, while the same did not happen in Holsteins. Dry matter digestibility increased in Holsteins when fed long particle size, while the opposite was observed in Girolando cows. Nutrient digestibility was reduced in Girolando cows when fed short particle size. Particle size did not influence eating time, eating rate, feed trough visits, visits with intake, milk yield and composition regardless of the breed. Reducing particle size increased CH4 intensity in both breeds.

Unrecognized extensive charge of microbial gas in the Junggar basin.

Different from the Qaidam basin with about 320 billion m3 microbial gas, only limited microbial gases were found from the Junggar basin with similarly abundant type III kerogen. To determine whether microbial gases have not yet identified, natural gas samples from the Carboniferous to Cretaceous in the Junggar basin have been analyzed for chemical and stable isotope compositions. The results reveal some of the gases from the Mahu sag, Zhongguai, Luliang and Wu-Xia areas in the basin may have mixed with microbial gas leading to straight ethane to butane trends with a "dogleg" light methane in the Chung's plot. Primary microbial gas from degradation of immature sedimentary organic matter is found to occur in the Mahu sag and secondary microbial gas from biodegradation of oils and propane occurred in the Zhongguai, Luliang and Beisantai areas where the associated oils were biodegraded to produce calcites with δ13C values from + 22.10‰ to + 22.16‰ or propane was biodegraded leading to its 13C enrichment. Microbial CH4 in the Mahu sag is most likely to have migrated up from the Lower Wuerhe Formation coal-bearing strata by the end of the Triassic, and secondary microbial gas in Zhongguai and Beisantan uplifts may have generated after the reservoirs were uplifted during the period of the Middle and Late Jurassic. This study suggests widespread distribution of microbial gas and shows the potential to find large microbial gas accumulation in the basin.

Prediction of Individual Gas Yields of Supercritical Water Gasification of Lignocellulosic Biomass by Machine Learning Models.

Supercritical water gasification (SCWG) of lignocellulosic biomass is a promising pathway for the production of hydrogen. However, SCWG is a complex thermochemical process, the modeling of which is challenging via conventional methodologies. Therefore, eight machine learning models (linear regression (LR), Gaussian process regression (GPR), artificial neural network (ANN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and categorical boosting regressor (CatBoost)) with particle swarm optimization (PSO) and a genetic algorithm (GA) optimizer were developed and evaluated for prediction of H2, CO, CO2, and CH4 gas yields from SCWG of lignocellulosic biomass. A total of 12 input features of SCWG process conditions (temperature, time, concentration, pressure) and biomass properties (C, H, N, S, VM, moisture, ash, real feed) were utilized for the prediction of gas yields using 166 data points. Among machine learning models, boosting ensemble tree models such as XGB and CatBoost demonstrated the highest power for the prediction of gas yields. PSO-optimized XGB was the best performing model for H2 yield with a test R2 of 0.84 and PSO-optimized CatBoost was best for prediction of yields of CH4, CO, and CO2, with test R2 values of 0.83, 0.94, and 0.92, respectively. The effectiveness of the PSO optimizer in improving the prediction ability of the unoptimized machine learning model was higher compared to the GA optimizer for all gas yields. Feature analysis using Shapley additive explanation (SHAP) based on best performing models showed that (21.93%) temperature, (24.85%) C, (16.93%) ash, and (29.73%) C were the most dominant features for the prediction of H2, CH4, CO, and CO2 gas yields, respectively. Even though temperature was the most dominant feature, the cumulative feature importance of biomass characteristics variables (C, H, N, S, VM, moisture, ash, real feed) as a group was higher than that of the SCWG process condition variables (temperature, time, concentration, pressure) for the prediction of all gas yields. SHAP two-way analysis confirmed the strong interactive behavior of input features on the prediction of gas yields.

The dynamics of nitrous oxide and methane emissions from various types of dairy manure at smallholder dairy farms as affected by storage periods.

Storing manure emits greenhouse gas (GHG) emissions, including nitrous oxide (N2O) and methane (CH4). However, the emissions from types of manure stored at smallholder dairy farms remains unknown. Hence, the study aims to analyse the dynamics of N2O and CH4 from different types of dairy manure as affected by storage periods. We collected samples from fresh manure (FM-DF1), manure from communal ponds in an urban dairy farm (IP-DF1, FP-DF1, MS-DF1), fresh manure from an urban dairy farm (FM-DF2), and fresh (FM-DF3), separated (FS-DF3), and fermented manure (FR-DF3) from a peri-urban dairy farm, and stored them for eight weeks and analyse them using the closed chamber method. The changes of manure composition including total solids (TS), nitrogen (N), ammonia-nitrogen (N-NH3), and carbon (C) were analysed. Results indicated an increase TS in all treatments except for MS-DF1, while N, N-NH3, and C content decreased in all treatments. The N2O emissions formed at the start, peaked in the middle, and declined towards the end storage period. The CH4 emissions peaked at the start and decreased until the end storage period. Treatment FM-DF2 yield highest cumulative of N2O (0.82 g/m2) and CH4 (41.63 g/m2) compared to other fresh manure treatment. A mixed model analysis detected a significant interaction (p < 0.05) between manure types and storage periods. In conclusion, manure types and storage periods affect the emissions. Changes in manure concentration during storage and animal diets are two important factors influencing emissions. Strategies to reduce emissions include reducing moisture content in manure, shortening storage periods, and improving feed quality.

Enhanced anaerobic co-digestion of cattle manure with food waste and pig manure: Statistical optimization of pretreatment condition and substrate mixture ratio.

This study investigated the optimal pretreatment condition and mixture ratio of cattle manure (CM) for its efficient anaerobic co-digestion (AcoD) with food waste (FW) and pig manure (PM). The pretreatment performances of thermal (TM), microwave (MW), and ultrasound (US) technologies and the AcoD performance were statistically and experimentally evaluated at various mixture ratios of CM, FW, and PM. The results revealed that the most effective pretreatment condition with the TM, MW, and US pretreatments was 129.3 °C for 49.6 min, 824.2 W for 7.3 min, and 418.0 W for 36.3 min, respectively. The best AcoD performance of optimally pretreated CM (PCM) was achieved when 30.5 % PCM was mixed with 42.5 % FW and 27.0 % PM. A long-term evaluation showed that the start-up rate for the anaerobic mono-digestion of PCM was 2.3 times faster than that of CM and the amount of methane produced was 4.7 times higher; process stability was thus preferentially maintained under a higher organic loading rate (OLR) (2.0 kg-VS/m3∙d). The start-up rate for the AcoD of PCM with FW and PM was 1.2 times higher than that of the AcoD of CM with FW and PM. Although the performance gap between the AcoD reactors after steady state was not significantly different, the PCM AcoD reactor provided a more stable operation under a higher OLR (5.0 kg-VS/m3∙d). This study demonstrates that the pretreatment and co-digestion of CM could significantly enhance the production of biogas and improve process stability.

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.

Synergy effect of silicate fertilizer and iron slag: A sustainable approach for mitigating methane emission in rice farming.

Although silicate fertilizer has been recently recognized for its ability to suppress methane (CH4) emissions in paddy fields, the effects of its consecutive application during the rice farming period are still a subject of debate. Moreover, while it was known that silicate fertilizer can mitigate CH4 emissions through several electron acceptors, the effect of additional application of electron acceptors have not been extensively studied. This study evaluated the effect of silicate fertilizer with varying concentrations of iron slag on CH4 emissions and rice yield over the 3 years rice farming period. Seasonal CH4 fluxes exhibited a significant decrease with the application of silicate fertilizer, with the treatment containing 2.5 % iron slag showing the maximum reduction of 35 % in 2020. Additionally, in 2021 and 2022, the application of silicate fertilizer with 2.5 % iron slag resulted in a decrease of total seasonal CH4 emission by 22 % and 23 %, respectively. Rice grain yield exhibited a significant increase with the inclusion of iron slag in the silicate fertilizer, which resulted in a 37 % and 16 % higher yield compared to no-silicate fertilization and no­iron slag silicate fertilization, respectively. Therefore, iron slag-based silicate fertilizer could be a beneficial soil amendment to mitigate CH4 emissions in rice paddy fields and improve rice productivity without negative effects on the atmospheric and soil ecosystem.

Soil greenhouse gas emissions from dead and natural mangrove forests in Southeastern Brazil.

Mangroves forests may be important sinks of carbon in coastal areas but upon their death, these forests may become net sources of carbon dioxide (CO2) and methane (CH4) to the atmosphere. Here we assessed the spatial and temporal variability in soil CO2 and CH4 fluxes from dead mangrove forests and paired intact sites in SE-Brazil. Our findings demonstrated that during warmer and drier conditions, CO2 soil flux was 183 % higher in live mangrove forests when compared to the dead mangrove forests. Soil CH4 emissions in live forests were > 1.4-fold higher than the global mangrove average. During the wet season, soil GHG emissions dropped significantly at all sites. During warmer conditions, mangroves were net sources of GHG, with a potential warming effect (GWP100) of 32.9 ± 10.2 (±SE) Mg CO2e ha-1 y-1. Overall, we found that dead mangroves did not release great amounts of GHG after three years of forest loss.

Complete long-term monitoring of greenhouse gas emissions from a full-scale industrial wastewater treatment plant with different cover configurations.

The greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs), consisting mainly of methane (CH4) and nitrous oxide (N2O), have been constantly increasing and become a non-negligible contributor towards carbon neutrality. The precise evaluation of plant-specific GHG emissions, however, remains challenging. The current assessment approach is based on the product of influent load and emission factor (EF), of which the latter is quite often a single value with huge uncertainty. In particular, the latest default Tier 1 value of N2O EF, 0.016 ± 0.012 kgN2O-N kgTN-1, is estimated based on the measurement of 30 municipal WWTPs only, without involving any industrial wastewater. Therefore, to resolve the pattern of GHG emissions from industrial WWTPs, this work conducted a 14-month monitoring campaign covering all the process units at a full-scale industrial WWTP in Shanghai, China. The total CH4 and N2O emissions from the whole plant were, on average, 447.7 ± 224.5 kgCO2-eq d-1 and 1605.3 ± 2491.0 kgCO2-eq d-1, respectively, exhibiting a 5.2- or 3.9-times more significant deviation than the influent loads of chemical oxygen demand (COD) or total nitrogen (TN). The resulting EFs, 0.00072 kgCH4 kgCOD-1 and 0.00211 kgN2O-N kgTN-1, were just 0.36% of the IPCC recommended value for CH4, and 13.2% for N2O. Besides, the parallel anoxic-oxic (A/O) lines of this industrial WWTP were covered in two configurations, allowing the comparison of GHG emissions from different odor control setup. Unit-specific analysis showed that the replacement of enclosed A/open O with enclosed A/O reduced the CH4 EF by three times, from 0.00159 to 0.00051 kgCH4 kgCOD-1, and dramatically decreased the N2O EF by an order of magnitude, from 0.00376 to 0.00032 kgN2O-N kgTN-1, which was among the lowest of all full-scale WWTPs.

Quantifying Methane Influx from Sewer into Wastewater Treatment Processes.

Wastewater treatment contributes substantially to methane (CH4) emissions, yet monitoring and tracing face challenges because the treatment processes are often treated as a "black box". Particularly, despite growing interest, the amount of CH4 carryover and influx from the sewer and its impacts on overall emissions remain unclear. This study quantified CH4 emissions from six wastewater treatment plants (WWTPs) across China, utilizing existing multizonal odor control systems, with a focus on Beijing and Guiyang WWTPs. In the Beijing WWTP, almost 90% of CH4 emissions from the wastewater treatment process were conveyed through sewer pipes, affecting emissions even in the aerobic zone of biological treatment. In the Guiyang WWTP, where most CH4 from the sewer was released at the inlet well, a 24 h online monitoring revealed CH4 fluctuations linked to neighborhood water consumption and a strong correlation to influent COD inputs. CH4 emission factors monitored in six WWTPs range from 1.5 to 13.4 gCH4/kgCODrem, higher than those observed in previous studies using A2O technology. This underscores the importance of considering CH4 influx from sewer systems to avoid underestimation. The odor control system in WWTPs demonstrates its potential as a cost-effective approach for tracing, monitoring, and mitigating CH4.

Including Methane Emissions from Agricultural Ponds in National Greenhouse Gas Inventories.

Agricultural ponds are a significant source of greenhouse gases, contributing to the ongoing challenge of anthropogenic climate change. Nations are encouraged to account for these emissions in their national greenhouse gas inventory reports. We present a remote sensing approach using open-access satellite imagery to estimate total methane emissions from agricultural ponds that account for (1) monthly fluctuations in the surface area of individual ponds, (2) rates of historical accumulation of agricultural ponds, and (3) the temperature dependence of methane emissions. As a case study, we used this method to inform the 2024 National Greenhouse Gas Inventory reports submitted by the Australian government, in compliance with the Paris Agreement. Total annual methane emissions increased by 58% from 1990 (26 kilotons CH4 year-1) to 2022 (41 kilotons CH4 year-1). This increase is linked to the water surface of agricultural ponds growing by 51% between 1990 (115 kilo hectares; 1,150 km2) and 2022 (173 kilo hectares; 1,730 km2). In Australia, 16,000 new agricultural ponds are built annually, expanding methane-emitting water surfaces by 1,230 ha yearly (12.3 km2 year-1). On average, the methane flux of agricultural ponds in Australia is 0.238 t CH4 ha-1 year-1. These results offer policymakers insights into developing targeted mitigation strategies to curb these specific forms of anthropogenic emissions. For instance, financial incentives, such as carbon or biodiversity credits, can mobilize widespread investments toward reducing greenhouse gas emissions and enhancing the ecological and environmental values of agricultural ponds. Our data and modeling tools are available on a free cloud-based platform for other countries to adopt this approach.

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.

Metagenomics reveals the variations in functional metabolism associated with greenhouse gas emissions during legume-vegetable rotation process.

Legume-based rotation is commonly recognized for its mitigation efficiency of greenhouse gas (GHG) emissions. However, variations in GHG emission-associated metabolic functions during the legume-vegetable rotation process remain largely uncharacterized. Accordingly, a soybean-radish rotation field experiment was designed to clarify the responses of microbial communities and their GHG emission-associated functional metabolism through metagenomics. The results showed that the contents of soil organic carbon and total phosphorus significantly decreased during the soybean-radish process (P < 0.05), while soil total potassium content and bacterial richness and diversity significantly increased (P < 0.05). Moreover, the predominant bacterial phyla varied, with a decrease in the relative abundance of Proteobacteria and an increase in the relative abundance of Acidobacteria, Gemmatimonadetes, and Chloroflexi. Metagenomics clarified that bacterial carbohydrate metabolism substantially increased during the rotation process, whereas formaldehyde assimilation, methanogenesis, nitrification, and dissimilatory nitrate reduction decreased (P < 0.05). Specifically, the expression of phosphate acetyltransferase (functional methanogenesis gene, pta) and nitrate reductase gamma subunit (functional dissimilatory nitrate reduction gene, narI) was inhibited, indicating of low methane production and nitrogen metabolism. Additionally, the partial least squares path model revealed that the Shannon diversity index was negatively correlated with methane and nitrogen metabolism (P < 0.01), further demonstrating that the response of the soil bacterial microbiome responses are closely linked with GHG-associated metabolism during the soybean-radish rotation process. Collectively, our findings shed light on the responses of soil microbial communities to functional metabolism associated with GHG emissions and provide important insights to mitigate GHG emissions during the rotational cropping of legumes and vegetables.

Greenhouse gas emissions from on-site sanitation systems: A systematic review and meta-analysis of emission rates, formation pathways and influencing factors.

Onsite sanitation systems (OSS) are significant sources of greenhouse gases (GHG) including carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). While a handful of studies have been conducted on GHG emissions from OSS, systematic evaluation of literature on this subject is limited. Our systematic review and meta-analysis provides state-of-the- art information on GHG emissions from OSS and identifies novel areas for investigation. The paper analyzes GHG emission rates from different OSS, the influence of various design, operational, and environmental factors on emission rates and proffers mitigation measures. Following the Preferred Reporting Items for Systematic reviews and Meta-analysis (PRISMA) guidelines, we identified 16 articles which quantified GHG emissions from OSS. Septic tanks emit substantial amounts of CO2 and CH4 ranging from 1.74 to 398.30 g CO2/cap/day and 0.06-110.13 g CH4/cap/day, respectively, but have low N2O emissions (0.01-0.06 g N2O/cap/day). CH4 emissions from pit latrines range from 0.77 to 20.30 g CH4/cap/day N2O emissions range from 0.76 to 1.20 gN2O/cap/day. We observed statistically significant correlations (p < 0.05) between temperature, biochemical oxygen demand, chemical oxygen demand, dissolved oxygen, storage period, and GHG emissions from OSS. However, no significant correlation (p > 0.05) was observed between soil volumetric water content and CO2 emissions. CH4 emissions (expressed as CO2 equivalents) from OSS estimated following Intergovernmental Panel for Climate Change (IPCC) guidelines were found to be seven times lower (90.99 g CO2e/cap/day) than in-situ field emission measurements (704.7 g CO2e/cap/day), implying that relying solely on IPCC guidelines may lead to underestimation of GHG emission from OSS. Our findings underscore the importance of considering local contexts and environmental factors when estimating GHG emissions from OSS. Plausible mitigation measures for GHG emissions from OSS include converting waste to biogas in anaerobic systems (e.g. biogas), applying biochar, and implementing mitigation policies that equally address inequalities in sanitation service access. Future research on GHG from OSS should focus on in-situ measurements of GHGs from pit latrines and other common OSS in developing countries, understanding the fate and transport of dissolved organics like CH4 in OSS effluents and impacts of microbial communities in OSS on GHG emissions. Addressing these gaps will enable more holistic and effective management of GHG emissions from OSS.