Predict Methane Emissions Research Articles

Prediction of methane emission and electricity generation from landfills: Deep learning approach

This paper presents a novel deep learning (DL) framework for predicting methane emissions from landfills by analyzing images of solid waste mixtures. The generation of renewable and sustainable electricity from landfills is a rapidly developing field that has gained considerable interest due to its potential to reduce greenhouse gas (GHG) emissions. Methane is a potent GHG produced during the decomposition of solid waste in landfills, significantly contributing to climate change. Precise estimation of methane emissions is crucial for efficient landfill management and the development of mitigation strategies. The framework is composed of four phases. During the initial phase, object detection identifies individual items within the landfill waste images. Subsequently, a hybrid Inception-ResNet-V2 DL method is suggested for categorizing the resulting waste image data into four classes: organic, paper and textile, wood, and food waste. In the next phase, the total weight of waste for each of the four classes is calculated utilizing a suggested method called waste weight estimation (WWE). Following that, methane production is estimated in the third phase using a proposed method (CH4PE), which relies on the weight values of waste mixtures. Finally, the fourth phase entails estimating electricity production using the electricity production estimation (EPE) method, which is based on the obtained methane weight. The validation findings revealed that the classification accuracy attained 93 %. Additionally, the framework's findings confirm a total estimated methane weight of 39,695.03 kg in the landfill case study, along with an estimated electricity production of 474,449.7 kW/month. This framework presents numerous advantages over conventional methods for predicting landfill-based methane emissions. It eliminates the necessity for labour-intensive manual waste classification and offers scalability, enabling application on large-scale landfill sites. This scalability facilitates the identification of high-emission areas and optimization of gas collection systems, fostering green and sustainable electricity production.

PSXII-14 Fecal near infrared reflectance spectroscopy for indirect prediction of methane emissions from grazing cattle

Abstract Methane (CH4) production from enteric fermentation of ruminants accounts for over a quarter of the greenhouse gas emissions from the agriculture sector in the U.S. Accurate and precise quantification of CH4 is essential to identify strategies for mitigating CH4 emissions. Numerous techniques and methodologies exist for measuring enteric CH4 emissions from ruminants, however, many were designed for specific research purposes, potentially limiting their applicability on commercial farms. Our objective was to evaluate the application of fecal near infrared reflectance spectroscopy (FNIRS) to provide inputs for indirect CH4 prediction calibrations. The Grazingland Animal Nutrition Laboratory (GAN Lab) has produced FNIRS predictions of grazing livestock diet quality since 1995 in conjunction with animal performance (weight/body condition change) generated by the Nutritional Balance Analyzer (Nutbal) Software package. FNIRS is a non-invasive approach that provides a significant opportunity to inform models for grazing cattle CH4 emissions. From a comprehensive U.S. dataset of approximately 30,000 samples, we selected a subset representing beef cattle grazing the gulf coast region of Texas from 2016 to 2019 (n = 85). Inputs of diet crude protein (CP), diet digestible organic matter (DOM), live weight (LW), and estimated live weight change (LWC) were used to predict feed intake (FI) employing a published equation and, from outputs generated by Nutbal, i.e. FI_Eq and FI_Nut, respectively. The FI (kg DM/animal.day) generated by both methods were applied to established prediction equations to generate preliminary estimates of CH4 emissions (CH4 FI_Eq and CH4 FI_Nut, respectively). Differences among estimations were assessed through ANOVA, with significance determined at P < 0.05. The estimated FI was different (P < 0.01) for both methodologies, resulting in different (P < 0.01) predictions of CH4 emissions. The CH4 prediction for CH4 FI_Eq was 0.24 ± 0.03 kg/animal.day, compared to 0.38± 0.06 kg/animal.day for CH4_Nut. A difference was observed between years for the CH4 predictions estimated with CH4 FI_Eq (P < 0.01). The FNIRS/Nutbal technique provided useful inputs to the established CH4 calibrations but will need to be validated against CH4 reference methods. The Gan Lab FNIRS/Nutbal dataset offers an opportunity to not only estimate U.S. grazing ruminant CH4 emissions over the past 30 years, but also predictions of current and future emissions at individual operation to national scales. This technique will enable producers with the knowledge to optimize forage resources management, mitigate CH4 emissions, and enhance the sustainability of grazing animal agriculture.

457 Prediction of methane emissions using rumination time and milk mid-infrared spectral data via artificial neural networks

Abstract Cattle methane emissions (ME) account for approximately 6% of global anthropogenic greenhouse gas emissions. Given the challenges in measuring ME directly from individual animals, there is a need for the development of novel indirect methods. Rumination time (RT) and milk mid-infrared spectral data (MIR) show promise for the indirect assessment of ME in dairy cows. Both traits have been used as indicators of reproduction, production, and gas emission traits. Methodologies combining the use of MIR and machine learning algorithms such as artificial neural networks (ANN) for the prediction of ME have been successful; however, the inclusion of RT has not been assessed. This study aimed to evaluate the impact of RT on milk MIR-based models using ANN for the prediction of ME. One-week averages for RT, ME, and MIR from first-lactation Canadian Holstein cows (n = 412) were calculated. Six data sets were evaluated using a multilayer perceptron ANN. All sets included age at calving, season of calving and days in milk as model factors, but varied in using milk MIR data points (1,060 or 235) and including or not including RT. The ANN architecture consisted of one input layer, one hidden layer with one or more neurons, and one output layer. Results showed that sets using both RT and milk MIR data achieved correlations from 0.5 to 0.6 between predicted and observed ME. Notably, the inclusion of RT did not improve the performance of the models. Predictions may be improved through the use of larger data sets, the use of daily records, and inclusion of data across herds and lactations. Optimizing parameters of the ANN could also improve predictions. Further research is needed to fully assess the potential of RT as a predictor of ME in dairy cows.

Solar‐Induced Chlorophyll Fluorescence as a Potential Proxy for Gross Primary Production and Methane Emission in a Cool‐Temperate Bog in Northern Japan

AbstractWetlands play an essential role in the global greenhouse gas budget via carbon dioxide sequestration as well as methane emission. In recent decades, solar‐induced chlorophyll fluorescence (SIF) has been recognized as a remotely sensed proxy of gross primary productivity (GPP), which generates substrates for methane production. To examine the suitability of SIF for estimation of these two fluxes, we conducted ground tower‐based SIF observation with an ultrafine‐resolution spectroradiometer in conjunction with eddy covariance measurement in a cool‐temperate bog. The daily SIF retrieved in the red (687 nm) and far‐red (760 nm) bands (SIFred and SIFfar‐red, respectively) increased nonlinearly with GPP and linearly with absorbed photosynthetically active radiation (APAR). The relatively weak correlation between apparent SIF yield (ΦSIF = SIF/APAR) and light use efficiency implied that both APAR and plant physiology constrained the SIF emission in this wetland. The SIFred/SIFfar‐red ratio showed a significant negative relationship with vegetation greenness indices, and the similar seasonal variation in SIFred and SIFfar‐red indicated that the SIFred reabsorption effect only weakly influenced the SIFred–GPP relationship. Episodic temporal reduction in the water table did not distinctly influence SIF and ΦSIF. Estimation of the methane emission rate was subtly improved by incorporating SIF, which was substituted for GPP as the methanogenesis substrate, in a multivariable regression analysis together with two environmental factors: soil temperature and water table depth. This study illustrates the potential of both SIFred and SIFfar‐red to monitor GPP and to predict methane emission in wetlands.

Review of equations to predict methane emissions in dairy cows from milk fatty acid profiles and their application to commercial dairy farms

Greenhouse gas emission from the activities of all productive sectors is currently a topic of foremost importance. The major contributors in the livestock sector are ruminants, especially dairy cows. This study aimed to evaluate and compare 21 equations for predicting enteric methane emissions (EME) developed on the basis of milk traits and fatty acid profiles, which were selected from 46 retrieved through a literature review. We compiled a reference database of the detailed fatty acid profiles, determined by GC, of 992 lactating cows from 85 herds under 4 different dairy management systems. The cows were classified according to DIM, parity order, and dairy system. This database was the basis on which we estimated EME using the selected equations. The EME traits estimated were methane yield (20.63 ± 2.26 g/kg DMI, 7 equations), methane intensity (16.05 ± 2.76 g/kg of corrected milk, 4 equations), and daily methane production (385.4 ± 68.2 g/d, 10 equations). Methane production was also indirectly calculated by multiplying the daily corrected milk yield by the methane intensity (416.6 ± 134.7 g/d, 4 equations). We also tested for the effects of DIM, parity, and dairy system (as a correction factor) on the estimates. In general, we observed little consistency among the EME estimates obtained from the different equations, with exception of those obtained from meta-analyses of a range of data from different research centers. We found all the EME predictions to be highly affected by the sources of variation included in the statistical model: DIM significantly affected the results of 19 of the 21 equations, and parity order influenced the results of 13. Different patterns were observed for different equations with only some of them in accordance with expectations based on the cow's physiology. Finally, the best predictions of daily methane production were obtained when a measure of milk yield was included in the equation or when the estimate was indirectly calculated from daily milk yield and methane intensity.

Dietary fat and carbohydrate-balancing the lactation performance and methane emissions in the dairy cow industry: A meta-analysis

For the agroecosystems of the dairy cow industry, dietary carbohydrate (starch, neutral detergent fiber [NDF]) and fat could directly affect rumen methane emissions and host energy utilization. However, the relationships among diet, lactation performance, and methane emissions need to be further determined to assist dairy farms to adjust diet formulations and feeding strategies for environmental and production management. A meta-analysis was conducted in the current study to explore quantitative patterns of dietary fat and carbohydrate at different levels in balancing lactation performance and environment sustainability of dairy cows, and to establish a methane emission prediction model using the artificial neural network (ANN) model. The results showed that the regression relationship between dietary fat, carbohydrate and methane emissions could be shown by the following models: methane = 106.78 + (14.86 × DMI), R2 = 0.80; methane = 443.17 – (46.41 × starch/NDF), R2 = 0.76; and methane = 388.91 + (31.40 × fat) – (5.42 × fat2), R2 = 0.80. The regression relationships between dietary fat, carbohydrate and lactation performance could be shown by the following models: milk fat yield = 1.08 + (0.43 × starch/NDF) – [0.34 × (starch/NDF)2], R2 = 0.79; milk protein yield = 0.68 + (0.15 × fat) – (0.016 × fat2), R2 = 0.82. In the structural equation model, we found that when formulating dietary carbohydrates and fats, it was necessary to balance the relationship between methane emissions and lactation performance. Specifically, dietary starch/NDF was lower than 0.63 (extremum point) and dietary fat was between 2.89% and 4.69% (extremum point), it could ensure that the aim of methane emission reduction (methane emissions decrease with increasing dietary starch/NDF and fat) was achieved without losing lactation performance of dairy cows (lactation performance increase with increasing dietary starch/NDF and fat). Finally, we established the ANN model to predict methane emissions (training set: R2 = 0.62; validation set: R2 = 0.61).

Climatic factors and fertilization rates co-regulate anaerobic methane oxidation driven by multiple electron acceptors in Chinese paddy fields

Paddy fields constitute a substantial anthropogenic reservoir of methane, with their inundation management fostering an ideal habitat for anaerobic methane oxidation. Within this context, a novel clade of anaerobic methanotrophic (ANME) archaea, known as ANME-2d, has been identified as capable of catalyzing anaerobic methane oxidation in conjunction with nitrate and metal oxide reduction processes within paddy fields. Nevertheless, our comprehension of the mechanisms governing anaerobic methane oxidation and its pivotal role in regulating methane emissions within rice paddies remains limited. This study quantified the rates of nitrate- and iron (III)-driven anaerobic methane oxidation through 13C-labeled stable isotope tracing experiments in Chinese paddy fields spanning diverse climate zones. Additionally, it investigated the ANME-2d archaeal community using quantitative polymerase chain reaction and high-throughput sequencing techniques. The nitrate-driven anaerobic methane oxidation contributed 10.9% to methane emission reduction. This contribution is equal to the previously identified contribution of nitrite-driven anaerobic methane oxidation (11.2%) mediated via NC10 bacteria, but played more important roles than iron-driven one (4.1%). The rates of nitrate- and nitrite-driven anaerobic methane oxidation differed significantly among climate zones and showed positive correlation with the mean annual temperature. Furthermore, their rates were more sensitive to temperature increases at higher and lower latitudes, respectively, under both representative concentration pathways 2.6 and 8.5. The rate of anaerobic methane oxidation driven by nitrate exhibited a positive correlation with nitrogen fertilization rate but displayed a negative correlation with phosphorus fertilization rate. Conversely, the rate of anaerobic methane oxidation driven by iron showed no significant correlation with either nitrogen or phosphorus fertilization rates. This study underscores the great potential of anaerobic methane oxidation in mitigating global warming, particularly under the conditions of future climate change and elevated nitrogen loading. These findings underline the necessity of incorporating anaerobic methane oxidation as a crucial parameter in methane emission prediction models.

N2 injection to enhance gas drainage in low-permeability coal seam: A field test and the application of deep learning algorithms

N2 injection to enhance coal seam gas drainage is an effective technology for coalbed methane recovery. Nevertheless, there is a limited body of research addressing the application of ECBM technology for gas management in low permeability coal seams within underground coal mines, and the effect is uncertain. Additionally, numerical simulators have been challenged to accurately predict methane emissions during gas injection and replacement. In this study, firstly, an underground field trials of N2 injection replacement in a coal mine with low-permeability coal seams in China were conducted, and a comparative analysis of “N2 injection to enhance gas drainage” and “conventional drainage” technique was performed. The result show that injecting pressurized N2 into a low-permeability coal seam could increase the pressure gradient to promote the directional flow of free gas from the coal seam cracks to the gas-production boreholes. After N2 injection, the mixed flow rate and CH4 flow rate significantly increased. Additionally, the gas content in the coal seam in the experimental area significantly decreased. Secondly, the lag-effect was discussed. Upon discontinuation of N2 injection, the gas-producing mixed flow initially increased slightly, then gradually decreased, and eventually settled at a high level. Despite the attenuation of N2 injection volume, the CH4 flow rate continued to increase during intermittent injection and remained high after gas injection discontinuation. Finally, three deep learning algorithms (BP, LSTM, and TCN) were employed to predict the dynamic change of gas drainage in coal seams displaced by N2 injection, based on the results of the field test. The results indicated that deep learning algorithms exhibited superior prediction performance. The TCN model demonstrated the lowest approximation error rate, displaying optimal performance in predicting the CH4 flow rate during nitrogen injection for enhanced gas drainage. Additionally, it exhibited good applicability to other similar field test results.

Unveiling microbial biomarkers of ruminant methane emission through machine learning.

Enteric methane from cow burps, which results from microbial fermentation of high-fiber feed in the rumen, is a significant contributor to greenhouse gas emissions. A promising strategy to address this problem is microbiome-based precision feed, which involves identifying key microorganisms for methane production. While machine learning algorithms have shown success in associating human gut microbiome with various human diseases, there have been limited efforts to employ these algorithms to establish microbial biomarkers for methane emissions in ruminants. In this study, we aim to identify potential methane biomarkers for methane emission from ruminants by employing regression algorithms commonly used in human microbiome studies, coupled with different feature selection methods. To achieve this, we analyzed the microbiome compositions and identified possible confounding metadata variables in two large public datasets of Holstein cows. Using both the microbiome features and identified metadata variables, we trained different regressors to predict methane emission. With the optimized models, permutation tests were used to determine feature importance to find informative microbial features. Among the regression algorithms tested, random forest regression outperformed others and allowed the identification of several crucial microbial taxa for methane emission as members of the native rumen microbiome, including the genera Piromyces, Succinivibrionaceae UCG-002, and Acetobacter. Additionally, our results revealed that certain herd locations and feed composition markers, such as the lipid intake and neutral-detergent fiber intake, are also predictive features for methane emissions. We demonstrated that machine learning, particularly regression algorithms, can effectively predict cow methane emissions and identify relevant rumen microorganisms. Our findings offer valuable insights for the development of microbiome-based precision feed strategies aiming at reducing methane emissions.

Prediction of methane emissions and electrical energy generation potential from MSW landfill in Khulna city of Bangladesh: a model-based approach

Prediction of methane emissions and electrical energy generation potential from MSW landfill in Khulna city of Bangladesh: a model-based approach

Quantification of Methane Emissions Rate Using Landgem Model and Estimating the Hydrogen Production Potential from Municipal Solid Waste Landfill Site

In India, solid waste is deposited mostly in uncontrolled open landfills without proper segregation and handling methods. Organic wastes dumped in a landfill undergo anaerobic decomposition and emit landfill gases like methane and carbon dioxide. Landfill gases are a significant contributor to greenhouse gases and greatly impact climate change. In the interim, reducing gas emissions and controlling and recycling such gasses is important from environmental hygienic, and global perspectives. Landfill gas has tremendous potential to convert as a source of alternative fuel. The present study estimates the CH4 (Methane) and CO2 (Carbon dioxide) emissions and quantifies the renewable energy available and hydrogen production potential using the LandGEM 3.02 empirical models for the Kanuru, Vijayawada landfill. It was observed that methane emission peaked in 2042 with an emission rate according to the model was 2.51E+08 Metric tons CO2 equivalents. The gas-recovery system is an essential component in landfills for extracting energy with 75-80% efficiency; the generation rate of greenhouse gases will reduce to around 1.78E06 Mg of CO2 eq. The predicted methane emissions vary from 1.33E6-9.22E6 cu.m per year for the period of 2010-2042. It was also estimated that annual energy production from LFG emissions was from 1.8-130 GWh per year, and hydrogen production potential was 0.6-43.3 Gg per year. The study concludes that projected scientific data will assist policymakers in creating sustainable MSW management by bridging the gap between sustainable renewable energy production and protecting the environment. The basic objectives of the study include the quantification of landfill gas production using the LandGEM model for Vijayawada, assessing the electricity generation potential of the landfill methane gas emitted, methane and carbon dioxide recovery from landfills with energy conversion could reduce GHG emissions, and estimation of hydrogen generation potential from the landfill methane emissions.

PSIX-21 Evaluation of Methane Emissions Predictions from Observed Methane Emissions Data in Beef Steers, Heifers and Bulls

Abstract Enteric methane emissions from cattle are the largest source of agricultural methane emissions; however, measuring enteric methane emissions is costly and infeasible in many environments. Consequently, prediction of enteric methane emissions for greenhouse gas emissions inventories for reporting and carbon market purposes is an important alternative to measurement. The objective of the current experiment was to evaluate the relationship between feed intake and methane emissions, and to compare observed methane emissions with commonly used enteric methane emissions prediction equations. Cattle (n = 194; steers n = 99, heifers n = 58, bulls n = 37) were housed at the Colorado State University Climate Smart Research pens equipped with Smartfeed feed intake measurement and Greenfeed emissions monitoring systems (C-Lock, Rapid City, SD). Body weight, body weight gain, dry matter intake, and methane emissions were collected for 37 days for each individual animal. All summary statistics and univariate fit analyses were conducted in JMP Pro 15.0 (SAS Institute Inc. Cary, NC). Observed methane emissions were compared with three extant methane prediction equations: IPCC tier 2 (IPCC), Moraes et al. (2014) animal-level (MAL), and Mills et al. (2003) non-linear equation 2 (MNL). Mean observed methane emissions were 185.3 g· animal-1· day-1 [standard deviation (SD) = 31.1], 135.6 g· animal-1· day-1 (SD = 29.3), and 177.8 g· animal-1· day-1 (SD = 20.1), for steers, heifers, and bulls, respectively. Across all 194 animals mean feed intake was 6.33 kg/d (SD = 1.65) and mean methane emissions were 169 g· animal-1· day-1 (SD = 36.2; Pearson correlation coefficient r = 0.65). When comparing observed vs. predicted methane emissions, MNL over predicted methane emissions (mean = 182.7 g· animal-1· day-1), whereas MAL (mean 119.3 g· animal-1· day-1) and IPCC (mean = 136.6 g· animal-1· day-1) underpredicted methane emissions (P < 0.01). Of the 3 prediction equations, MAL had the lowest root mean square error (RMSE) of 20.9 (R square = 0.50), followed by IPCC (RMSE = 27.2, R square = 0.42), and MNL (RMSE = 32.8, R square = 0.42). The current preliminary data suggests further refinement of enteric methane emissions prediction equations could improve inventories and estimates of enteric methane emissions on commercial cattle operations.

Pig slurry organic matter transformation and methanogenesis at ambient storage temperatures.

Manure management is a significant source of global methane emissions, and there is an increased interest in understanding and predicting emissions. The hydrolysis rate of manure organic matter is critical for understanding and predicting methane emissions. We estimated hydrolysis rate constants of crude protein, fibers, and lipids and used the Arrhenius equation to describe its dependency on temperature. Simultaneously, measurements of methane emission, 13/12 C isotope ratios, and methanogen community were conducted. This was achieved by incubating fresh pig manure without inoculum at 10°C, 15°C, 20°C, and 25°C for 85 days in a lab-scale setup. Hydrolysis of hemicellulose and cellulose increased more with temperature than crude protein, but still, hydrolysis rate of crude protein was highest at all temperatures. Results suggested that crude protein consisted of multiple substrate groups displaying large differences in degradability. Lipids and lignin were not hydrolyzed during incubations. Cumulative methane emissions were 7.13±2.69, 24.6±8.00, 66.7±4.8, and 105.7±7.14 gCH4 kgVS -1 at 10°C, 15°C, 20°C, and 25°C, respectively, and methanogenic community shifted from Methanosphaera toward Methanocorpusculum over time and more quickly at higher temperatures. This study provides important parameter estimates and dependencies on temperature, which is important in mechanistic methane emission models. Further work should focus on characterizing quickly degradable substrate pools in the manure organic matter as they might be the main carbon source of methane emission from manure management.

Predicting methane emissions of individual grazing dairy cows from spectral analyses of their milk samples

Data on the enteric methane emissions of individual cows are useful not just in assisting management decisions and calculating herd inventories but also as inputs for animal genetic evaluations. Data generation for many animal characteristics, including enteric methane emissions, can be expensive and time consuming, so being able to extract as much information as possible from available samples or data sources is worthy of investigation. The objective of the present study was to attempt to predict individual cow methane emissions from the information contained within milk samples, specifically the spectrum of light transmittance across different wavelengths of the mid-infrared (MIR) region of the electromagnetic spectrum. A total of 93,888 individual spot measures of methane (i.e., individual samples of an animal's breath when using the GreenFeed technology) from 384 lactations on 277 grazing dairy cows were collapsed into weekly averages expressed as grams per day; each weekly average coincided with a MIR spectral analysis of a morning or evening individual cow milk sample. Associations between the spectra and enteric methane measures were performed separately using partial least squares regression or neural networks with different tuning parameters evaluated. Several alternative definitions of the enteric methane phenotype (i.e., average enteric methane in the 6 d preceding or 6 d following taking the milk sample or the average of the 6 d before and after the milk sample, all of which also included the enteric methane emitted on the day of milk sampling), the candidate model features (e.g., milk yield, milk composition, and milk MIR) as well as validation strategy (i.e., cross-validation or leave-one-experimental treatment-out) were evaluated. Irrespective of the validation method, the prediction accuracy was best when the average of the milk MIR from the morning and evening milk sample was used and the prediction model was developed using neural networks; concurrently including milk yield and days in milk in the prediction model generated superior predictions relative to just the spectral information alone. Furthermore, prediction accuracy was best when the enteric methane phenotype was the average of at least 20 methane spot measures across a 6-d period flanking each side of the milk sample with associated spectral data. Based on the strategy that achieved the best accuracy of prediction, the correlation between the actual and predicted daily methane emissions when based on 4-fold cross-validation varied per validation stratum from 0.68 to 0.75; the corresponding range when validated on each of the 8 different experimental treatments focusing on alternative pasture grazing systems represented in the dataset varied from 0.55 to 0.71. The root mean square error of prediction across the 4-folds of cross-validation was 37.46 g/d, whereas the root mean square error averaged across all folds of leave-one-treatment-out was 37.50 g/d. Results suggest that even with the likely measurement errors contained within the MIR spectrum and gold standard enteric methane phenotype, enteric methane can be reasonably well predicted from the infrared spectrum of milk samples. What is yet to be established, however, is whether (a) genetic variation exists in this predicted enteric methane phenotype and (b) selection on estimates of genetic merit for this phenotype translate to actual phenotypic differences in enteric methane emissions.

Study on Characteristics and Model Prediction of Methane Emissions in Coal Mines: A Case Study of Shanxi Province, China

The venting of methane from coal mining is China’s main source of methane emissions. Accurate and up-to-date methane emission factors for coal mines are significant for reporting and controlling methane emissions in China. This study takes a typical coal mine in Shanxi Province as the research object and divides the coal mine into different zones based on the occurrence structure of methane in Shanxi Province. The methane emission characteristics of underground coal mine types and monitoring modes were studied. The emissions of methane from coal seams and ventilation methane of six typical coal mine groups in Shanxi Province were monitored. The measured methane concentration data were corrected by substituting them into the methane emission formula, and the future methane emissions were predicted by the coal production and methane emission factors. The results show that the number of methane mines and predicted reserves in Zone I of Shanxi Province are the highest. The average methane concentration emitted from coal and gas outburst mines is about 22.52%, and the average methane concentration emitted from high-gas mines is about 10.68%. The methane emissions from coal and gas outburst mines to the atmosphere account for about 64% of the total net methane emissions. The predicted methane emission factor for Shanxi coal mines is expected to increase from 8.859 m3/t in 2016 to 9.136 m3/t in 2025, and the methane emissions from Shanxi coal mines will reach 8.43 Tg in 2025.

Phenotypic and genetic characterisation of methane emission predicted from milk fatty acid profile of Sarda dairy ewes

Individual methane emissions are a potential breeding goal for selection plans aimed at improving the sustainability of ruminant farming systems. However, a large-scale recording of individual methane emissions is hampered by the high costs of the equipment and the logistics of the experiments. Equations have been developed to predict methane emissions in dairy cattle based on variables such as dry matter intake (DMI), gross energy intake, body weight and milk fatty acids (FAs) profile. No equations are currently available for dairy sheep. The aims of this study were: (i) to estimate the enteric methane yield (eMY, expressed as g/kg of DMI) and enteric methane intensity (eMI, expressed as g/kg of FPCM) of dairy sheep using equations developed for dairy cattle that consider as predictors milk FA; (ii) to evaluate the effect of stage of lactation, parity and month of lambing on eMY and eMI; (iii) to estimate the heritability of eMY and eMI. For this purpose, 964 individual milk samples from Sarda dairy ewes were analysed for milk composition and FA profile. Nine different equations were used to predict eMY and eMI. Values of eMY ranged from 19.4 g/kg to 20.4 g/kg of DMI, whereas values of eMI ranged from 15.1 g/kg to 21.0 g/kg of DMI, respectively. Stage of lactation was an important factor in the variability of eMY (p < .01). Indeed, this trait increased towards the end of lactation. Both eMY and eMI were negatively correlated with milk yield, lactose concentration and urea and positively correlated with fat concentration and NaCl, respectively. Heritability of eMI and eMY estimated using a single trait model was 0.13 ± 0.05 and 0.05 ± 0.04, respectively. The values of heritability estimated for eMY using a bivariate models ranged from 0.10 to 0.16 and they were higher than the value estimated for eMI. Results of the present study, even if based on prediction equations developed for cows, increased the knowledge about the phenotypic and genetic background of methane emissions in Sarda dairy sheep.

Assessing machine learning tools for methane emission prediction from POME treatment in Malaysia

Abstract Palm oil mill effluent (POME) treatment is an anthropogenic activity contributing to global warming through methane emission. The inability to address this issue would deem true the catastrophic impacts of global temperatures exceeding 2 °C as was predicted by the Intergovernmental Panel on Climate Change (IPCC) in 2015. Little research and development exist on GHGs monitoring and methane emissions in POME treatment facilities as opposed to research on improving biogas production. A methane emission prediction tool based on machine learning models and tools can address this problem and consequently facilitate the development of efficient carbon neutrality approaches in POME treatment plants. In this study, six regression models were explored alongside their kernels using eight predictors, linking towards methane emission volume. The best model found was support vector machine (SVM), producing performance metrics for R2 and RMSE with values of 0.45 and 0.749, respectively.

Factors Affecting Enteric Emission Methane and Predictive Models for Dairy Cows.

Enteric methane emission is the main source of greenhouse gas contribution from dairy cattle. Therefore, it is essential to evaluate drivers and develop more accurate predictive models for such emissions. In this study, we built a large and intercontinental experimental dataset to: (1) explain the effect of enteric methane emission yield (g methane/kg diet intake) and feed conversion (kg diet intake/kg milk yield) on enteric methane emission intensity (g methane/kg milk yield); (2) develop six models for predicting enteric methane emissions (g/cow/day) using animal, diet, and dry matter intake as inputs; and to (3) compare these 6 models with 43 models from the literature. Feed conversion contributed more to enteric methane emission (EME) intensity than EME yield. Increasing the milk yield reduced EME intensity, due more to feed conversion enhancement rather than EME yield. Our models predicted methane emissions better than most external models, with the exception of only two other models which had similar adequacy. Improved productivity of dairy cows reduces emission intensity by enhancing feed conversion. Improvement in feed conversion should be prioritized for reducing methane emissions in dairy cattle systems.

Predicting methane emissions and developing reduction strategies for a Central Appalachian Basin, USA, longwall mine through analysis and modeling of geology and degasification system performance

Coal mine methane is a safety concern in active mines due to explosion risk and an environmental concern due to the greenhouse gas (GHG) properties of methane emissions to the atmosphere. Depending on the mine design and operation, structural and stratigraphic characteristics of the geology, and the properties of coal beds affected by mining, a significant amount of methane can be released during coal extraction. These emissions may be low and uniform, but they also can be high and abrupt, if not captured by using pre- and post-mining methods of degasification or not controlled by ventilation during mining. Therefore, emissions should be monitored and predicted accurately for underground safety and GHG reduction. Ventilation and degasification systems should be designed accordingly by taking into account the mine geological properties and the degasification system's performance.This paper presents a comprehensive study to predict emissions and proposes alternatives to reduce emissions in a longwall mine extracting metallurgical coal from the Pocahontas No. 3 coal bed in Virginia (Central Appalachian Basin), USA. The work focused on mining activity in four adjacent panels through analysis and modeling of geology and evaluation of the performance of the methane control system. Results showed that the mine geology contained a significant amount of gas within and around the panel areas, which was controlled by utilizing different degasification methods besides ventilation during mining. The study showed that after pre-mining degasification using fractured vertical wells and in-seam horizontal wells, each panel potentially contained ∼19 MMscf and ∼ 2 MMscf of gas remaining to be handled by the gob gas ventholes (GGVs) and the ventilation, respectively, per acre of mining. It was shown that extending the pre-mining degasification duration of vertical wells by as much as 4 years or drilling more horizontal wells with closer spacing could significantly reduce ventilation and gob emissions during the mining of coal.

Prediction of methane emissions from fattening cattle using the methane-to-carbon dioxide ratio.

This study aimed to develop a prediction equation for methane (CH4 ) emissions from fattening cattle based on the CH4 /carbon dioxide (CO2 ) ratio and validate the predictive ability of the developed equation. The prediction equation was developed using the CH4 /CO2 ratio combined with oxygen consumption and respiratory quotient estimations that were theoretically calculated from the relation between gas emissions and energy metabolism. To validate the prediction equation, gas measurements in the headboxes were conducted using eight Japanese Black steers. The predictive ability of the developed equation was compared with that of two previously reported equations. As a result, the developed and reported equations had significant (P < 0.01) linear relationships between the observed and predicted CH4 emissions. Notably, only the developed equation had a significant (P < 0.01) linear relationship between the observed and predicted CH4 emissions when expressed per unit of dry matter intake. The results suggest that the developed prediction equation has a higher predictive ability than previously reported equations, particularly in evaluating the efficiency of CH4 emissions. Although further validation is required, the equation developed in this study can be a valuable tool for on-farm estimations of individual CH4 emissions from fattening cattle.