Methane Quantification Research Articles

Constructing a measurement-based spatially explicit inventory of US oil and gas methane emissions (2021)

Abstract. Accurate and comprehensive quantification of oil and gas methane emissions is pivotal in informing effective methane mitigation policies while also supporting the assessment and tracking of progress towards emissions reduction targets set by governments and industry. While national bottom-up source-level inventories are useful for understanding the sources of methane emissions, they are often unrepresentative across spatial scales, and their reliance on generic emission factors produces underestimations when compared with measurement-based inventories. Here, we compile and analyze previously reported ground-based facility-level methane emissions measurements (n=1540) in the major US oil- and gas-producing basins and develop representative methane emission profiles for key facility categories in the US oil and gas supply chain, including well sites, natural-gas compressor stations, processing plants, crude-oil refineries, and pipelines. We then integrate these emissions data with comprehensive spatial data on national oil and gas activity to estimate each facility's mean total methane emissions and uncertainties for the year 2021, from which we develop a mean estimate of annual national methane emissions resolved at 0.1° × 0.1° spatial scales (∼ 10 km × 10 km). From this measurement-based methane emissions inventory (EI-ME), we estimate total US national oil and gas methane emissions of approximately 16 Tg (95 % confidence interval of 14–18 Tg) in 2021, which is ∼ 2 times greater than the EPA Greenhouse Gas Inventory. Our estimate represents a mean gas-production-normalized methane loss rate of 2.6 %, consistent with recent satellite-based estimates. We find significant variability in both the magnitude and spatial distribution of basin-level methane emissions, ranging from production-normalized methane loss rates of < 1 % in the gas-dominant Appalachian and Haynesville regions to > 3 %–6 % in oil-dominant basins, including the Permian, Bakken, and the Uinta. Additionally, we present and compare novel comprehensive wide-area airborne remote-sensing data and results for total area methane emissions and the relative contributions of diffuse and concentrated methane point sources as quantified using MethaneAIR in 2021. The MethaneAIR assessment showed reasonable agreement with independent regional methane quantification results in sub-regions of the Permian and Uinta basins and indicated that diffuse area sources accounted for the majority of the total oil and gas emissions in these two regions. Our assessment offers key insights into plausible underlying drivers of basin-to-basin variabilities in oil and gas methane emissions, emphasizing the importance of integrating measurement-based data when developing high-resolution spatially explicit methane inventories in support of accurate methane assessment, attribution, and mitigation. The high-resolution spatially explicit EI-ME inventory is publicly available at https://doi.org/10.5281/zenodo.10734299 (Omara, 2024).

Methane Quantification Performance of the Quantitative Optical Gas Imaging (QOGI) System Using Single-Blind Controlled Release Assessment.

Quantitative optical gas imaging (QOGI) system can rapidly quantify leaks detected by optical gas imaging (OGI) cameras across the oil and gas supply chain. A comprehensive evaluation of the QOGI system's quantification capability is needed for the successful adoption of the technology. This study conducted single-blind experiments to examine the quantification performance of the FLIR QL320 QOGI system under near-field conditions at a pseudo-realistic, outdoor, controlled testing facility that mimics upstream and midstream natural gas operations. The study completed 357 individual measurements across 26 controlled releases and 71 camera positions for release rates between 0.1 kg Ch4/h and 2.9 kg Ch4/h of compressed natural gas (which accounts for more than 90% of typical component-level leaks in several production facilities). The majority (75%) of measurements were within a quantification factor of 3 (quantification error of -67% to 200%) with individual errors between -90% and 831%, which reduced to -79% to +297% when the mean of estimates of the same controlled release from multiple camera positions was considered. Performance improved with increasing release rate, using clear sky as plume background, and at wind speeds ≤1 mph relative to other measurement conditions.

Integrated microcontroller mq sensors for monitoring biogas: Advancements in methane and hydrogen sulfide detection

Recent technological advances in microcontroller systems enable novel biogas monitoring capabilities. This study investigates microcontroller-based quantification of methane and hydrogen sulfide concentrations in biogas derived from anaerobic digestion. Anaerobic digesters were fed either 100% cow dung substrates or a 50:50 mixture of cow dung with municipal solid waste (MSW). Methane levels were monitored using an MQ-4 sensor, hydrogen sulfide via an MQ-136 sensor, and temperature with a K-type thermocouple, all integrated with an ATmega 2560 microcontroller system. The 100% cow dung digester produced biogas with maximum methane concentrations of 3488 ppm at 21 days, indicating improved methane production compared to the 50:50 mixture of cow dung with MSW. Hydrogen sulfide reached 195 ppm and 192 ppm for the 100% cow dung and mixed digesters. Mesophilic temperature conditions were maintained throughout the digestion process. Real-time quantification of biogas composition demonstrates the capabilities of microcontroller-based anaerobic digester monitoring to provide precise methane and hydrogen sulfide measurements.

Field Testing of a Mixed Potential IoT Sensor Platform for Methane Quantification

Emissions of CH4 from natural gas infrastructure must urgently be addressed to mitigate its effect on global climate. With hundreds of thousands of miles of pipeline in the US used to transport natural gas, current methods of surveying for leaks are inadequate. Mixed potential sensors are a low cost, field deployable technology for remote and continuous monitoring of natural gas infrastructure. We demonstrate for the first time a field trial of a mixed potential sensor device coupled with machine learning and internet-of-things platform at Colorado State University’s Methane Emissions Technology Evaluation Center (METEC). Emissions were detected from a simulated buried underground pipeline source. Sensor data was acquired and transmitted from the field test site to a remote cloud server. Quantification of concentration as a function of vertical distance is consistent with previously reported transport modelling efforts and experimental surveys of methane emissions by more sophisticated CH4 analyzers.

Ground-to-UAV, laser-based emissions quantification of methane and acetylene at long standoff distances

Abstract. Determination of trace gas emissions from sources is critical for understanding and regulating air quality and climate change. Here, we demonstrate a method for rapid quantification of the emission rate of multiple gases from simple and complex sources using a mass balance approach with a spatially scannable open-path sensor – in this case, an open-path dual-comb spectrometer. The open-path spectrometer measures the total column density of gases between the spectrometer and a retroreflector mounted on an uncrewed aerial vehicle (UAV). By measuring slant columns at multiple UAV altitudes downwind of a source (or sink), the total emission rate can be rapidly determined without the need for an atmospheric dispersion model. Here, we demonstrate this technique using controlled releases of CH4 and C2H2. We show an emission rate determination to within 56 % of the known flux with a single 10 min flight and within 15 % of the known flux after 12 flights. Furthermore, we estimate the detection limit for CH4 emissions to be 0.03 g CH4 s−1. This detection limit is approximately the same as the emissions from 25 head of beef cattle and is less than the average emissions from a small oil field pneumatic controller. Other gases including CO2, NH3, HDO, ethane, formaldehyde (HCHO), CO, and N2O can be measured by simply changing the dual-comb spectrometer.

173 Awardee Talk: Spot Sample Protocol for Gas Quantification of Grazing Beef Cattle Using a Greenfeed

Abstract Through a natural digestive process called enteric fermentation, cattle produce greenhouse gases that are an environmental concern and an energetic loss. Gas fluxes can be quantified with several techniques; however, an open circuit gas quantification system (OCGQS) allows for unencumbered quantification of methane (CH4), carbon dioxide (CO2), and oxygen (O2) from grazing cattle. In the literature, there is wide variation in the minimum number of spot samples recommended to evaluate gas fluxes of an individual animal, while no recommendations have been made to evaluate metabolic heat production. The objective of this study was to characterize the minimum number of spot samples needed to accurately quantify metabolic heat production and CH4, CO2, and O2 gas fluxes in grazing beef cattle. A minimum of 100 spot samples each were collected from seventeen grazing animals using a OCGQS (GreenFeed, C-Lock, Inc). The mean gas fluxes were computed starting from the first 10 visits (forward) and increasing by increments of 10 until 100 visits was reached. Mean gas fluxes were also computed starting from visit 100 moving back in time in increments of 10 (reverse) using the same approach. Mean gas fluxes were used to calculate individual animal metabolic heat production for each interval with both the forward and reverse approaches. Pearson and Spearman correlations were computed between the full 100 visits and each shortened visit interval. The minimum number of spot samples was determined when correlations with the full 100 visits were greater than 0.95. The 0.95 correlation threshold was met between 30 and 40 visits for all gas fluxes and metabolic heat production. Therefore, mean forward and reverse gas fluxes and metabolic heat production were also computed starting at 30 visits and increasing by 2 until 40 visits and vice versa. The results indicated that the minimum number of spot samples needed for accurate quantification of CH4, CO2, and O2 gas fluxes are 38, 40, and 40, respectively. Using gas fluxes collected by the OCGQS, metabolic heat production can be calculated after 36 spot samples. The practical calculation of metabolic heat production will require 40 spot samples given the necessary number of spot samples for component gases in the calculation. Animals met the required number of spot samples for quantification of metabolic heat production, CH4, CO2, and O2 in 29.5 ± 8.7, 30.5 ± 9.1, 31.8 ± 9.2, and 31.8 ± 9.2 days, respectively. Large variation existed around the number of days needed to achieve the recommended number of spot samples. For this reason, protocols for the OCGQS should be based on the total number of spot samples, rather than a test duration.

S2MetNet: A novel dataset and deep learning benchmark for methane point source quantification using Sentinel-2 satellite imagery

Methane, as a crucial greenhouse gas, plays a significant role in global warming, contributing to approximately one-quarter of the observed climate change since pre-industrial times. Consequently, the detection and quantification of major methane emitters are vital in addressing this issue effectively. Satellite sensors with shortwave infrared (SWIR) spectral bands provide valuable information for monitoring methane emissions. For example, Sentinel-2 multispectral data have the capability to detect methane plumes of large point sources. As such, a wide range of quantification approaches have been developed to quantify methane source rates based on this dataset. Most of the existing methods, however, require auxiliary data, such as wind speed, and have large uncertainties. In this study, we introduce a novel approach based on deep learning models to enhance the precision of methane quantification using Sentinel-2 data without the reliance on external data sources. To train the proposed deep learning model, a comprehensive benchmark dataset has been generated, using Sentinel-2 data. This dataset is created by integrating simulated plumes and background noise extracted from real Sentinel-2 images. This approach ensures the integration of realistic environmental conditions within the simulated data, enhancing the robustness and reliability of our proposed model. The generated benchmark dataset is utilized in different deep learning architectures, namely VGG-19, ResNet-50, Inception-v3, DenseNet-121, Swin-T, and EfficientNet-V2L, to estimate methane source rate. The performance of deep models has been evaluated in three learning strategies, namely from scratch, transfer-learning, and fine-tuning. The fine-tuned EfficientNet-V2L achieves the highest accuracy with root-mean-square error (RMSE), mean absolute percentage error (MAPE), and Pearson R of 2101 kg h−1, 10.05%, and 95.70%, respectively. More importantly, the proposed model demonstrates superior performance compared to conventional physical-based quantification methods (e.g., integrated mass enhancement) and recently developed deep learning model techniques (e.g., MethaNet). In particular, the proposed model exhibits an improvement of approximately 1287 kg h−1 in terms of RMSE, a 3.92% reduction in MAPE, and a 5.01% enhancement in R compared to the IME method. These results highlight the advancements achieved by the proposed approach in accurately quantifying methane emissions using Sentinel-2 imagery. The generated benchmark dataset and the developed deep learning model presented in this study serve as a fundamental resource and constructive framework for future research, promoting extensive implementation across various methane monitoring scenarios on different satellites and in distinct geographic regions, which delivering greater effectiveness to global methane emission monitoring initiatives.

Controlled-release testing of the static chamber methodology for direct measurements of methane emissions

Abstract. Direct measurements of methane emissions at the component level provide the level of detail necessary for the development of actionable mitigation strategies. As such, there is a need to understand the magnitude of component-level methane emission sources and to test methane quantification methods that can capture methane emissions at the component level used in national inventories. The static chamber method is a direct measurement technique that has been applied to measure large and complex methane sources, such as oil and gas infrastructure. In this work, we compile methane emission factors from the Intergovernmental Panel on Climate Change (IPCC) Emission Factor Database in order to understand the magnitude of component-level methane flow rates, review the tested flow rates and measurement techniques from 40 controlled-release experiments, and perform 64 controlled-release tests of the static chamber methodology with mass flow rates of 1.02, 10.2, 102, and 512 g h−1 of methane. We vary the leak properties, chamber shapes, chamber sizes, and use of fans to evaluate how these factors affect the accuracy of the static chamber method. We find that 99 % of the component-level methane emission rates from the IPCC Emission Factor Database are below 100 g h−1 and that 77 % of the previously available controlled-release experiments did not test for methane mass flow rates below 100 g h−1. We also find that the static chamber method quantified methane flow rates with an overall accuracy of +14/-14 % and that optimal chamber configurations (i.e., chamber shape, volume, and use of fans) can improve accuracy to below ±5 %. We note that smaller chambers (≤20 L) performed better than larger-volume chambers (≥20 L), regardless of the chamber shape or use of fans. However, we found that the use of fans can substantially increase the accuracy of larger chambers, especially at higher methane mass flow rates (≥100 g h−1). Overall, our findings can be used to engineer static chamber systems for future direct measurement campaigns targeting a wide range of sources, including landfills, sewerage utility holes, and oil and natural gas infrastructure.

Open-path dual-comb spectroscopy of methane and VOC emissions from an unconventional oil well development in Northern Colorado.

We present results from a field study monitoring methane and volatile organic compound emissions near an unconventional oil well development in Northern Colorado from September 2019 to May 2020 using a mid-infrared dual-comb spectrometer. This instrument allowed quantification of methane, ethane, and propane in a single measurement with high time resolution and integrated path sampling. Using ethane and propane as tracer gases for methane from oil and gas activity, we observed emissions during the drilling, hydraulic fracturing, millout, and flowback phases of well development. Large emissions were seen in drilling and millout phases and emissions decreased to background levels during the flowback phase. Ethane/methane and propane/methane ratios varied widely throughout the observations.

Review Spotlights Methane Detection, Quantification Technologies

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 205166, “Deployment of Methane Detection and Quantification Technologies,” by Noor A.A. Talip, Mohd H.M. Pikri, and Shahrul A.Z. Abidin, Petronas, et al. The paper has not been peer reviewed. _ In supporting the energy transition, a need exists to improve accurate quantification and reduction of methane emissions and determine long-term reduction targets to drive the competitive advantages of natural gas as a low-carbon fuel. The complete paper reviews an initiative taken to measure baseline data for methane emissions for gas-processing facilities and gas-transmission and regasification units through the use of accurate measurement tools and methodologies. Introduction A methane quantification tool has been developed in-house by Petronas, with two of the operator’s business units as the first users. Methane emission is quantified based on methane sources listed in the Oil and Gas Climate Initiative Guidelines. The quantification covers the following methane-emission sources based on applicability to sample plants: - Unintended release (fugitive leaks, loss of primary containment) - Intended release (venting and flaring of hydrocarbon, stationary combustion, compressor seals, and pneumatic controls and pumps) Methane-emission volume from fugitive leaks, compressor seals, and pneumatic devices had not been quantified accurately by the operator until before the development of its methane-quantification tool. Much of the complete paper is dedicated to describing methodologies used to detect and quantify methane emissions; this synopsis identifies those methodologies but directs the reader to the complete paper for fuller discussions thereof, as well as equations used in those methodologies. Methodology Fugitive Leaks. Global fugitive-emission standards for quantification are nonbinding, regardless of regulations or policies. Nevertheless, the oil and gas industry is still working to develop regulations for methane-emission measurements for effective monitoring and, ultimately, setting the targets for emission reduction. The choice of methods to estimate emission depends on how the estimates will be used and the degree of accuracy required. Methods using site-specific data from direct measurement are preferred. Table 1 of the complete paper shows the fugitive-emission-quantification methodologies sorted into inclining order with respect to accuracy. The methodology-complexity level can be depicted per the terminology in Fig. 1. The five methods are described by the authors, and detailed in the complete paper, as the following: - Method 1: Facility-/plant-level average emission factors - Method 2: Equipment-level average emission factors - Method 3: Device-component-level average emission factors - Method 4: Alternative-leak/no-leak emission factors for optical-gas-imaging technologies - Method 5: Screened value correlation approach

Methane, Ethylene, and Ethane Detection by Differential Helmholtz Resonance Spectroscopy Using a 3345 nm Mid-Infrared Tunable Diode Laser Source

Gas detection based on photoacoustic spectroscopy (PAS) has attracted extensive attention due to its high sensitivity and large range of linearity. Herein, to achieve the simultaneous detection of the light carbon gases (CH4, C2H4, and C2H6), a gas detection system was constructed using a single mid-infrared tunable diode laser (central wavelength 3345 nm) source based on differential Helmholtz resonance spectroscopy (DHRS). The detection parameters (driving current, modulation depth, phase angle, etc.) were optimized under ambient pressure using the 1f demodulation method. With an integration time of 2 s, the detection limit reached 98.8 ppb, 252 ppb, and 33 ppb for methane, ethylene, and ethane, respectively. The validation test of the three-component mixture shows that the cross-interference can be effectively reduced by multiwavelength linear regression, and single wavelength linear regression causes large errors in the quantification of methane and ethylene.

Characterization of the number of spot samples required for quantification of gas fluxes and metabolic heat production from grazing beef cows using a GreenFeed.

Enteric fermentation from cattle results in greenhouse gas production that is an environmental concern and also an energetic loss. Several methods exist to quantify gas fluxes; however, an open circuit gas quantification system (OCGQS) allows for unencumbered quantification of methane (CH4), carbon dioxide (CO2), and oxygen (O2) from grazing cattle. While previous literature has proven the accuracy of an OCGQS, little work has been done to establish the minimum number of spot samples required to best evaluate an individual grazing animal's gas fluxes and metabolic heat production. A GreenFeed system (C-Lock Inc.) was used to collect at least 100 spot samples each from 17 grazing cows. The mean gas fluxes and metabolic heat production were computed starting from the first 10 visits (forward) and increasing by increments of 10 until an animal had 100 visits. Mean gas fluxes and metabolic heat production were also computed starting from visit 100 (reverse) in increments of 10 using the same approach. Pearson and Spearman correlations were computed between the full 100 visits and each shortened visit interval. A large increase in correlations were seen between 30 and 40 visits. Thus, mean forward and reverse gas fluxes and metabolic heat production were also computed starting at 30 visits and increasing by 2 until 40 visits. The minimum number of spot samples was determined when correlations with the full 100 visits were greater than 0.95. The results indicated that the minimum numbers of spot samples needed for accurate quantification of CH4, CO2, and O2 gas fluxes are 38, 40, and 40, respectively. Metabolic heat production can be calculated using gas fluxes collected by the OCGQS with 36 spot samples. Practically, calculation of metabolic heat production will require 40 spot samples because the component gases for metabolic heat calculation require up to 40 spot samples. Published literature from nongrazing (confined) environments recommended a similar number of total spot samples. Large variation existed around the average number of spot samples for an animal per day, therefore a wide range of test durations may be needed to meet the same number of spot samples in different populations. For this reason, protocols for the OCGQS should be based on the total number of spot samples, rather than a test duration.

Quantification of methane and carbon dioxide emissions from an active landfill: study the effect of surface conditions on emissions

Quantification of methane and carbon dioxide emissions from an active landfill: study the effect of surface conditions on emissions

Machine learning techniques to increase the performance of indirect methane quantification from a single, stationary sensor

Researchers are searching for ways to better quantify methane emissions from natural gas infrastructure. Current indirect quantification techniques (IQTs) allow for more frequent or continuous measurements with fewer personnel resources than direct methods but lack accuracy and repeatability. Two IQTs are Other Test Method (OTM) 33A and Eddy Covariance (EC). We examined a novel approach to improve the accuracy of single sensor IQT whereby the results from both OTM and EC were combined with two machine learning (ML) models, a random forest (RF) and a neural network (NN). Then, models were enhanced with feature reduction and hyper-parameter tuning and compared to traditional quantification methods. The NN and RF improved upon the default OTM by an average of 44% and 78%, respectively. When compared to traditional OTM estimates with low Data Quality Indicators (DQIs), RF and NN models reduced 1σ errors from ±66% to ±13% and ±34%, respectively. Models also reduced the standard deviation of estimates with 93% and 85% of estimates falling within ±50% of the known release rate. This approach can be deployed with single sensor systems at well sites to improve confidence in reported emissions, reducing the number of anomalous overestimates that would trigger unnecessary site evaluations. Additional improvements could be realized by expanding training datasets with more methane release rates. Further, deployment of such models in a variety of situations could enhance their ability help close the gap between bottom-up inventory and top-down studies by enabling continuous monitoring of temporal emissions that could identify with improved confidence, atypically higher emissions. Accurate remote single sensor systems are key in developing an improved understanding of methane emissions to enable industry to identify and reduce methane emissions.

Quantification of methane emitted by ruminants: a review of methods.

The contribution of greenhouse gas (GHG) emissions from ruminant production systems varies between countries and between regions within individual countries. The appropriate quantification of GHG emissions, specifically methane (CH4), has raised questions about the correct reporting of GHG inventories and, perhaps more importantly, how best to mitigate CH4 emissions. This review documents existing methods and methodologies to measure and estimate CH4 emissions from ruminant animals and the manure produced therein over various scales and conditions. Measurements of CH4 have frequently been conducted in research settings using classical methodologies developed for bioenergetic purposes, such as gas exchange techniques (respiration chambers, headboxes). While very precise, these techniques are limited to research settings as they are expensive, labor-intensive, and applicable only to a few animals. Head-stalls, such as the GreenFeed system, have been used to measure expired CH4 for individual animals housed alone or in groups in confinement or grazing. This technique requires frequent animal visitation over the diurnal measurement period and an adequate number of collection days. The tracer gas technique can be used to measure CH4 from individual animals housed outdoors, as there is a need to ensure low background concentrations. Micrometeorological techniques (e.g., open-path lasers) can measure CH4 emissions over larger areas and many animals, but limitations exist, including the need to measure over more extended periods. Measurement of CH4 emissions from manure depends on the type of storage, animal housing, CH4 concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the variable that contributes the greatest to measurement uncertainty. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Ranging), but research is lagging in validating these methods. Bottom-up approaches to estimating CH4 emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources (enteric and manure). In contrast, top-down approaches estimate the amount of CH4 in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point. While these two estimation approaches rarely agree, they help identify knowledge gaps and research requirements in practice.

A cheap handheld NIR spectrometric system for automatic determination of methane, ethane, and propane in natural gas and biogas

In this study, a new NIR detection system using a cheap handheld NIR spectrometer, and a tungsten lamp coupled to a lab-made NIR flow cell was developed for the successful automatic quantification of methane, ethane, and propane in natural gas and biogas samples. To prepare standard gaseous mixtures for full-spectrum PLS multivariate calibration modeling and to record the NIR spectra of these mixtures and samples, this NIR detection system was connected to a flow automatic system and a gas chromatograph. The raw and second-derivative NIR spectra of the calibration set were then employed to construct PLS models using full cross-validation leave-one-out. All models presented high correlation coefficients (>0.9) and root mean square error (<5% mol mol−1) values. However, the RMSEP values using raw and preprocessed datasets were statistically similar according to the F-test at the 95% confidence level. The advantage of using raw data is the elimination of the preprocessing step for future prediction of samples to be analyzed. Once the NIR detection system has been previously calibrated via an automatic or batch process, it can be used to monitor light hydrocarbons or other gases inline (as in pipelines).

Analysis of Greenhouse Gas Emissions and the Environmental Impact of the Production of Asphalt Mixes Modified with Recycled Materials

This research focuses on the production and construction stages of the life cycle analysis (LCA) of asphalt mixtures modified with industrial waste and by-products, based on the quantification of methane (CH4), carbon monoxide (CO) and carbon dioxide (CO2) emissions produced during these processes. A laboratory-designed and calibrated gas measurement system with a microcontroller and MQ sensors is used to compare the emissions (GHG) of a “conventional” asphalt mix with those emitted by waste-modified asphalt mixes (polyethylene terephthalate and nylon fibres) and industrial by-products (copper slag and cellulose ash). The results obtained show that the gases emitted by each type of material can influence the design criteria from an environmental perspective. Methane gas emissions for asphalt mixes made with polymeric materials increase compared to the production phase of a conventional mix (M1) by 21% for PET and 14% for nylon. In contrast, for mixtures made with copper slag and cellulose ash, this emission is reduced by 12%. In addition, the use of copper slag and cellulose ash to replace natural aggregates reduces greenhouse gas emissions by 15% during the production phase and contributes to the creation of photochemical ozone for a shorter period of time. Regarding carbon dioxide emission, it increases considerably for all asphalt mixes, by 26% and 44.5% for cellulose ash and copper slag, respectively. For asphalt mixtures made of polymeric materials, the increase in carbon dioxide emission is significant, 130% for PET and 53% for nylon. In addition, it is noted that for this type of material, not only the emission of the gas must be taken into consideration, but also the time that the volatile particles spend in the atmosphere, affecting climate change and photochemical ozone (smog). The carbon monoxide gases emitted in the production phase of all the asphalt mixes analysed is similar among them.

Molecular analyses of the gill symbiosis of the bathymodiolin mussel Gigantidas platifrons.

SummaryAlthough the deep-sea bathymodiolin mussels have been intensively studied as a model of animal-bacteria symbiosis, it remains challenging to assess the host-symbiont interactions due to the complexity of the symbiotic tissue—the gill. Using cold-seep mussel Gigantidas platifrons as a model, we isolated the symbiont harboring bacteriocytes and profiled the transcriptomes of the three major parts of the symbiosis—the gill, the bacteriocyte, and the symbiont. This breakdown of the complex symbiotic tissue allowed us to characterize the host-symbiont interactions further. Our data showed that the gill's non-symbiotic parts play crucial roles in maintaining and protecting the symbiosis; the bacteriocytes supply the symbiont with metabolites, control symbiont population, and shelter the symbiont from phage infection; the symbiont dedicates to the methane oxidation and energy production. This study demonstrates that the bathymodiolin symbiosis interacts at the tissue, cellular, and molecular level, maintaining high efficiency and harmonic chemosynthetic micro niche.

Methane production through anaerobic co-digestion of tropical fruit biomass and urban solid waste

The objective of the research was to produce biogas by anaerobic co-digestion of biomass and urban solid waste, which made it possible to take advantage of these organic wastes, transforming them into a renewable energy source such as biogas being a fuel with an high calorific on an average of 19.6 to 25 MJ/m3. The methodology consisted of developing different experiments to evaluate the biogas performance in each of the selected biomasses. The percentage of CH4 and CO2 was determined by gas chromatography, from which the following results were obtained: by rinds of, potato (35.64% CH4), papaya (1.64% CH4) , pineapple (0.11% CH4), pea (16.13% CH4), banana (0.39% CH4), bean (0.76% CH4), sugarcane bagasse (80.85% CH4), wet sugarcane bagasse (96.06% CH4), semi-dry sugarcane bagasse (91.39% CH4) and potato peel with sugarcane bagasse (58.74% CH4). After the methane quantification in the different codigestion, the sugarcane bagasse presented better results with a 15% increase in the generation of methane when it was pretreated. Subsequently, we worked with anaerobic biodigesters on a laboratory scale with 500 mL capacity, with a 75% feed (sediment, sugarcane bagasse and 0.75: 1.5: 1.5 water). Propylene bags were used for the storage of biogas, which was quantified over a period of 78 days at ambient conditions, in this period of time it was also possible to determine humidity in experimentation 8 with 55% and in experimentation 9 with 32%, likewise the energy potential where 15.05 was obtained in experimentation 8 and 16, 72 in experimentation 9.

Methane gas generation through the anaerobian codigestion of urban solid waste and biomass

The objective of the research was to produce biogas by anaerobic co-digestion of biomass and urban solid waste, which made it possible to take advantage of these organic wastes, transforming them into a renewable energy source such as biogas being a fuel with an high calorific on an average of 19.6 to 25 MJ/m3. The methodology consisted of developing different experiments to evaluate the biogas performance in each of the selected biomasses. The percentage of CH4 and CO2 was determined by gas chromatography, from which the following results were obtained: by rinds of, potato (35.64% CH4), papaya (1.64% CH4), pineapple (0.11% CH4), pea (16.13% CH4), banana (0.39% CH4), bean (0.76% CH4), sugarcane bagasse (80.85% CH4), wet sugarcane bagasse (96.06% CH4), semi-dry sugarcane bagasse (91.39% CH4) and potato peel with sugarcane bagasse (58.74% CH4). After the methane quantification in the different codigestion, the sugarcane bagasse presented better results with a 15% increase in the generation of methane when it was pretreated. Subsequently, we worked with anaerobic biodigesters on a laboratory scale with 500 mL capacity, with a 75% feed (sediment, sugarcane bagasse and water 0.75: 1.5: 1.5). In experimentation 8 with 55% of methane and experimentation 9 with 32%, likewise the energy potential where 15.05 MJ/m3 in experimentation 8 and 16, 72 MJ/m3 in experimentation 9.