Estimate Greenhouse Gas Emissions Research Articles

A Modeling Approach for Addressing Sensitivity and Uncertainty of Estuarine Greenhouse Gas (CO2 and CH4) Dynamics

AbstractEstuaries make an important contribution to the global greenhouse gas budget. Yet modeling predictions of carbon dioxide (CO2) and methane (CH4) emissions from estuaries remain highly uncertain due to both simplified assumptions about the underpinning hydrologic and biologic processes and inadequate data availability to uniquely define parameters related to CO2 and CH4 processes. This study presents a modeling framework to quantify the sensitivity and uncertainty of predicted CO2 and CH4 concentrations and emissions, which is demonstrated through application to a subtropical urban estuary (Brisbane River, Australia). A 3D hydrodynamic‐biogeochemical model was constructed, and calibrated using the model‐independent Parameter ESTimation software (PEST) with field data sets that captured strong gradients of CO2 and CH4 concentrations and emissions along the estuary. The approach refined uncertainty in the estimation of whole‐estuary annual emissions, and enabled us to assess the sensitivity and uncertainty of CO2 and CH4 dynamics. Estuarine CO2 concentrations were most sensitive to uncertainty in riverine inputs, whereas estuarine CH4 concentrations were most sensitive to sediment production and pelagic oxidation. Over the modeled year, variance in the daily fluctuations in carbon emissions from this case‐study spanned the full range of emission rates reported for estuaries around the world, highlighting that spatially or temporally limited sampling regimes could significantly bias estuarine greenhouse gas emission estimates. The combination of targeted field campaigns with the modeling approach presented in this study can help to improve carbon budgeting in estuaries, reduce uncertainty in emission estimates, and support management strategies to reduce or offset estuary greenhouse gas emissions.

Drivers of Anaerobic Methanogenesis in Sub-Tropical Reservoir Sediments

Anaerobic methanogenesis is dependent on key macronutrients (carbon, nitrogen and phosphorus) and trace metals (including iron, nickel and cobalt) to drive methane production. Reservoir derived methane emissions have correlated to eutrophication status, with elevated emissions associated with more eutrophic systems. Additionally, sediment organic matter can enhance methane emissions, particularly through the ebullition pathway. As such, it is critical to understand how organic carbon and nutrient inputs into reservoir water columns and sediments drive methanogenesis to improve flooded land greenhouse gas emission estimates. In this study we examine the methane potential of sediments in mesotrophic (Little Nerang Dam) and eutrophic (Lake Wivenhoe) sub-tropical reservoirs under different nutrient and organic carbon availabilities using biological methane potential (BMP) tests. BMP tests were conducted with sediments incubated under anaerobic conditions using replicate controls (reservoir bottom waters) or treatments (excess nutrient and/or organic carbon availability). The results indicated that these systems are carbon limited. The addition of organic carbon significantly increases anaerobic methanogenesis by 20-fold over controls. Analysis of sediment samples from the reservoirs showed that both reservoirs were replete in key macronutrient and trace metal content for methanogenesis. Finally, a comprehensive catchment monitoring program of Little Nerang Dam measuring catchment inflow events, lateral transport of forest litter, and bulk atmospheric deposition showed that catchment inflows and lateral transport of forest litter were strongly linked to rainfall and accounted for more than 99% of the total annual load. This suggests the frequency of rainfall events is a critical driver of organic matter inputs that drive reservoir methane emissions in the humid, sub-tropical region.

Reduction in Greenhouse Gas Emission from Seedless Lime Cultivation Using Organic Fertilizer in a Province in Vietnam Mekong Delta Region

This study aimed to evaluate greenhouse gas (GHG) emissions from conventional cultivation (S1) of seedless lime (SL) fruit in Hau Giang province, in the Mekong Delta region of Vietnam. We adjusted the scenarios by replacing 25% and 50% of nitrogen chemical fertilizer with respective amounts of N-based organic fertilizer (S2 and S3). Face-to-face interviews were conducted to collect primary data. Life cycle assessment (LCA) methodology with the “cradle to gate” approach was used to estimate GHG emission based on the functional unit of one hectare of growing area and one tonnage of fresh fruit weight. The emission factors of agrochemicals, fertilizers, electricity, fuel production, and internal combustion were collected from the MiLCA software, IPCC reports, and previous studies. The S1, S2, and S3 emissions were 7590, 6703, and 5884 kg-CO2 equivalent (CO2e) per hectare of the growing area and 273.6, 240.3, and 209.7 kg-CO2e for each tonnage of commercial fruit, respectively. Changing fertilizer-based practice from S1 to S2 and S3 mitigated 887.0–1706 kg-CO2e ha–1 (11.7–22.5%) and 33.3–63.9 kg-CO2e t–1 (12.2–25.6%), respectively. These results support a solution to reduce emissions by replacing chemical fertilizers with organic fertilizers.

Estimation of Greenhouse Gas Emissions of Petrol, Biodiesel and Battery Electric Vehicles in Malaysia Based on Life Cycle Approach

A steady rise in the ownership of vehicles in Malaysia has drawn attention to the need for more effective strategies to reduce the emissions of the road transport sector. Although the electrification of vehicles and replacing petrol with biofuel are the strategies being considered in Malaysia, these strategies have yet to be fully evaluated from an environmental perspective. In this study, a life cycle assessment was conducted to compare the greenhouse gas emissions of different types of transportation means (passenger cars, two-wheelers (motorbikes), and buses) with several types of powertrains (petrol, biodiesel, electricity) based on multiple lifecycle stages in Malaysia. The impact of considering land use change for the biodiesel production in the LCA was also considered in this study. It was found that the transition from internal combustion engine vehicles fueled by petrol to electric vehicles would reduce the greenhouse gas emission for passenger cars, two-wheelers, and buses. However, because the greenhouse gas emissions of biodiesel-fueled vehicles are higher than those of petrol-fueled vehicles, even without considering land use change, the results indicate that the transition from a 10% to 20% biofuel blend, which is a current strategy in Malaysia, will not result in a reduction in greenhouse gas emissions for the transport sector in Malaysia.

Analyzing the role of in situ coal fire in greenhouse gases emission in a coalfield using remote sensing data and their dispersion and source apportionment study.

In situ coal fires significantly pollute the environment in many countries of the world. Monitoring these pollutants is challenging due to extensive area coverage and spatial variations. Thus, the present study demonstrates the method of deriving the spatial and temporal profiles of columnar density of three major greenhouse gases (carbon monoxide (CO), sulfur dioxide (SO2), and nitrogen dioxide (NO2)) in an in situ coal fire region (Jharia coalfield (JCF), India) using high-resolution satellite data (TROPOMI) of the European Space Agency (ESA). The study also demonstrates a new methodology for estimating greenhouse gas emissions from in situ coal burning. JCF is one of the significant polluted mining regions with multiple in situ coal fire pockets. The columnar density of the gaseous pollutants in the mining region was compared with the same in the rural, urban, and forest regions to identify the major emission inventories. The study results indicated that coal fire is the major source of CO emission in the region, as the CO was high in the fire regions compared to that of the non-fire regions. But, the major source of NO2 is the traffic, as the NO2 was high in the city area as compared to other regions. The spatial profile of SO2 does not reveal the specific emission sources. The study results indicated that TROPOMI onboard satellite sensors could be effectively used for deriving the spatial profiles of greenhouse gaseous in coal fire regions, which further assist in identifying the emission inventories. Furthermore, the satellite-based Earth observations offer information to understand and manage the greenhouse gas emissions over a large area.

Estimation of greenhouse gases emission from domestic wastewater in Nepal: A scenario-based analysis applicable for developing countries

Domestic wastewater and wastewater treatment plants (WWTPs) are key emitters of greenhouse gases (GHGs). Quantifying these emissions in the present and future is crucial to tackle global climate change issues. As a developing country with few rural and urban wastewater treatment facilities, Nepal may have a unique opportunity to reduce future GHGs emissions by a proper selection of wastewater treatment technology. In this paper, the authors used Python programming to estimate the GHGs emissions from the domestic wastewater sector in Nepal under various technological development scenarios for 2020 to 2040 using the refined 2019 estimation methodology developed by Inter-governmental Panel on Climate Change (IPCC). Results show total equivalent CO2 emission of 3829.43 and 4523.65 Gigagrams in 2020 and 2040, respectively. The 2020 value is seven times greater than Nepal's 2017 national estimates because this study considered rural population and updated methodology. Comparing the technology development scenarios with the Business as Usual scenario, the highest GHGs reduction could be achieved by hybrid constructed wetlands (69.20%) followed by a combined anaerobic and aerobic system with biogas recovery for energy generation (61.72%). Further accuracy may be attained only through the actual measurement of WWTPs emissions and country-specific emission factors. Thus, this paper proposes GHGs estimation of future scenarios portraying urban and rural populations may be invaluable to policymakers of GHGs mitigation for selection of feasible WWTPs, especially in developing countries with limited wastewater treatment facilities and wastewater activity data.

Artificial Intelligence Approach for Estimating Dairy Methane Emissions.

California's dairy sector accounts for ∼50% of anthropogenic CH4 emissions in the state's greenhouse gas (GHG) emission inventory. Although California dairy facilities' location and herd size vary over time, atmospheric inverse modeling studies rely on decade-old facility-scale geospatial information. For the first time, we apply artificial intelligence (AI) to aerial imagery to estimate dairy CH4 emissions from California's San Joaquin Valley (SJV), a region with ∼90% of the state's dairy population. Using an AI method, we process 316,882 images to estimate the facility-scale herd size across the SJV. The AI approach predicts herd size that strongly (>95%) correlates with that made by human visual inspection, providing a low-cost alternative to the labor-intensive inventory development process. We estimate SJV's dairy enteric and manure CH4 emissions for 2018 to be 496-763 Gg/yr (mean = 624; 95% confidence) using the predicted herd size. We also apply our AI approach to estimate CH4 emission reduction from anaerobic digester deployment. We identify 162 large (90th percentile) farms and estimate a CH4 reduction potential of 83 Gg CH4/yr for these large facilities from anaerobic digester adoption. The results indicate that our AI approach can be applied to characterize the manure system (e.g., use of an anaerobic lagoon) and estimate GHG emissions for other sectors.

Using deep learning to classify grassland management intensity in ground-level photographs for more automated production of satellite land use maps

Collecting accurate reference data for training and validating remotely sensed land use/land cover (LULC) maps is important. Financial and logistical constraints on projects mean it may not be possible to collect field data. Automated photo recognition methods using deep learning are increasingly being used for mapping land cover (the physical surface at a location). Their use in mapping land use (how the surface is used) is less well developed. This study explores an approach of using geotagged ground-level photographs to identify management intensity on agricultural grasslands. Management intensity on grasslands has implications for soil and water quality, biodiversity and habitat loss, and carbon fluxes. Uncertainty in the level of management intensity occurring at field scale is a source of error in greenhouse gas emission estimates from grasslands. Our study uses convolutional neural networks (CNN) to automate the labelling of unseen images into three management classes (intensive, extensive, abandoned). Ground-level photographs were taken during the 2018 EUROSTAT Land Use/Coverage Area Frame Survey (LUCAS). Accuracy up to 92.8% was achieved. Predicted labels were used to train a management intensity map using Sentinel 1 and Sentinel 2 satellite imagery. Overall accuracy for a random forest (RF) classification was 84.8% (64.8% after class imbalance was accounted for). Potential improvements to both the CNN and RF models are discussed. This study demonstrates the potential of CNN for classifying grassland use from geotagged photographs as part of automated LULC mapping workflows.

Polyethylene microplastics alter the microbial functional gene abundances and increase nitrous oxide emissions from paddy soils

The accumulation of microplastics (MPs) in terrestrial ecosystems can affect greenhouse gases (GHGs) production by changing soil structure and microbial functions. In this study, microcosm experiments were conducted to investigate the impact of polyethylene (PE) MP addition on soil carbon dioxide (CO2) and nitrous oxide (N2O) emissions from paddy soils and their associated microbial functional genes. Methane was not considered due to the negligible emissions throughout the incubation. The amendment of both virgin and aged PE MPs did not significantly (p > 0.05) affect soil CO2 emissions, but significantly (p < 0.05) increased the abundances of microbial functional genes encoding enzymes involved in hemicellulose (abfA) and lignin (mnp) decomposition, indicating plastic particle has potential to stimulate soil organic carbon decomposition. The presence of PE MP significantly increased N2O emissions by 3.7-fold, which was probably due to PE MP increased the abundances of nirS gene involved in nitrite reductase. In addition, compared with virgin PE MP treatment, artificially aged PE MP did not significantly (p > 0.05) influence soil CO2 and N2O emissions. Our results provide evidence that PE MP likely cause a high risk of N2O emission from paddy soils, this factor should be considered in future estimates of GHGs emissions from rice fields.

Impact of Dietary Meat and Animal Products on GHG Footprints: The UK and the US

Direct and indirect greenhouse gas (GHG) emissions from the ~30+ billion animals consumed as food each year contribute ~14–16% of the global total. The aim of this research is to determine the contribution of meat and animal products to individual GHG footprints. Top-down estimates of GHG emissions from each livestock species are determined using livestock numbers, types, and region-specific emission factors. Comparing livestock emissions with those from individual countries, cattle rank as the third largest emitter after China and the United States (US). The largest uncertainty in these emissions calculations is in the range of emissions factors. Global top-down calculations indicate that the per capita GHG footprint from livestock emissions alone are approximately 1 tCO2eyr−1. For the United Kingdom (UK) and the US, the calculated GHG livestock-related footprints are 1.1 tCO2eyr−1 and 1.6 tCO2eyr−1 per person, respectively. Bottom-up calculations focused on the UK and the US from consumption figures indicated emissions related to meat consumption are approximately 1.3–1.5 tCO2eyr−1 per person. Comparing dietary changes with other ways of reducing GHG footprints indicates removing dietary meat is similar to avoiding one long-haul flight each year and a larger reduction than driving 100 miles less each week.

Estimating & comparing greenhouse gas emissions for existing intramuscular COVID-19 vaccines and a novel thermostable oral vaccine.

BackgroundClimate impacts are rarely considered in health impact and economic assessments of public health programs. This study estimates the greenhouse gas (GHG) emissions averted by a novel oral SARS-CoV-2 (COVID-19) vaccine compared with four existing intramuscular vaccines: AstraZeneca's COVISHIELD®, Pfizer/BioNTech's COMIRNATY®, Moderna's mRNA-1273, and Johnson & Johnson's Ad26.COV2.S COVID-19 vaccine.MethodsWe estimated GHG emissions averted for five vaccine modalities across nine countries. GHG emissions averted were derived from differences in cold chain logistics, production of vaccine supplies, and medical waste disposal. Countryspecific data including population coverage and electricity production mix were included in GHG emissions calculations. Results are presented in averted GHG per vaccine course and country level based on modeled vaccination demand.FindingsPer course, an oral vaccine is estimated to avert between 0.007 and 0.024 kgCO2e compared with Johnson & Johnson, 0.013 to 0.048 kgCO2e compared with AstraZeneca, 0.23 to 0.108 kgCO2e compared with Moderna, and 0.134 to 0.466 kgCO2e compared with Pfizer/BioNTech. The total GHG averted varied across countries based upon predicted demand, mix of electrical production, and vaccination strategy with the largest emissions reductions projected for India and the United States.InterpretationOur results demonstrate large potential GHG emissions reductions from the use of oral vs. intramuscular vaccines for mass COVID-19 vaccination programs. Up to 82.25 million kgCO2e could be averted from utilization of an oral vaccine in the United States alone, which is equivalent to eliminating 17,700 automobiles from the road for one year.FundingFunding was provided by Vaxart, Inc. Vaxart, Inc. is currently developing an oral COVID-19 vaccine, the characteristics of which were utilized to define the thermostable oral vaccine discussed in this study. Apart from providing data on the characteristics of the oral vaccine under development, the funders had no influence over the study design, methods, statistical analyses, results, framing of results, decision to submit the manuscript for publication, or choice of journal.

Greenhouse gas emissions from Mexican inland waters: first estimation and uncertainty using an upscaling approach

ABSTRACT The traditional upscaling approach to greenhouse gas (GHG) emission estimates of inland waters is imprecise, but more precise methods based on environmental drivers are a longstanding challenge. Mexico lacks GHG emission estimates for its inland waters, and only sparse but scientifically validated information is available. This study provides the first GHG emission estimates from Mexican inland waters using 4275 GHG flux measurements from 26 distinctive waterbodies and one local and another global surface area dataset (INEGI and HydroLAKES). GHG emission factors were calculated and subsequently upscaled to estimate total national GHG emissions from the inland waters and compare to other emission measures based on mean global emission factors or size-productivity weighted (SPW) models. Mean (standard error) annual fluxes from all inland waters were 2.2 (5.3) kg CO2 m−2 yr−1, 0.6 (1.14) kg CH4 m−2 yr−1, and 1.0 × 10−3 (6.0 × 10−4) kg N2O m−2 yr−1. Estimates for natural waterbodies are annual average release rates between 74 (87) and 139 (163.23) Tg CO2eq while artificial waterbodies reach between 32 (2) and 21 (21) Tg CO2eq according to INEGI and HydroLAKES datasets, respectively. Considerable uncertainty was determined in the calculated mean emission factor, mostly for anthropogenic emissions. Waterbody area and chlorophyll a concentration were used as proxies to model CO2 and CH4 fluxes through regression analysis. According to SPW and IPCC models, computed mean annual CH4 emission factors were close to our estimates and exhibited a strong influence from eutrophication. In a likely scenario of increased eutrophication in Mexico, an increase in total net emissions from inland waters could be expected.

Microdata selection for estimating household consumption-based emissions

To estimate household emissions from a consumption-perspective, national accounts are typically disaggregated to a sub-national level using household expenditure data. While limitations around using expenditure data are frequently discussed, differences in emission estimates generated from seemingly comparable expenditure microdata are not well-known. We compare UK neighbourhood greenhouse gas emission estimates derived from three such microdatasets: the Output Area Classification, the Living Costs and Food Survey, and a dataset produced by the credit reference agency TransUnion. Findings indicate moderate similarity between emission estimates from all datasets, even at detailed product and spatial levels; importantly, similarity increases for higher-emission products. Nevertheless, levels of similarity vary by products and geographies, highlighting the impact microdata selection can have on emission estimates. We focus our discussion on how uncertainty from microdata selection can be reduced in other UK and international contexts by selecting data based on the data generation process, the level of disaggregation needed, physical unit availability and research implications.

Homogeneous land-use sequences in heterogeneous small-scale systems of Central Kenya: Land-use categorization for enhanced greenhouse gas emission estimation

The current understanding of the link between land management practices and GHG (greenhouse gas) emissions is limited in the small-scale farm sector of sub-Sahara regions due to insufficient or fragmented land-use data. Land-use categories recognized in the national GHG inventories in Kenya are coarse. Therefore, they do not adequately account for the diversity in small-scale land uses. Characterization of land-use and knowledge of key drivers of land-use change is necessary to improve national GHG inventories in the SSA (Sub-Sahara Africa) region. We implemented a cross-sectional survey to characterize land-use and determine factors which influenced changes in land use within small-scale farms of Tharaka-Nithi County, Kenya. We sampled 300 farmers using multistage sampling and collected crop sequence recall data at plot level for three years (seven seasons). We grouped crop sequences into clusters using the ‘TraMineR’ R package. We derived four clusters including banana, tea, and declining fallows (cluster 1, 19.2% of plots), cereal-legume systems (cluster 2, 55.1%), fodder (cluster 3, 11.7%) and coffee (cluster 4, 14.0%). We observed higher N application rates in perennial cropping systems, than in annual crops, including cereal-legume systems. We observed that farmers in higher potential agro-ecological zones, male-managed farms, with higher per capita land area, higher remittances and higher total house-hold incomes, were associated with a higher propensity to adjust crop enterprises, leading to more unstable land-use sequences. Contrariwise, farmers with higher education, credit access, secure land tenure, increasing slope, good soil fertility, and longer farming experiences recorded a lower propensity to adjust their land uses, resulting in more stable crop sequences. Farmer socio-demographic characteristics influenced land-use change, which is directly linked with soil GHG emissions. Our findings propose the adoption of Tier II GHG quantification approaches which disaggregate between annual and perennial crop enterprises. GHG emissions are likely to be more generalizable in stable perennial crop systems than annual systems. Thus, better disaggregation of GHG sampling in annual crop systems is needed due to high diversity in crop and soil fertility management, and the dynamic nature of C and N cycling in these systems.

Implications of Generation Efficiencies and Supply Chain Leaks for the Life Cycle Greenhouse Gas Emissions of Natural Gas-Fired Electricity in the United States.

Uncertainties in supply chain emissions raise questions about the benefits of natural gas as a bridge fuel, but recent efficiency improvements in gas-fired electricity generation remain overlooked. Our comprehensive analysis of supply chain infrastructure and electricity generation across the United States informs spatially and temporally resolved estimates of life cycle greenhouse gas emissions. Results show decreasing life cycle emissions over each year examined: 629, 574, and 525 kg CO2 eq MWh-1 in 2005, 2010, and 2015, respectively. Electricity generation contributed 86% of emissions or greater for each year. Despite concerns over uncertain methane leaks, efficiency improvements make it much more likely that natural gas electricity has an unambiguous greenhouse gas benefit relative to coal. Methane leaks would have to be 4.4 times the Environmental Protection Agency (EPA) value in 2015 to reverse these benefits over 20-year time horizons. With retiring coal plants and scrutinized supply chain emissions, our results show that natural gas can provide a lower emissions option to coal in an increasingly decarbonized power sector.

A comprehensive estimate of life cycle greenhouse gas emissions from onshore wind energy in China

Rapid deployment of wind energy plays an important role in China's proposed energy transition to carbon neutrality before 2060. Greenhouse gas (GHG) emissions are, however, unavoidable during the entire life cycle of wind energy from manufacturing to disposal. It is important to estimate these GHG emissions and the emission intensity for programs of energy transition. In this study, simplified LCA models and engineering-based models were developed to provide a comprehensive estimate of the GHG emission intensity from onshore wind energy in China at provincial and national scales. Results showed that in 2019, the GHG emission intensity per unit power generation was 19.88 g CO2 eq/kWh (provincial intensity ranges from 13.59 to 34.50 g CO2 eq/kWh). The results indicated that onshore wind energy in China has an emission intensity more than 98% lower than traditional fossil fuels and the mitigation effect can reach 84%–98% compared to the energy mix in 2020. The effects on emission intensity of shifting the turbine mix towards larger sizes, reducing wind curtailment and using advanced designs to improve efficiency were further investigated. Advanced design of turbines can further decrease GHG emission intensity by 21.6%, more than the scenario of reducing curtailment (5.4%), while the emission intensity could be reduced by 2.1% under the scenario of shifting the turbine mix towards larger sizes. The results will aid future energy-mix scenario design and policy formulation.

Estimation of greenhouse gas emissions of a tropical reservoir in Colombia

Abstract Tropical reservoirs are generally flooded in soils with a high content of organic matter. This, combined with high temperatures, favors the generation of carbon dioxide and methane by biological degradation, contributing to the impact on climate change. A tropical reservoir in Colombia was monitored for 7 years in the pre-fill, fill and post-fill stages, for the last of these during the day and night. Emissions from diffusive fluxes at the surface of the water were measured using a floating static chamber, while inverted funnel methodology was used to measure the fluxes by bubbling. The samples collected in the field were analyzed in the laboratory using a gas chromatograph with a mass detector. The results showed average emissions of 70892.51±41079.16-ton CO2eq/year for pre-filling; 178254.53±105838.01-ton CO2eq/year for filling; and 466946.57-ton CO2eq/year for post-filling (for 5 years), concluding that the weather conditions and the filling percentage had an impact on the generation of greenhouse gases at filling and post-filling stages, as did the organic matter present in the area of influence of the sampling point. Higher greenhouse gas emissions were found during the day compared to the results at night, indicating that temperature affects these processes especially in tropical reservoirs.

Sensors and Methods for Measuring Greenhouse Gas Emissions from Different Components of Livestock Production Facilities

Greenhouse gas monitoring on a broader scale is necessary to ensure that a cap-and-trade system is effective, reduces measurement uncertainty, and detects fraudulent or illegal activities. The recent strict air quality regulation in livestock production facilities has accelerated the need for accurate on-farm determination of greenhouse gas (GHG) emission rates (ERs) from livestock operations in the United States under a wide range of production, management, and climate conditions. The estimation of GHG emissions from different ground-level sources or at a property line is a very complicated process, and such measurements require multidirectional expertise including engineering, micrometeorology, agronomy, applied physics, and chemistry. Accurate measurement of gaseous concentration from an emitting source is a prerequisite and of paramount importance for estimating emissions rates (ERs) using any micro-meteorological and sampling device-based method. This paper provides an overview of the state-of-the-art sensors and analyzers used to measure GHG concentrations. Sensor and analyzer selection and their performance in the laboratory and field were discussed. In addition, protocols for data quality control (QC) and quality assurance (QA) when deploying sensors in the area for long-term use were also discussed. In addition, the preparation of measurement systems, coupling of air samplers with sensing systems for measuring gaseous concentrations, and uncertainties inherent to such measurement methods as a whole to estimate ERs were discussed in this paper.

Assessing the carbon stock of cultivated pastures in Rondônia, southwestern Brazilian Amazon.

Cattle ranching is the primary land-use of deforested areas in the Brazilian Amazon. Deforestation precedes pasture establishment, implying tremendous amounts of greenhouse gas emissions caused by carbon stock losses. Despite several studies addressing carbon storage in forests, there is a lack of data regarding cultivated pastures. Hence, the estimation of greenhouse gas emissions associated with land-use change becomes uncertain. In this study, we assessed the carbon stock of cultivated pastures located in Rondônia, southwestern Brazilian Amazon. A total of 50 squared plots of 1 m² were randomly allocated in cattle ranching farms covered by Oxisols (Dystrophic Yellow and Dystrophic Red-Yellow Latosols). Carbon fraction ranged from 0.36 for belowground biomass to 0.45 gC.g-1 d.m. for aboveground biomass. The average total carbon stock was 5.17 MgC.ha-1, with non-significant differences when stratifying data by soil types. Considering data from the III Brazilian Inventory of Anthropogenic Emissions and Removals of Greenhouse Gases, our results suggested that land-use change from primary forests to cultivated pastures resulted in a loss of 192.54 MgC.ha-1, which corresponds to a net emission of 705.98 MgCO2eq.ha-1 to the atmosphere. This study provides valuable information to improve the Brazilian Inventory of Anthropogenic Emissions and Removals of Greenhouse Gases.

An Ensemble Learning Method for the Kernel‐Based Nonlinear Multivariate Grey Model and its Application in Forecasting Greenhouse Gas Emissions

The global warming problem caused by greenhouse gas (GHG) emissions has aroused wide public concern. In order to give policy makers more power to set the specific target of GHG emission reduction, we propose an ensemble learning method with the least squares boosting (LSBoost) algorithm for the kernel‐based nonlinear multivariate grey model (KGM) (1, N), and it is abbreviated as BKGM (1, N). The KGM (1, N) has the ability to handle nonlinear small‐sample time series prediction. However, the prediction accuracy of KGM (1, N) is affected to an extent by selecting the proper regularization parameter and the kernel parameter. In boosting scheme, the KGM (1, N) is used as a base learner, and the use of early stopping method avoids overfitting the training dataset. The empirical analysis of forecasting GHG emissions in 27 European countries for the period 2015–2019 is carried out. Overall error analysis indicators demonstrate that the BKGM (1, N) provides remarkable prediction performance compared with original KGM (1, N), support vector regression (SVR), and robust linear regression (RLR) in estimating GHG emissions.