Biogenic Greenhouse Gas Emissions Research Articles

The potential of urban irrigation for counteracting carbon-climate feedback

Global climate changes, especially the rise of global mean temperature due to the increased carbon dioxide (CO2) concentration, can, in turn, result in higher anthropogenic and biogenic greenhouse gas emissions. This potentially leads to a positive loop of climate–carbon feedback in the Earth’s climate system, which calls for sustainable environmental strategies that can mitigate both heat and carbon emissions, such as urban greening. In this study, we investigate the impact of urban irrigation over green spaces on ambient temperatures and CO2 exchange across major cities in the contiguous United States. Our modeling results indicate that the carbon release from urban ecosystem respiration is reduced by evaporative cooling in humid climate, but promoted in arid/semi-arid regions due to increased soil moisture. The irrigation-induced environmental co-benefit in heat and carbon mitigation is, in general, positively correlated with urban greening fraction and has the potential to help counteract climate–carbon feedback in the built environment.

Geochemical Stability of Oil Sands Tailings in Mine Closure Landforms

Oil sands surface mining in Alberta has generated over a billion cubic metres of waste, known as tailings, consisting of sands, silts, clays, and process-affected water that contains toxic organic compounds and chemical constituents. All of these tailings will eventually be reclaimed and integrated into one of two types of mine closure landforms: end pit lakes (EPLs) or terrestrial landforms with a wetland feature. In EPLs, tailings deposits are capped with several metres of water while in terrestrial landforms, tailings are capped with solid materials, such as sand or overburden. Because tailings landforms are relatively new, past research has heavily focused on the geotechnical and biogeochemical characteristics of tailings in temporary storage ponds, referred to as tailings ponds. As such, the geochemical stability of tailings landforms remains largely unknown. This review discusses five mechanisms of geochemical change expected in tailings landforms: consolidation, chemical mass loading via pore water fluxes, biogeochemical cycling, polymer degradation, and surface water and groundwater interactions. Key considerations and knowledge gaps with regard to the long-term geochemical stability of tailings landforms are identified, including salt fluxes and subsequent water quality, bioremediation and biogenic greenhouse gas emissions, and the biogeochemical implications of various tailings treatment methods meant to improve geotechnical properties of tailings, such as flocculant (polyacrylamide) and coagulant (gypsum) addition.

Improving the accuracy of electricity carbon footprint: Estimation of hydroelectric reservoir greenhouse gas emissions

Hydropower is usually considered as a low-carbon electricity source, as it does not lead to direct greenhouse gas (GHG) emissions, unlike producing electricity from fossil fuels. However, the flooding of lands following the construction of the dam generally leads to an increase in biogenic GHG emissions due to the degradation of biomass found in the newly created reservoir. The life cycle assessment (LCA) methodology is widely used to calculate and compare the carbon footprint of different electricity production pathways, while considering all life cycle stages. Net biogenic GHG emissions from hydropower reservoirs have been poorly considered in LCA because of the scarcity of data. These emissions are complex to quantify as several mechanisms are involved, and extrapolating observations from one reservoir to another is risky as emissions vary greatly depending on different parameters, such as climate, geographic location, age of impoundment, and watershed properties. The objective of this article is to compare different approaches to estimate hydropower reservoir emissions in LCA, to select the most appropriate one, and to apply it to the calculation of the carbon footprint of electricity distributed in the Canadian province of Québec. Net biogenic GHG emissions of all hydropower reservoirs in the province (with 2.5 and 97.5% confidence intervals), as estimated using the G-res model, are 16.5 (14.7–18.6) gCO2∙kWh−1 and 0.29 (0.23–0.35) gCH4∙kWh−1. Combined to ecoinvent data for other life cycle emissions, the carbon footprint of electricity distributed in the province in 2017 is 34.5 gCO2eq∙kWh−1.

A Fast-Response Automated Gas Equilibrator (FaRAGE) for continuous in situ measurement of CH<sub>4</sub> and CO<sub>2</sub> dissolved in water

Abstract. Biogenic greenhouse gas emissions, e.g., of methane (CH4) and carbon dioxide (CO2) from inland waters, contribute substantially to global warming. In aquatic systems, dissolved greenhouse gases are highly heterogeneous in both space and time. To better understand the biological and physical processes that affect sources and sinks of both CH4 and CO2, their dissolved concentrations need to be measured with high spatial and temporal resolution. To achieve this goal, we developed the Fast-Response Automated Gas Equilibrator (FaRAGE) for real-time in situ measurement of dissolved CH4 and CO2 concentrations at the water surface and in the water column. FaRAGE can achieve an exceptionally short response time (t95 %=12 s when including the response time of the gas analyzer) while retaining an equilibration ratio of 62.6 % and a measurement accuracy of 0.5 % for CH4. A similar performance was observed for dissolved CO2 (t95 %=10 s, equilibration ratio 67.1 %). An equilibration ratio as high as 91.8 % can be reached at the cost of a slightly increased response time (16 s). The FaRAGE is capable of continuously measuring dissolved CO2 and CH4 concentrations in the nM-to-sub mM (10−9–10−3 mol L−1) range with a detection limit of sub-nM (10−10 mol L−1), when coupling with a cavity ring-down greenhouse gas analyzer (Picarro GasScouter). FaRAGE allows for the possibility of mapping dissolved concentration in a “quasi” three-dimensional manner in lakes and provides an inexpensive alternative to other commercial gas equilibrators. It is simple to operate and suitable for continuous monitoring with a strong tolerance for suspended particles. While the FaRAGE is developed for inland waters, it can be also applied to ocean waters by tuning the gas–water mixing ratio. The FaRAGE is easily adapted to suit other gas analyzers expanding the range of potential applications, including nitrous oxide and isotopic composition of the gases.

Discrepancies in N2O emissions between household waste and its food waste and non-food waste components during the predisposal stage

Nitrous oxide (N2O) is a greenhouse gas (GHG) and an ozone-depleting substance. Municipal solid waste (MSW) management and treatment activities are some of the sources of GHG emissions. However, the biogenic GHG emissions during the predisposal stage of MSW management, during which waste is transferred to garbage cans and then transported to disposal sites, have received little attention. In this study, household waste was divided into food and non-food waste, and the effects of these types of waste and different oxygen concentrations (21%, 10%, and 1%) on N2O emissions were investigated. A15N-labeled isotope experiment was conducted over three days to determine the contributions of nitrification and denitrification to N2O emissions. The results showed that the N2O fluxes first increased and then decreased during the three-day tests at different O2 concentrations. The maximum N2O flux of 1469.59 ± 1004.32 μg N·kg−1 wet waste·h−1 occurred during the predisposal of food waste at an O2 concentration of 21%, with the total N2O emissions reaching 20.26 ± 10.87 mg N·kg−1 wet waste, which exceeds the emissions from some waste disposal processes, such as composting and landfills. The N2O emissions decreased in the following order: food waste > household waste > non-food waste. For food waste, the peak value and total amount of N2O emissions decreased significantly as the O2 concentration decreased. In contrast, the N2O emissions from non-food waste increased as the O2 concentration decreased. Denitrification was the predominant biogenic source of N2O emissions; it accounted for over 60% of N2O production in all treatments. Nitrification also played an important role in N2O emissions during the early predisposal stage.

Spatial and technological variability in the carbon footprint of durum wheat production in Iran

The purpose of this study was to quantify the spatial and technological variability in life cycle greenhouse gas (GHG) emissions, also called the carbon footprint, of durum wheat production in Iran. The calculations were based on information gathered from 90 farms, each with an area ranging from 1 to 150 ha (average 16 ha). The carbon footprint of durum wheat was calculated by quantifying the biogenic GHG emissions of carbon loss from soil and biomass, as well as the GHG emissions from fertilizer application and machinery use, irrigation, transportation, and production of inputs (e.g., fertilizers, seeds, and pesticides). We used Spearman’s rank correlation to quantify the relative influence of technological variability (in crop yields, fossil GHG emissions, and N2O emissions from fertilizer application) and spatial variability (in biogenic GHG emissions) on the variation of the carbon footprint of durum wheat. The average carbon footprint of 1 kg of durum wheat produced was 1.6 kg CO2-equivalents with a minimum of 0.8 kg and a maximum of 3.0 kg CO2-equivalents. The correlation analysis showed that variation in crop yield and fertilizer application, representing technological variability, accounted for the majority of the variation in the carbon footprint, respectively 76 and 21%. Spatial variation in biogenic GHG emissions, mainly resulting from differences in natural soil carbon stocks, accounted for 3% of the variation in the carbon footprint. We also observed a non-linear relationship between the carbon footprint and the yield of durum wheat that featured a scaling factor of −2/3. This indicates that the carbon footprint of durum wheat production (in kg CO2-eq kg−1) typically decreases by 67% with a 100% increase in yield (in kg ha−1 year−1). Various sources of variability, including variation between locations and technologies, can influence the results of life cycle assessments. We demonstrated that technological variability exerts a relatively large influence on the carbon footprint of durum wheat produced in Iran with respect to spatial variability. To increase the durum wheat yield at farms with relatively large carbon footprints, technologies such as site-specific nutrient application, combined tillage, and mechanized irrigation techniques should be promoted.

Quantification of Greenhouse Gas Emissions from the Predisposal Stage of Municipal Solid Waste Management.

Municipal solid waste (MSW) disposal represents one of the largest sources of anthropogenic greenhouse gas (GHG) emissions. However, the biogenic GHG emissions in the predisposal stage of MSW management (i.e., the time from waste being dropped off in community or household garbage bins to being transported to disposal sites) are excluded from the IPCC inventory methodology and rarely discussed in academic literature. Herein, we quantify the effluxes of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from garbage bins in five communities along the urban-rural gradient in Beijing in four seasons. We find that the annual average CO2, CH4, and N2O effluxes in the predisposal stage were (1.6 ± 0.9)103, 0.049 ± 0.016, and 0.94 ± 0.54 mg kg-1h-1 (dry matter basis) and had significant seasonal differences (24- to 159-fold) that were strongly correlated with temperature. According to our estimate, the N2O emission in the MSW predisposal stage amounts to 20% of that in the disposal stage in Beijing, making the predisposal stage a nontrivial source of waste-induced N2O emissions. Furthermore, the CO2 and CH4 emissions in the MSW predisposal account for 5% (maximum 10% in summer) of the total carbon contents in a Beijing's household food waste stream, which has significance in the assessment of MSW-related renewable energy potential and urban carbon cycles.

Ecosystem model parameterization and adaptation for sustainable cellulosic biofuel landscape design

AbstractRenewable fuel standards in the US and elsewhere mandate the production of large quantities of cellulosic biofuels with low greenhouse gas (GHG) footprints, a requirement which will likely entail extensive cultivation of dedicated bioenergy feedstock crops on marginal agricultural lands. Performance data for such systems is sparse, and non‐linear interactions between the feedstock species, agronomic management intensity, and underlying soil and land characteristics complicate the development of sustainable landscape design strategies for low‐impact commercial‐scale feedstock production. Process‐based ecosystem models are valuable for extrapolating field trial results and making predictions of productivity and associated environmental impacts that integrate the effects of spatially variable environmental factors across diverse production landscapes. However, there are few examples of ecosystem model parameterization against field trials on both prime and marginal lands or of conducting landscape‐scale analyses at sufficient resolution to capture interactions between soil type, land use, and management intensity. In this work we used a data‐diverse, multi‐criteria approach to parameterize and validate the DayCent biogeochemistry model for upland and lowland switchgrass using data on yields, soil carbon changes, and soil nitrous oxide emissions from US field trials spanning a range of climates, soil types, and management conditions. We then conducted a high‐resolution case study analysis of a real‐world cellulosic biofuel landscape in Kansas in order to estimate feedstock production potential and associated direct biogenic GHG emissions footprint. Our results suggest that switchgrass yields and emissions balance can vary greatly across a landscape large enough to supply a biorefinery in response to variations in soil type and land‐use history, but that within a given land base both of these performance factors can be widely modulated by changing management intensity. This in turn implies a large sustainable cellulosic biofuel landscape design space within which a system can be optimized to meet economic or environmental objectives.

An approach for verifying biogenic greenhouse gas emissions inventories with atmospheric CO2 concentration data

Verifying national greenhouse gas (GHG) emissions inventories is a critical step to ensure that reported emissions data to the United Nations Framework Convention on Climate Change (UNFCCC) are accurate and representative of a country’s contribution to GHG concentrations in the atmosphere. Furthermore, verifying biogenic fluxes provides a check on estimated emissions associated with managing lands for carbon sequestration and other activities, which often have large uncertainties. We report here on the challenges and results associated with a case study using atmospheric measurements of CO2 concentrations and inverse modeling to verify nationally-reported biogenic CO2 emissions. The biogenic CO2 emissions inventory was compiled for the Mid-Continent region of United States based on methods and data used by the US government for reporting to the UNFCCC, along with additional sources and sinks to produce a full carbon balance. The biogenic emissions inventory produced an estimated flux of −408 ± 136 Tg CO2 for the entire study region, which was not statistically different from the biogenic flux of −478 ± 146 Tg CO2 that was estimated using the atmospheric CO2 concentration data. At sub-regional scales, the spatial density of atmospheric observations did not appear sufficient to verify emissions in general. However, a difference between the inventory and inversion results was found in one isolated area of West-central Wisconsin. This part of the region is dominated by forestlands, suggesting that further investigation may be warranted into the forest C stock or harvested wood product data from this portion of the study area. The results suggest that observations of atmospheric CO2 concentration data and inverse modeling could be used to verify biogenic emissions, and provide more confidence in biogenic GHG emissions reporting to the UNFCCC.

Landmark Papers: No. 3

I am very pleased to introduce the third in the series of Landmark Papers. This paper, another of the important papers published in the Journal and its predecessor The Journal of Soil Science, has inspired the longer-term thinking about the way in which soils function and, especially in this case, contribute to the understanding of wider-scale interactions of soils with other components of the global ecosystem. Landmark Paper 3 on the global status of soil carbon by Niels Batjes, first published in the European Journal of Soil Science in 1996, (Total carbon and nitrogen in the soils of the world; N.H. Batjes, European Journal of Soil Science, 1996, 47, 151–163) was, arguably, the first paper to alert the world to the functional role of soils at this scale. As Niels Batjes notes in his reflections ‘Such information is needed to inform land-use policies and to propose interventions that promote a reduction in biogenic greenhouse gas emissions at a global scale while ensuring food security, biodiversity and human well-being and livelihood at the local scale.’ The paper also provided an excellent example of how data from many sources can be integrated and deployed to provide a stimulus for further discussion and debate by scientists of all disciplines and indeed by policy makers. The importance of the paper is clearly demonstrated by the comprehensive commentary provided by three of the Journal's Associate Editors (Peter Loveland, Franz Conen and Bas van Wesemael). This commentary illustrates the extent to which the paper stimulated not only research on the contribution of soil carbon to global budgets from many parts of the world, but also on the way that we measure it and interpret the data. As usual, to accompany and complement the publication of Landmark Paper 3, we are also publishing an online virtual issue (available at www.EJSSLandmarkPapers.com) that comprises a selection of other papers published in the Journal up until the present day that have used and developed the ideas first set out by Batjes. There are many others that could have been included, but I have restricted the list to those papers that provide quantification of soil carbon. As a result of Batjes's paper, many papers have also been produced which have led to improvements in, and development of, the way in which we measure and understand soil carbon and its cycling in soils. I am grateful to Niels Batjes, Peter Loveland, Franz Conen and Bas van Wesemael for their thoughtful and stimulating contributions. I hope that this publication of the Landmark Paper and the associated virtual issue are of interest and value not only to the soil science community but to other disciplines and policy makers too.

Addressing Biogenic Greenhouse Gas Emissions from Hydropower in LCA

The ability of hydropower to contribute to climate change mitigation is sometimes questioned, citing emissions of methane and carbon dioxide resulting from the degradation of biogenic carbon in hydropower reservoirs. These emissions are, however, not always addressed in life cycle assessment, leading to a bias in technology comparisons, and often misunderstood. The objective of this paper is to review and analyze the generation of greenhouse gas emissions from reservoirs for the purpose of technology assessment, relating established emission measurements to power generation. A literature review, data collection, and statistical analysis of methane and CO2 emissions are conducted. In a sample of 82 measurements, methane emissions per kWh hydropower generated are log-normally distributed, ranging from micrograms to 10s of kg. A multivariate regression analysis shows that the reservoir area per kWh electricity is the most important explanatory variable. Methane emissions flux per reservoir area are correlated with the natural net primary production of the area, the age of the power plant, and the inclusion of bubbling emissions in the measurement. Even together, these factors fail to explain most of the variation in the methane flux. The global average emissions from hydropower are estimated to be 85 gCO2/kWh and 3 gCH4/kWh, with a multiplicative uncertainty factor of 2. GHG emissions from hydropower can be largely avoided by ceasing to build hydropower plants with high land use per unit of electricity generated.

Uncertainty propagation in soil greenhouse gas emission models: An experiment using the DNDC model and at the Oensingen cropland site

The increase of green house gas (GHG) concentrations in the atmosphere is predominantly caused by the anthropogenic activities of fossil fuel burning and land use change. The flux of GHGs from soils and ecosystems to the atmosphere is large, and any errors in estimating these fluxes have a significant impact on our quantification of the relative importance of land use in contributing to global warming. Numerical models have been developed to estimate the net flux of the biogenic GHGs: CO 2, N 2O and CH 4, for various agricultural management practices. These models have been developed using data from many different experimental sites around the world, encompassing different crops, farm management systems, soil and climatic conditions. Crop experiments and GHG flux measurements are expensive and last several years if not decades so these models are often used to test hypothesis about the effect of future conditions, land use scenarios and also to predict the effect of novel land management scenarios to reduce emissions. However, uncertainties in the input soil parameters and meteorological data that drive these models propagates through them, resulting in uncertainties in the predictions of biogenic GHG emissions. This paper describes an experiment that investigates how well the commonly used de-nitrification de-composition (DNDC) soil model performs when used to predict the eddy-covariance CO 2 fluxes and crop yields measured in the first full year of the Oensingen cropland site in Switzerland. DNDC N 2O predictions are compared to the IPCC emissions factors for arable land. This study includes an estimation of the uncertainty of soil input parameters, a sensitivity study as to their effect on predicted GHG emissions and the propagation of their uncertainty through the model. This study considers uncertainty in meteorological measurements and the impact of using subsets of this data in the model. In particular the effect of using monthly meteorological parameters to generate daily time series for input into the model is investigated and the error propagation quantified. The overall impact of uncertainty in input parameters on predicted biogenic GHG emissions is relatively small with the PDF of the uncertainties indicating that the NEE is over estimated by 3.6% and has a SD of 3.6% of the actual NEE. Nitrous oxide emissions are not biased but have a larger SD of 23% of emissions, which when the global warming impact is considered is only 3% of net flux. DNDC can therefore be used with confidence to predict emissions, with the caveat that the biomass production needs to be match to local conditions.

Biogenic greenhouse gas emissions linked to the life cycles of biodiesel derived from European rapeseed and Brazilian soybeans

Biogenic emissions of carbonaceous greenhouse gases and N 2O turn out to be important determinants of life cycle emissions of greenhouse gases linked to the life cycle of biodiesel from European rapeseed and Brazilian soybeans. For biodiesel from European rapeseed and for biodiesel from Brazilian soybeans grown for up to 25 years with no tillage on arable soil for which tropical rainforest or Cerrado (savannah) have been cleared, the life cycle emissions of greenhouse gases are estimated to be worse than for conventional diesel. Improving agricultural practices should be an important focus for cleaner production of biodiesel. These may include increasing soil carbon stocks by, e.g., conservation tillage and return of harvest residues and improving N-efficiency by precision agriculture and/or improved irrigation practices.

Regionalised inventory of biogenic greenhouse gas emissions from European agriculture

This study develops a detailed methodology compatible to the Guidelines of the Intergovernmental Panel on Climate Change (IPCC) to assess the annual direct biogenic emissions of greenhouse gases (GHG) released from European agriculture. This approach relies on emission factors and regression equations derived from all long-term measurements in Europe available by the end of 2001. Applying the methodology, the biogenic GHG emissions from agriculture within the European Union (EU) and within its Member States are calculated for the period from 1975 to 1997 at a spatial resolution of regions or federal states (NUTS 1–2 level). As a result, in 1995, European agriculture emitted 0.84±0.19 Tg N 2O, 8.1±2.0 Tg CH 4 and 39 Tg±25 CO 2, which adds up to 470±80 Tg CO 2-equivalents or 11% of the overall GHG emissions. At the EU level, these numbers are surprisingly close to the official inventory submitted under the United Nations Framework Convention on Climate Change (UNFCCC). But compared with the latter, the approach taken here leads to higher agricultural CH 4 emissions in Austria and the Netherlands, at least 20% lower CH 4 emissions in Denmark, Germany, Greece, Spain and Sweden and higher N 2O emissions in most EU Member States. In countries with—even small—areas of farmed organic soils, CO 2 emitted from peat oxidation can significantly contribute to the overall emissions. Hence, only the detailed approach adequately resolves regional and national specifics of agricultural conditions. It furthermore reduces the uncertainty in the emissions estimates to half of the one in inventories based on the IPCC Guidelines. Fair agreement with inverse atmospheric models was achieved. These results suggest that the methodology developed and applied here could serve as a significantly improved standard for official inventories of biogenic GHG emissions from EU Member States.