Modeling Of Greenhouse Gas Emissions Research Articles

Idiosyncratic phenology of greenhouse gas emissions in a Mediterranean reservoir

AbstractExtreme hydrological and thermal regimes characterize the Mediterranean zone and can influence the phenology of greenhouse gas (GHG) emissions in reservoirs. Our study examined the seasonal changes in GHG emissions of a shallow, eutrophic, hardwater reservoir in Spain. We observed distinctive seasonal patterns for each gas. CH4 emissions substantially increased during stratification, influenced predominantly by the increase in water temperature, net ecosystem production, and the decline in reservoir mean depth. N2O emissions mirrored CH4's seasonal trend, significantly correlating to water temperature, wind speed, and gross primary production. Conversely, CO2 emissions decreased during stratification and displayed a quadratic, rather than a linear relationship with water temperature—an unexpected deviation from CH4 and N2O emission patterns—likely associated with photosynthetic uptake of bicarbonate and formation of intracellular calcite that might be exported to sediments. This investigation highlights the imperative of integrating these idiosyncratic patterns into GHG emissions models, enhancing their predictive power.

Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions

Abstract. Despite covering only 3 % of the planet’s land surface, peatlands store 30 % of the planet’s terrestrial carbon. The net greenhouse gas (GHG) emissions from peatlands depend on many factors but primarily soil temperature, vegetation composition, water level and drainage, and land management. However, many peatland models rely on water levels to estimate CH4 exchange, neglecting to consider the role of CH4 transported to the atmosphere by vegetation. To assess the impact of vegetation on the GHG fluxes of peatlands, we have developed a new model, Peatland-VU-NUCOM (PVN). The PVN model is a site-specific peatland CH4 and CO2 emissions model, able to reproduce vegetation dynamics. To represent dynamic vegetation, we have introduced plant functional types and competition, adapted from the NUCOM-BOG model, into the framework of the Peatland-VU model, a peatland GHG emissions model. The new PVN model includes plant competition, CH4 diffusion, ebullition, root, shoot, litter, exudate production, belowground decomposition, and aboveground moss development under changing water levels and climatic conditions. Here, we present the PVN model structure and explore the model's sensitivity to environmental input data and the introduction of the new vegetation competition schemes. We evaluate the model against observed chamber data collected at two peatland sites in the Netherlands to show that the model is able to reproduce realistic plant biomass fractions and daily CH4 and CO2 fluxes. We find that daily air temperature, water level, harvest frequency and height, and vegetation composition drive CH4 and CO2 emissions. We find that this process-based model is suitable to be used to simulate peatland vegetation dynamics and CH4 and CO2 emissions.

Carbon footprint of a conventional wastewater treatment plant: An analysis of water-energy nexus from life cycle perspective for emission reduction

Reducing greenhouse gas (GHG) emissions is of far-reaching implications for the sustainable development of a conventional domestic wastewater treatment plant (WWTP). GHG emissions of the WWTP in different working loads were monitored to quantify the influence of critical processes on GHG emissions and shed light on the relations between direct and indirect GHG emissions with the water-energy nexus. The life cycle carbon footprint of the WWTP was assessed to find the most critical impact factor. A conceptual model of direct and indirect GHG emissions was established based on the water-energy nexus and life cycle assessment (LCA). Results showed that direct GHG emissions were highest in medium working load in May. While high indirect GHG emissions were in February, with the highest working load. Methane (CH4) emission rates, from 44.39 ± 42.49 to 4.94 ± 2.29 kg CO2e·d−1, decreased along the treatment processes due to oxidation. Dissolved CH4 in wastewater mainly came from the influent that was 0.08 ± 0.04 kg CH4·d−1. The increase of nitrous oxide (N2O) emissions and dissolved N2O occurred in the secondary treatment stage and was affected by the C/N ratio. The operation and maintenance phase had the most significant carbon footprint due to 86.4% of indirect GHG emissions from electricity usage through LCA. Electricity usage and glucose dosing were significantly correlated with N2O emissions but had no contribution to CH4 emissions. The conceptual model qualitatively demonstrated that direct and indirect GHG emissions reduced as electricity usage decreased based on the water-energy nexus. This study provided new insight into the analysis of GHG emissions from WWTPs.

Estimating energy consumption and GHG emissions in crop production: A machine learning approach

It is necessary to observe the relationship between the energy inputs and outputs of agricultural production in the context of long-term strategies to determine optimal sustainable solutions and identify the most susceptible system components. Comprehensive research to assess energy performance data over a relatively long-term is required to predict crop production and greenhouse gas (GHG) emissions based on energy inputs. This study collects and analyzes energy inputs and GHG emissions from 17 crops of Iran in five major categories (cereal, pulse, oilseed, fiber, and tubers) during 1970–2019 to quantify the long-term energy efficiency of major crops. Three machine learning (ML) algorithms are developed to quantify the relationship between the resources consumed per unit of energy output and GHG emission models. These models are evaluated through three quantitative statistics. The analysis considers all physical and chemical inputs used to produce the major crops. These energy variables include human labor, machinery, fossil fuels, electricity, irrigation water, fertilizers (phosphate, nitrogen, and potassium), pesticides and seed rate. The results revealed that the input and output energy values increased from 39.47 to 190.18 MJ ha-1 in 1970 to 253.43 and 654 MJ ha−1 in 2019. The GHG emissions from electricity (47.1%) were followed by those of nitrogen fertilizer (25.75%). Reducing the consumption of electricity and nitrogen fertilizers is the most efficient options to improve the energy efficiency of the crops studied.

Multiyear methane and nitrous oxide emissions in different irrigation management under long-term continuous rice rotation in Arkansas.

Rice paddies are one of the major sources of anthropogenic methane (CH4 ) emissions. The alternate wetting and drying (AWD) irrigation management has been shown to reduce CH4 emissions and total global warming potential (GWP) (CH4 and nitrous oxide [N2 O]). However, there is limited information about utilizing AWD management to reduce greenhouse gas (GHG) emissions from commercial-scale continuous rice fields. This study was conducted for five consecutive growing seasons (2015-2019) on a pair of adjacent fields in a commercial farm in Arkansas under long-term continuous rice rotation irrigated with either continuously flooded (CF) or AWD conditions. The cumulative CH4 emissions in the growing season across the two fields and 5 years ranged from 41 to 123kg CH4 -C ha-1 for CF and 1 to 73kg CH4 -C ha-1 for AWD. On average, AWD reduced CH4 emissions by 73% relative to CH4 emissions in CF fields. Compared to N2 O emissions, CH4 emissions dominated the GWP with an average contribution of 91% in both irrigation treatments. There was no significant variation in grain yield (7.3-11.9 Mg ha-1 ) or growing season N2 O emissions (-0.02 to 0.51kg N2 O-N ha-1 ) between the irrigation treatments. The yield-scaled GWP was 368 and 173kg CO2 eq. Mg-1 season-1 for CF and AWD, respectively, showing the feasibility of AWD on a commercial farm to reduce the total GHG emissions while sustaining grain yield. Seasonal variations of GHG emissions observed within fields showed total GHG emissions were predominantly influenced by weather (precipitation) and crop and irrigation management. The influence of air temperature and floodwater heights on GHG emissions had high degree of variability among years and fields. These findings demonstrate that the use of multiyear GHG emission datasets could better capture variability of GHG emissions associated with rice production and could improve field verification of GHG emission models and scaling factors for commercial rice farms.

Greenhouse gas emissions from ant nests: A systematic review

Abstract Ant nests are ecologically important emission sources of CO2, CH4, and N2O. An updated review of the progress in studying greenhouse gas (GHG) flux from ant nests could provide a more comprehensive understanding of their role in this context. We evaluate the progress in assessing GHG flux assessment through a systematic review and identify factors responsible for higher GHG emissions from ant nests. The specific goals were to conduct a bibliographic analysis of the frequency and geographical distribution of scientific works addressing this topic, reported species, and methodologies; to relate the mean GHG flux to species‐specific characteristics of ant nests; and to identify patterns and biases responsible for reported higher GHG levels. More data is needed on the species and habitats studied. The most frequently examined gas was CO2, and the closed chamber system was the most used method, with a wide variation in chamber size and material. No relation was found between CO2 emissions and species‐specific characteristics, which can be explained by the fact that these data show a high variability, probably due to the abiotic factors in each study, different measurement methods, and their respective configurations. Our study underlines the need to standardise GHG measurement methods to allow for reliable comparisons between ant species and habitats. Furthermore, it signals the need to investigate more information to build appropriate global GHG emissions models. This progress will only be possible through collaborative studies by increasing interaction among researchers through projects on a continental or global scale.

Synergies in sustainable phosphorus use and greenhouse gas emissions mitigation in China: Perspectives from the entire supply chain from fertilizer production to agricultural use

Synergies to achieve high phosphorus (P) use efficiency (PUE) and mitigate greenhouse gas (GHG) emissions are critical for developing strategies aimed toward agricultural green development. However, the potential effects of such synergies in the entire P supply chain through optimizing P management in crop production are poorly understood. In this study, a partial life cycle of a GHG emissions model was developed to quantify the P-related GHG emissions in the entire P supply chain in China. Our results showed that 16.3 kg CO2-equivalent (CO2-eq) was produced from the entire P supply chain per unit of P used for grain agriculture (maize, rice, and wheat). P-related GHG emissions in China increased more than five-fold from 1980 (7.2 Tg CO2-eq) to 2018 (44.9 Tg CO2-eq). GHG emissions were found to be strongly associated with the intensity of grain production in China, and they varied considerably across production regions owing to the differences in the P fertilizer production efficiency. Mineral P fertilizer use in crop production was the primary source of P-related GHG emissions. The results suggest that sustainable P management by matching mineral P fertilizer rates and fertilizer types with crop needs can mitigate GHG emissions by 10.8–27.7 Tg (24.0–65.1%). Moreover, this can improve PUE and reduce mineral P input by 0.7–1.4 Tg (24.0–46.0%). These findings highlight that potential synergies between high PUE and low P-related GHG emissions can be achieved via sustainable P management, thereby enhancing green agricultural development in China and other regions worldwide.

Can the Framing of Climate Mitigation Actions into Government Policies Lead to Delivering Them? – Insights from Nepal’s Experience

Many low-income countries (LICs), including Nepal, endeavour to deliver climate mitigation by reducing greenhouse gas (GHG) emissions and achieving more sustainable resource consumption. However, their prospects of delivering on such goals alongside the rapid structural changes in the economy prevalent in the LICs are not clear. This research aims to better understand the underlying complexity in the linkage between the framing of climate mitigation actions into government policies and the prospects for their delivery. We use critical discourse analysis, post-structural discourse analysis, and thematic analysis of textual data corpus generated from government policies (n = 12) and semi-structured interviews (n = 12) with policy actors, such as government policymakers and private sector and non-government organisations’ representatives. We also develop energy and material consumption and GHG emissions models to predict their values up to 2050 via the R tools and machine learning algorithms that validate the accuracy of models. Our findings suggest that the social context of policymaking creates a knowledge structure on climate mitigation which is reflected in government policies. The policy actors and their institutions exchange their ideas and interests in a deliberative and collaborative environment to prioritise policies for the energy, forest, and transport sectors to deliver climate mitigation actions in Nepal. However, the energy sector, together with the agriculture sector, has insufficient climate mitigation actions. Reflecting on the high proportion of biomass in the energy mix and the rapid rise in fossil fuel and energy consumption per capita—both of which are driven by the remittance inflows—this research suggests measures to reduce these in an absolute sense.

A life-cycle perspective for analyzing carbon neutrality potential of polyethylene terephthalate (PET) plastics in China

Production, consumption, and disposal of plastics are associated with the generation of a large amount of greenhouse gas (GHG). Polyethylene terephthalate (PET) is one of the most widely used plastics, which is mainly produced and consumed by China and causing increasing concerns. The previous studies mainly focused on flows and stocks of PET. Detailed information on GHG emissions for the entire life cycle of PET in China is limited. Particularly, the key paths of emission reduction for life-cycle PET considering carbon neutrality are unknown. In this research, a network analysis system and model of GHG emissions were developed for PET in China and helping explore characteristics of GHG emissions over the three development periods of the PET industry. The results showed that the most potential stage of carbon neutrality for PET was the stage of PET production, accounting for approximately 74.9% over 2000 to 2018. The manufacturing process of PET fibers and bottles would have a major contribution to GHG emissions. At the same time, GHG emissions from the mechanical recovery process should not be ignored. The plastic restriction order for PET and the waste treatment ways of low-carbon would have a significant contribution to emission reduction. According to the results, this study identified the most potential key process of carbon neutrality in the PET life cycle and proposed policies to reduce GHG emissions, which would provide scientific support for the PET industry to achieve the goal of carbon neutrality in China.

A Concurrence Optimization Model for Low-Carbon Product Family Design and the Procurement Plan of Components under Uncertainty

With the increase in pollution and people’s awareness of the environment, reducing greenhouse gas (GHG) emissions from products has attracted more and more attention. Companies and researchers are seeking appropriate methods to reduce the GHG emissions of products. Currently, product family design is widely used for meeting the diverse needs of customers. In order to reduce the GHG emission of products, some methods for low-carbon product family design have been presented in recent years. However, in the existing research, the related GHG emission data of a product family are given as crisp values, which cannot assess GHG emissions accurately. In addition, the procurement planning of components has not been fully concerned, and the supplier selection has only been considered. To this end, in this study, a concurrence optimization model was developed for the low-carbon product family design and the procurement plan of components under uncertainty. In the model, the relevant GHG emissions were considered as the uncertain number rather than the crisp value, and the uncertain GHG emissions model of the product family was established. Meanwhile, the order allocation of the supplier was considered as the decision variable in the model. To solve the uncertain optimization problem, a genetic algorithm was developed. Finally, a case study was performed to illustrate the effectiveness of the proposed approach. The results showed that the proposed model can help decision-makers to simultaneously determine the configuration of product variants, the procurement strategy of components, and the price strategies of product variants based on the objective of maximizing profit and minimizing GHG emission under uncertainty. Moreover, the concurrent optimization of low-carbon product family design and order allocation can bring the company greater profit and lower GHG emissions than just considering supplier selection in low-carbon product family design.

Evaluating greenhouse gas emission of transit agencies: A case study of Ontario, Canada

The objective of this research was to map out the greenhouse gas (GHG) footprint of Ontario transit agencies. A system-by-system GHG emissions model was developed to determine which transit agencies are low, medium, or high emitters based on the metric tonnes of carbon dioxide equivalent (CO2e) emitted from mobile sources. A score card was created based on four preliminary indicators: (1) CO2e per passenger, per revenue vehicle kilometer; (2) CO2e per passenger, per service area density; (3) CO2e per vehicle, per revenue vehicle kilometer; and (4) CO2e per vehicle, per service area density. This score card provides a relative metric to compare the GHG emissions of different transit agencies which can be used as an audit standard for quantifying comparative CO2e reductions. The results indicated that one-third of transit agencies operate in the “A” grade range, while the remaining agencies received grades of “B” to “E”; thus, demonstrating the potential for utilizing Green Funds in different jurisdictions to advance technology and ridership solutions aimed at GHG emissions reduction to achieve climate change objectives.

Utility-specific projections of electricity sector greenhouse gas emissions: a committed emissions model-based case study of California through 2050

The environmental profile of electricity is changing rapidly, motivating a need for provider- and time-specific estimates for accurate environmental assessment. This work shows that defensible, provider- and time-specific emissions projections can be derived from two major factors: committed emissions from existing power plants and policy restrictions on future system characteristics. This letter introduces a bottom–up, power plant-based model that projects utility-specific annual average greenhouse gas (GHG) intensity of electricity in the U.S. state of California for 2018–2050, believed to be the first openly available GHG emissions model with utility-specific projections. California is a useful case study for testing in part because of its strict regulatory GHG targets and the complexity of its electricity system, including limited asset ownership by utilities and substantial reliance on imported electricity. This plant-based approach to emissions projections bounds uncertainty in a way that less infrastructurally grounded approaches cannot. For example, emissions from unspecified sources of power can be estimated based on available plants. Based on historical power plant lifetimes, existing policy, and default model assumptions, the CO2 intensity of Californian electricity is projected to drop from 175 kg CO2/MWh (sales + losses, 2020) to 95 kg CO2/MWh by 2030, operationally decarbonizing by 2047. Upstream methane leakage increases GHG intensity of natural gas-fired power plants by about 30%, assuming a 100 year time horizon and national average estimates for leakage (which likely underestimate leakage for California). Although drivers like market conditions also affect future outcomes, California’s current policy targets do not appear to require early retirement for utility generation assets, though up to about two gigawatts of extant in-state merchant capacity might be affected. Under current policy, new generating assets must either comply with the 100% clean electricity standard by 2045 or stop selling in California before the end of their expected useful life.

Is there a joint lever? Identifying and ranking factors that determine GHG emissions and profitability on dairy farms in Bavaria, Germany

Farms are increasingly expected to contribute to greenhouse gas (GHG) mitigation actions to help governments to achieve GHG reduction commitments. In order to identify key mechanisms for GHG mitigation on farms, many studies use mass flow simulation or optimization models. However, by assuming “best practice” and not accounting for “real farm practices”, these models cannot predict variability between farms. In contrast, studies that include variability between farms can identify determinants that are important factors to reduce GHG emissions. From a farmer's perspective, it is often crucial that these mechanisms also increase farm profitability. The objectives of this article are (1) to explore factors that jointly affect GHG emissions and profitability of dairy farms and, (2) to assess if these factors cause synergies or trade-offs to simultaneously reduce GHG emissions and increase profitability. To assess variability between farms, we utilize detailed site- or farm-specific input variables for a large number of farms. To this end, we combined a detailed high quality dataset of 92 farms for the year 2013 in Bavaria, Germany. In relation to GHG emissions, we collected emission factors from national and international life cycle analysis databases, and applied national and site-specific GHG emission models. Our global sensitivity analysis identified five factors affecting GHG emissions per kg of fat and protein corrected milk in the following order of relative importance (i.e. proportion of farm variability explained): feed use efficiency (26%), weighted nitrogen balance (23%), site specific nitrogen emission factor (15%), milk yield (13%), and replacement rate (8%). Of these five factors, feed use efficiency and milk yield were also relatively important factors for profitability. However, milk yield is strongly interlinked with beef output, an important by-product of our sample dairy farms, and thus needs special attention when defining effective GHG reduction targets. Site-specific nitrogen emission factors cannot be influenced directly by farmers. This leaves three main determinants for farm variability between farms of GHG emissions i.e. on field nitrogen use efficiency, feed use efficiency and replacement rate. Since feed use efficiency was also identified as an important factor increasing profitability, this could be addressed by advisory services assessing synergies between profitability and GHG emissions. On field nitrogen use efficiency and replacement rate were not identified as an important factor affecting profitability and thus may be addressed by additional incentives for farmers, advisory service, or stricter regulations.

Impacts of dissolved oxygen control on different greenhouse gas emission sources in wastewater treatment process

Activated sludge wastewater treatment plant (WWTP) has been recognized as one of the main contributors of greenhouse gas (GHG). Several technologies have attempted to control GHG in WWTP but have resulted in limited impacts and caused secondary pollution. Recently, often calculating total GHG as an evaluation indicator, the GHG emissions from different GHG sources have rarely been investigated. In this study, focusing on the automation of WWTP process, an alternative solution is proposed to explore the effects of control implement on GHG reduction. Firstly, simulating with the influent data of 14-days stormy weather, based on the recognized benchmark simulation model No 1 (BSM1) that accounts for the reaction kinetics and material balances of microorganism, the on-site GHG emission models of four sub-processes in WWTP, including CO2 from endogenous decay, biochemical oxygen demand removal, nitrification, and N2O from denitrification, are developed, and the off-site CO2 and CH4 emissions caused by the sludge disposal, CO2 from energy for aeration, pumping and chemical usage, are also estimated. The simulation results show that the overall averaged GHG of 844.01 kg CO2-eq/h are emitted, where 25.83% emits from the on-site sub-processes and 74.17% discharges from the off-site sources. Subsequently, the traditional proportional-integral (PI) control strategy is applied to control the concentrations of dissolved oxygen (DO) in three aerated tanks. With the control actions, the DO-PI control systems manage to reduce the GHG emissions by 15.53 kg CO2-eq/h averagely, and the GHG reduction mainly comes from the sources of aeration and nitrification, which are reduced by 11.67 kg CO2-eq/h and 4.64 kg CO2-eq/h, respectively. But, there are few influences on the other sources. Summarily, the impacts of DO-PI control on the GHG emissions from different sources of WWTP are investigated, revealing that the automatic control system can be a practical and viable approach to achieve the GHG reduction in WWTP, which caters to the sustainable development of WWTP.

Life cycle greenhouse gas emissions of China shale gas

Having the largest shale gas reserve in the world, China is vigorously developing its shale gas resources. In this work, a hybrid life cycle inventory is used to estimate upstream greenhouse gas (GHG) emissions and evaluate the atmospheric environmental effects of China’s shale gas development. A comprehensive GHG emission model of the preproduction phase is constructed by considering green completion, methane emission from flowback water, indirect emissions from materials upstream material manufacturing, and Monte Carlo simulation. Results indicate that the upstream GHG emission of 230 shale gas wells is 18.8 g CO2eq/MJ. During the preproduction phase (from well pad preparation to well completion), indirect GHG emissions from upstream material manufacturing are substantial and account for 78%. The estimation of GHG emissions in preproduction phase presents considerable uncertainty (39%–79%) due to the uncertainty of constructing the pad preparation process and the estimated ultimate recovery. In preproduction phase, the GHG emission of China shale gas is estimated to be approximately 4.18 g CO2eq/MJ (95% CI: 2.57 to 7.47 g CO2eq/MJ), which is higher than that of the US shale gas.

Fallow season CO2 and CH4 fluxes from US mid-south rice-waterfowl habitats

Flooding rice fields in the US mid-south during the fallow season (November–March) for migratory waterfowl habitats provides revenue from hunting-related activities and helps with soil retention and water quality. However, flooding has the potential to increase greenhouse gas (GHG) emissions. Research on GHG emissions in US mid-south during the fallow season has been limited. Eddy covariance (EC) systems measuring CO2 and CH4 fluxes were installed during the 2015–2016, 2016–2017, and 2017–2018 fallow seasons at paired production-sized fields in NE Arkansas. Rice residue was burned after harvest at both field locations. The paired fields were separated by an access road and consisted of one field flooded (F) with precipitation by installing barriers to prevent drainage and the other field allowed to drain or non-flooded field (NF). Flooding did not influence CO2 emissions during the fallow season significantly. However, F fields emitted 45 ± 2% more CH4 than NF fields. The three-year fallow season average was 1408 ± 185 kg CO2-C ha−1 season-1 and 12.5 ± 2.0 kg CH4-C ha−1 season-1 in F fields and 1355 ± 132 kg CO2-C ha−1 season-1 and 8.6 ± 1.4 kg CH4-C ha−1 season-1 in NF fields. Methane contributed 5 to 10% global warming potential (GWP), reported as CO2 equivalents from CO2 and CH4 measurements. The three-year fallow season average GWP was not different by treatment. Increases in fallow season GWP were noted to increase after rice cultivation. These findings will improve our overall understanding and modeling of GHG emissions from agricultural fields during an understudied but important period of the year.

Modelling historical and potential future climate impacts on Keremeos Creek, an Okanagan-Similkameen watershed, British Columbia, Canada: Part II. Forecasting change in farm-level greenhouse gas emissions

This manuscript, Part II of a climate change impacts assessment series, describes changes in CO2, CH4, and N2O emissions and soil carbon storage on a theoretical farm in the Keremeos Creek watershed under scenarios of historical and potential climate using Holos v3.0.3 greenhouse gas (GHG) emissions model. The GENerate Earth SYstems Science input (GENESYS) spatial hydro-meteorological model outputs from Part I, which represent future hydro-meteorological conditions in the watershed under the ensemble averages of 15 General Circulation Models (GCMs) for two Representative Concentration Pathways (RCP) scenarios (RCP 4.5 and RCP 8.5) and three time periods (2011–2040, 2041–2070, and 2071–2100) relative to the 1961–1990 base period, are adapted to provide Holos v3.0.3 inputs. Adapting the GENESYS output facilitated Holos v3.0.3 modelling of farm GHG emissions to determine whether the simulated farm with both beef and crop production becomes a carbon sink, or source. The result demonstrated that there may be reductions in emissions related to enteric CH4, manure CH4, manure direct N2O, and indirect N2O for different climate change scenarios and time periods. However, there may also be increases in crop direct N2O and energy use CO2 for the same scenarios. Furthermore, soil carbon storage under fruit tree orchards is expected to increase. According to this result, the simulated farm in the Keremeos Creek watershed is a carbon sink and the carbon capture projects an increase to higher levels, depending on different climate change scenarios and time periods. However, if the simulation is conducted without fruit tree orchards as a carbon sink, the simulated farm in the Keremeos Creek watershed is a carbon source and the carbon emissions will increase for different climate change scenarios and time periods. These results demonstrate the importance of woody crops as a mitigation option for reducing farm-level GHG emissions in future.

Evaluation of Greenhouse Gas Emission Benefits of Vehicle Speed Limiters on On-Road Heavy-Duty Line-Haul Vehicles

Vehicle speed limiters (VSLs) are one of the strategies for reducing greenhouse gas (GHG) emissions from heavy-duty vehicles; they work by limiting peak vehicle speed. In the Federal Phase 2 Rule—GHG Emissions and Fuel Efficiency Standards for Medium- and Heavy-duty Engines and Vehicles, the greenhouse gas emission model (GEM) includes emission reduction credits for vehicles equipped with tamper-proof VSLs set at 55 to 65 miles per hour (mph). In this study, on-road testing of three class 8 combination tractor-trailers was conducted at various cruising speeds between 45 and 78 mph under steady conditions to evaluate the emission impacts of VSLs on heavy-duty vehicles. The three tested trucks were equipped with model year (MY) 2007 and newer engines and low-rolling-resistance tires on both the tractors and trailers. One truck was tested with a combined test weight of 35,000 lb and each of the other two trucks was tested with a combined test weight of 76,000 lb. A portable emission measurement system (PEMS) was used to measure carbon dioxide (CO2) emissions, and results showed that distance-based CO2 emission rates were dependent on both vehicle speed and engine revolutions per minute (rpm). Compared to average CO2 emission rates of approximately 1524 grams per mile (g/mile) at 78 mph for these test trucks, average CO2 emission rates at 51 mph were ~ 36% lower (approximately 978 g/mile) and represented the minimum distance-based (i.e., in g/mile) CO2 emission rates within the speed range evaluated for the three test trucks. Specifically, when decreasing speeds from 78 to 55 mph, CO2 emissions were between 0.7 and 2.6% lower per mph reduced. The emission benefits of using VSLs estimated from data in this study agree with the emission credits in the federal GEM. In addition, the quadratic relationship between CO2 emissions and vehicle speed measured for the three test trucks was used to corroborate updates to CARB’s EMission FACtor (EMFAC) 2017 model that were based on dynamometer testing; the measured trends from this VSL study support the predictions in that model.

A Theoretical Model for GHG Emissions Due to Biochar Application in Tropical Agricultural Soils

Core Ideas Biochar mitigates greenhouse gas (GHG) emissions when applied to soils. The reduction in biochar pH leads to higher GHG mitigation. Enhancing biochar pyrolysis temperature also leads to higher GHG mitigation. CO2 emission is explained negatively by the variables soil texture and biochar pH. CO2 emission is explained positively by pyrolysis temperature and biochar dose. Biochar, a pyrolysis product, has the potential to reduce greenhouse gas (GHG) emissions while increasing the soil water holding capacity. The purpose of this paper is to model biochars GHG emission based on its applied dose, its pH value, pyrolysis temperature and soil texture. Poultry manure and sugarcane straw biochar produced at two temperatures (350 and 650°C), were applied in three doses (12.5, 25, and 50 Mg ha−1), to sandy and clayey soils at two soil pH values (original 7.5 and adjusted to 5.5). The concentrations of CO2, CH4 and N2O were determined using gas chromatography. Increasing the biochar pyrolysis temperature and reducing the soil pH to 5.5 reduced the CO2, CH4, and N2O emissions from the soil. The mitigating effect of biochars was more evident in the sandy soil, since the clayey soil was highly buffed. The CO2 emission, explained negatively by the variables soil texture and biochar pH and positively by the variables pyrolysis temperature and biochar dose, had the final equation: CO2 = 82.28 – 0.05Cclay – 0.08pH + 6.98Tpyrolysis + 0.64Dbiochar (R2 = 0.65).

Considering Battery Degradation in Life Cycle Greenhouse Gas Emission Analysis of Electric Vehicles

Lithium ion batteries (LIBs) are predominantly employed to power Electric Vehicles (EVs). Battery degradation can significantly affect battery performance and further influence the energy consumptions and life cycle greenhouse gas (GHG) emissions from EVs. However, currently battery degradation has not been taken into account in life cycle assessment (LCA) of EVs. In this paper, we report our research on considering battery degradation into life cycle GHG emissions of EVs, by analyzing a mid-size EV using a 24 kWh Lithium-Manganese-Oxide (LMO)-graphite battery. Both cycling loss and calendar loss in battery degradation are analyzed under average driving conditions of U.S. The model is validated with experimental testing data on Nissan Leaf from Argonne National Lab. In this study, the required battery replacement is also integrated into the LCA GHG emission model and results, by considering the cradle-to-gate GHG emissions from a battery pack. This model can improve the accuracy and reliability of life cycle GHG studies on EVs.