Improving the temporal and spatial distribution of CO2 emissions from global fossil fuel emission data sets
Explore the source record for details and available documents.
Nassar, R↗
SEARCH · Engineering Papers
Results for “EMISSION”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The role of Chinese cities in greenhouse gas emissions reduction: Briefing on urban energy use and greenhouse gas emissions
Explore the source record for details and available documents.
Ohshita, Stephanie↗
Nanolaser Emission under Few-Emitter or Unity-Spontaneous-Emission-Factor Conditions (invited).
Abstract not provided.
Chow, Weng Wah↗
National Emission Standards for Hazardous Air Pollutants - Radionuclide Emissions Calendar Year 2024
Explore the source record for details and available documents.
Warren, Ronald W. [Mission Support and Test Servic↗
Emissions mitigation technology for advanced water-lean solvent-based CO 2 capture processes
This technical final report submitted to DOE/NETL presents all the research activities performed during the entirety of DE-FE0031660 project-Emissions Mitigation Technology for Advanced Water-Lean Solvent-Based CO 2 Capture Processes which spans from October 2018 through March 2022. RTI International has been conducting studies from fundamental and operational aspects to reduce the overall amine emissions from the advanced Water-Lean Solvent (WLS) systems, specifically RTI’s Non-Aqueous Solvent (NAS). This technical final report will highlight the key findings from project which align closely to the project objectives which are: Identify the contribution of vapor loss, entrainment, and aerosols to the overall emissions of water-lean systems; Determine the significance of CO 2 capture system operating parameters to the amine emissions; Develop an emissions model based on critical operating parameters; Evaluate the effectiveness of emissions mitigation devices to reduce the amine emissions to <1 ppm under flue coal-fired flue gas; and, Determine the contribution of the ECTs to the overall CO 2 capture cost. The following are the key findings based on numerous tests using both lab-scale setups and parametric testing performed at RTI’s Bench-scale Gas Absorption System (BsGAS). During the BP1, the aerosol generation system and monitoring equipment were installed at BsGAS to produce and determine the aerosol characteristics during the NAS CO 2 capture process. The aerosol produced by this setup produced aerosols with the peak diameter and concentration of 50 micron and 1.2E10 7 cm -3 , respectively. These particle sizes and concentrations are matched to those observed in the actual coal-fired power plant flue gases and expected to be found at the absorber inlet of the CO 2 capture system. Over 1,300 hours of parametric testing have been conducted to evaluate the impact of the aerosols and operating conditions during the CO 2 capture with NAS on the overall amine emissions in the treated flue gas. At the worse condition tested, the presence of the aerosols in the flue gas could increase the overall emissions by 10X compared to the baseline emissions from NAS’s vapor pressure. CO 2 capture rate was found to be a main factor impacting the overall emissions as well as aerosol size and concentrations in the absorber off-gas. The higher CO 2 capture rate, the higher amine emissions in the treated gas. The temperature difference between the temperature bulge seen in the absorber and the water wash temperature also impacts the particle growth where the larger the temperature difference, the more amine emissions from aerosols in the treated gas. The majority of the aerosols did not grow substantially in the system, and the particle concentrations remained nearly constant between the absorber inlet and wash outlet. Only a small portion of the particles were found to grow significantly. The high efficiency demister with mesh size of 5-10 micron can be installed to remove a portion of the aerosols from the gas stream leaving the water wash. Overall, these results from parametric testing have established the emission baseline and validate our assumption on the need of emission control technologies (ECT) in order to minimize the emissions from the baseline NAS CO 2 capture process. Over 2,000 of BsGAS operating hours was used to investigate a handful of process improvements which led to a selection of the vital few changes that effectively control the amine emissions. These process improvements are lime-coated-filters for absorber gas inlet, advanced demister at the top of the absorber, a second water wash with amine recovery unit were designed, installed, and tested at BsGAS at the end of BP1. The result showed that the NAS CO 2 capture process with these additional emission control devices could lower the amine emission in the treated gas to about 1 ppm using a simulated coal-fire flue gas stream. The main contributor in lowering the amine emission came from the second water wash with amine recovery unit where the amine concentration in the scrubbing water was kept below 2 wt% through a continuous amine removal via an adsorbent bed, resulting in a low amine vapor pressure. The adsorbent bed was regenerated via a direct steam regeneration and the recover amine was returned to the absorber to minimize wastewater and makeup amine. A flue gas generation system was designed and installed during the first half of BP2 to support the emission testing using a real coal-derived flue gas. The system is capable of generating both coal- and natural gas- derived flue gases with the composition of the gaseous species highly resemble to that of the power plant flue gases. The particulates detected in the coal-derived flue gas showed the mean diameter of 1 micron. The CO 2 capture operating was then proceed using the real coal-derived flue gas where the amine emission was controlled to be about 0-3 ppm for the total run time of about 200 hours. Similar testing was conducted with natural gas-derived flue gas and the result showed a highly amine emission of 30 ppm under the total run time of 200 hours. The Principal Component Analysis (PCA) and the Partial Least Squares Projection to Latent Structures (PLS) techniques were applied to the parametric testing data to derive a multivariate statistical model. The model was validated and trained with half of the data collected, and the predictive ability of the model was evaluated using the remaining half of the data. The resulting empirical model was capable of predicting the overall emissions from the NAS process without the ECTs with ±15% accuracy (average absolute deviation, AAD) in BP1. As more emission data were obtained under the real coal-flue gas in the BP2, the model incorporated these new set of data to reflect the final process configuration, operating parameters, and amine emission. This results in the updated empirical model predicting the amine emission from the NAS CO 2 capture process with 84% goodness-of-fit (R 2 ), 85% predictability (Q 2 ), and 15% AAD. The study evaluates the use of RTI’s Non-Aqueous Solvent technology for 90% CO 2 capture from a net 650 MWe pulverized coal power plant, downstream of the flue-gas desulfurization unit. The captured CO 2 has a purity of > 95% CO 2 , and is dried, compressed to 15.3 MPa (2,215 psia), ready for sequestration. The analysis uses Case B12B from the DOE Baseline study on Bituminous Coal, Revision 4 where the Cansolv CO 2 capture plant is replaced by the RTI CO 2 Capture plant. The CO 2 capture plant has been sized to capture >90% CO 2 from flue gas derived from a net 650 MWe supercritical pulverized coal power plant. The CO 2 capture plant is equipped with emission control technologies that limits the amine emissions to < 1 ppm. Two different cases were evaluated for the technoeconomic study. The key difference between the two cases is the regenerator pressure. In Case 1, the regenerator operates at 0.195 MPa (28.3 psia), whereas in Case 2, the regenerator pressure is 0.44 MPa (64 psia) thus removing the need for the first stage of compression of the eight-stage compression train. Results from the TEA are compared against the DOE reference cases for SCPC plant with and without CO 2 Capture (Case B12A and Case B12B of the DOE Baseline study, respectively). Case 2 with CO 2 regeneration at higher pressure results in the lower cost of CO 2 capture. The total capital cost of the capture process has been estimated using 2018 dollars in Aspen Process Economic Analyzer and was estimated to be $579 MM. The capture plant operation leads to a total parasitic power loss rate of 96 MWe, resulting in a decrease in pulverized coal power plant efficiency of 7.8% points. The resulting cost of electric power increases from 64.4 mills/kWh, for no capture, to 97.5 mills/kWh, with 90% capture, an increase of 51% in the COE. The cost of capturing 90% CO 2 was estimated to be $38.2/tonne-CO 2 , and meets the DOE target of $40/t-CO 2 . Emission control technologies (ECT) investigated in this project includes a second water wash with use of activated carbon beds for removal of amine from the wash water prior to recirculation in the water wash. These ECT allow operation of the CO 2 capture plant with < 1 ppm amine emissions with the treated flue gas and contributes to $2.4/t-CO 2 captured. Amine emissions derived from thermal and oxidative degradations were investigated under this project along with the emissions derived from aerosols for the NAS system. The thermally degraded of the lean NAS showed less than 4% decreased of the original total amine content in the NAS at 150 °C while the result obtained at 120 °C showed no drop in total amine content, suggesting that thermal degradation of the NAS is minimal. These results also suggested that the thermally degraded species are not likely formed and contributed to the emissions due to the low regeneration temperature of the NAS at 90-105 °C. The oxidative degradation, on the other hand, could become problematic as some of these oxidative degraded species were observed during the NAS-5 testing at National Carbon Capture Center (NCCC) and SINTEF in our previous project. The rapid screening of selected inhibitors suggested that oxidative degradation of NAS can be suppressed using thiol containing compounds in amounts of at least 1 mol%. The detailed mechanistic degradation pathway was conceived for a specific amine used in NAS formulation during BP2. he reduction of the nitrosamines caused by the NO x present in the flue gas was also examined. The study suggested that the thermo-chemical treatment of the NAS solvent would be a more effective and economically viable compared to removing NO x at the DCC.
01 COAL, LIGNITE, AND PEAT↗
A comprehensive and synthetic dataset for global, regional, and national greenhouse gas emissions by sector 1970–2018 with an extension to 2019
To track progress towards keeping global warming well below 2 °C or even 1.5 °C, as agreed in the Paris Agreement, comprehensive up-to-date and reliable information on anthropogenic emissions and removals of greenhouse gas (GHG) emissions is required. Here we compile a new synthetic dataset on anthropogenic GHG emissions for 1970–2018 with a fast-track extension to 2019. Our dataset is global in coverage and includes CO 2 emissions, CH 4 emissions, N 2 O emissions, as well as those from fluorinated gases (F-gases: HFCs, PFCs, SF 6 , NF 3 ) and provides country and sector details. We build this dataset from the version 6 release of the Emissions Database for Global Atmospheric Research (EDGAR v6) and three bookkeeping models for CO 2 emissions from land use, land-use change, and forestry (LULUCF). We assess the uncertainties of global greenhouse gases at the 90 % confidence interval (5th–95th percentile range) by combining statistical analysis and comparisons of global emissions inventories and top-down atmospheric measurements with an expert judgement informed by the relevant scientific literature. We identify important data gaps for F-gas emissions. The agreement between our bottom-up inventory estimates and top-down atmospheric-based emissions estimates is relatively close for some F-gas species (~ 10 % or less), but estimates can differ by an order of magnitude or more for others. Our aggregated F-gas estimate is about 10 % lower than top-down estimates in recent years. However, emissions from excluded F-gas species such as chlorofluorocarbons (CFCs) or hydrochlorofluorocarbons (HCFCs) are cumulatively larger than the sum of the reported species. Using global warming potential values with a 100-year time horizon from the Sixth Assessment Report by the Intergovernmental Panel on Climate Change (IPCC), global GHG emissions in 2018 amounted to 58 ± 6.1 GtCO 2 eq. consisting of CO 2 from fossil fuel combustion and industry (FFI) 38 ± 3.0 GtCO 2 , CO 2 -LULUCF 5.7 ± 4.0 GtCO 2 , CH 4 10 ± 3.1 GtCO 2 eq., N2O 2.6 ± 1.6 GtCO 2 eq., and F-gases 1.3 ± 0.40 GtCO 2 eq. Initial estimates suggest further growth of 1.3 GtCO 2 eq. in GHG emissions to reach 59 ± 6.6 GtCO 2 eq. by 2019. Our analysis of global trends in anthropogenic GHG emissions over the past 5 decades (1970–2018) highlights a pattern of varied but sustained emissions growth. There is high confidence that global anthropogenic GHG emissions have increased every decade, and emissions growth has been persistent across the different (groups of) gases. There is also high confidence that global anthropogenic GHG emissions levels were higher in 2009–2018 than in any previous decade and that GHG emissions levels grew throughout the most recent decade. While the average annual GHG emissions growth rate slowed between 2009 and 2018 (1.2 % yr –1 ) compared to 2000–2009 (2.4 % yr –1 ), the absolute increase in average annual GHG emissions by decade was never larger than between 2000–2009 and 2009–2018. Our analysis further reveals that there are no global sectors that show sustained reductions in GHG emissions. There are a number of countries that have reduced GHG emissions over the past decade, but these reductions are comparatively modest and outgrown by much larger emissions growth in some developing countries such as China, India, and Indonesia. There is a need to further develop independent, robust, and timely emissions estimates across all gases. As such, tracking progress in climate policy requires substantial investments in independent GHG emissions accounting and monitoring as well as in national and international statistical infrastructures. The data associated with this article (Minx et al., 2021) can be found at https://doi.org/10.5281/zenodo.5566761.
54 ENVIRONMENTAL SCIENCES↗
Update of Emission Factors of Greenhouse Gases and Criteria Air Pollutants, and Generation Efficiencies of the U.S. Electricity Generation Sector
The last decade has seen a steady evolution of the electricity generation sector. Fuels used for electricity generation have shifted from coal to cleaner energy sources such as natural gas and renewables including solar, wind, and other renewable sources. The share of U.S. electricity generated from coal decreased from 45% in 2010 to 24% in 2019, and is expected to decrease further to 13% by 2050. The conversion efficiency of electricity generation has also increased gradually for fuels such as natural gas due as less-efficient old generators are retired and more-efficient generators replace them. These changes in the electricity generation industry are likely to cause changes in the emissions from power generation units. Emission factors of greenhouse gases (GHG) including CO 2 , CH 4 , and N 2 O, and criteria air pollutants (CAPs) including CO, NO x , PM 10 , PM 2.5 , and SO x , from power plants are important parameters for estimating life-cycle emissions associated with vehicle electrification, energy systems, and the production of materials and chemicals. The electricity generation technologies and associated emission factors in the Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) model need to be updated to reflect recent developments in the electricity generation sector. The most recent update of the electricity generation emission factors in GREET adopted a mixed method. The emission factors of CH 4 , N 2 O, NO x , and SOx were estimated using a “topdown” approach by dividing the total emissions by the total net electricity generation, because emission data of these pollutants are readily available in the Emissions & Generation Resource Integrated Database (eGRID). For other CAPs such as CO, VOC, PM 10 , and PM 2.5 , emission data were not reported in eGRID. A “bottom-up” method was used to estimate the emission factors for these pollutants by considering generic uncontrolled emission factors and the pollutant removal efficiencies of emission control technologies adopted in the electricity generation sector. However, the uncontrolled emission factors and the emission removal efficiencies of various emission control technologies considered in the 2012 study came from the legacy AP-42 emission factors, and may not reflect the actual emission performances of the electricity generation sector of today. To leverage new data that recently became available, especially emission data measured from continuous emission monitoring systems (CEMS), we developed a new “top-down” approach to estimate efficiencies and GHG and CAP emission factors for electricity generation from combustion of individual fuel types by individual combustion technologies on the basis of power-generation data from U.S. Energy Information Administration’s (EIA’s) form EIA-923, and plant emission data from Environmental Protection Agency’s (EPA’s) Clean Air Markets Division (CAMD) dataset and National Emissions Inventory (NEI) dataset. Detailed discussion of the method and data used in this study can be found in Section 2.1. With this topdown approach, we aim to improve the estimates of energy efficiencies and emission factors for power plants using a more consistent methodology, and to update the emission factors, generation efficiencies, and generation technologies mixes in GREET to reflect recent technology advancements in the electricity generation sector.
20 FOSSIL-FUELED POWER PLANTS↗
Global Emissions of Terpenoid VOCs from Terrestrial Vegetation in the Last Millennium
We investigated the millennial variability of global BVOC emissions by using two independent numerical models: The Model of Emissions of Gases and Aerosols from Nature (MEGAN), for isoprene, monoterpene and sesquiterpene and Lund-Potsdam-Jena General Ecosystem Simulator (LPJ8 GUESS), for isoprene and monoterpenes. We found the millennial trends of global isoprene emissions to be mostly affected by land cover and atmospheric carbon dioxide changes, whereas monoterpene and sesquiterpene emission were dominated by temperature change. Isoprene emissions declined substantially in regions with large and rapid land cover change. In addition, isoprene emission sensitivity to drought proved to have signicant short term global effects. By the end of the past millennium MEGAN isoprene emissions were 634 TgC yr-1 (13% and 19% less than during during 1750-1850 and 1000- 15 1200, respectively) and LPJ-GUESS emissions were 323 TgC yr-1 (15% and 20% less than during 1750-1850 and 1000-1200, respectively). Monoterpene emissions were 89 TgC yr-1 (10% and 6% higher than during 1750-1850 and 1000-1200, respectively) in MEGAN, and 24 TgC yr-1 (2% higher and 5% 19 20 less than during 1750-1850 and 1000-1200, respectively) in LPJ-GUESS. MEGAN sesquiterpene emissions were 36 TgC yr-1 (10% and 4% higher than during1750-1850 and 1000-1200, respectively). Although both models capture similar We investigated the millennial variability of global BVOC emissions by using two independent numerical models: The Model of Emissions of Gases and Aerosols from Nature (MEGAN), for isoprene, monoterpene and sesquiterpene and Lund-Potsdam-Jena General Ecosystem Simulator (LPJ8GUESS), for isoprene and monoterpenes. We found the millennial trends ofglobal isoprene emissions to be mostly a*ected by land cover and atmospheric carbon dioxide changes, whereas monoterpene and sesquiterpene emission were dominated by temperature change. Isoprene emissions declined substantially in regions with large and rapid land cover change. In addition, isoprene emission sensitivity to drought proved to have signifcant short term global effects. By the end of the past millennium MEGAN isoprene emissions were 634 TgC yr-1 (13% and 19% less than during during 1750-1850 and 1000- 1200, respectively) and LPJ-GUESS emissions were 323 TgC yr-1 (15% and 16 17 20% less than during 1750-1850 and 1000-1200, respectively). Monoterpene emissions were 89 TgC yr-1 (10% and 6% higher than during 1750-1850 and 18 1000-1200, respectively) in MEGAN, and 24 TgC yr-1 (2% higher and 5% less than during 1750-1850 and 1000-1200, respectively) in LPJ-GUESS. MEGAN sesquiterpene emissions were 36 TgC yr-1 (10% and 4% higher than during1750-1850 and 1000-1200, respectively). Although both models capture similar emission trends, the magnitude of the emissions are different. This highlights the importance of building better constraints on VOC emissions from terrestrial vegetation.emission trends, the magnitude of the emissions are different. This highlights the importance of building better constraints on VOC emissions from terrestrial vegetation.
Acosta Navarro, J. C.↗
Evaluating Bay Area Methane Emission Inventory
As a regulatory agency, evaluating and improving estimates of methane (CH4) emissions from the San Francisco Bay Area is an area of interest to the Bay Area Air Quality Management District (BAAQMD). Currently, regional, state, and federal agencies generally estimate methane emissions using bottom-up inventory methods that rely on a combination of activity data, emission factors, biogeochemical models and other information. Recent atmospheric top-down measurement estimates of methane emissions for the US as a whole (e.g., Miller et al., 2013) and in California (e.g., Jeong et al., 2013; Peischl et al., 2013) have shown inventories underestimate total methane emissions by ~ 50% in many areas of California, including the SF Bay Area (Fairley and Fischer, 2015). The goal of this research is to provide information to help improve methane emission estimates for the San Francisco Bay Area. The research effort builds upon our previous work that produced methane emission maps for each of the major source sectors as part of the California Greenhouse Gas Emissions Measurement (CALGEM) project (http://calgem.lbl.gov/prior_emission.html; Jeong et al., 2012; Jeong et al., 2013; Jeong et al., 2014). Working with BAAQMD, we evaluate the existing inventory in light of recently published literature and revise the CALGEM CH4 emission maps to provide better specificity for BAAQMD. We also suggest further research that will improve emission estimates. To accomplish the goals, we reviewed the current BAAQMD inventory, and compared its method with those from the state inventory from the California Air Resources Board (CARB), the CALGEM inventory, and recent published literature. We also updated activity data (e.g., livestock statistics) to reflect recent changes and to better represent spatial information. Then, we produced spatially explicit CH4 emission estimates on the 1-km modeling grid used by BAAQMD. We present the detailed activity data, methods and derived emission maps by sector. In total, we estimate the anthropogenic emissions for BAAQMD to be 116.4 Gg (1 Gg = 109 g) CH4/yr, with a likely uncertainty of ~ 50% or more (e.g., NRC, 2010; US-EPA, 2015). Including the emissions from wetland (Jeong et al., 2013), the total CH4 emission estimate for BAAQMD is 120.1 Gg CH4/yr. Table 1 summarizes the estimated CH4 emissions for 2011 by sector. The sectors were categorized following those that are used in recent regional emission quantification studies (e.g., Jeong et al., 2013; Peischl et al., 2013; Wecht et al., 2014). However, we note that this result is marginally lower than the top-down estimate of 240 ± 60 Gg CH4/yr (at 95% confidence) reported by Fairley and Fischer (2015), suggesting some combination of systematic error in the top-down estimate, underestimation of emissions from known sources, or as yet unidentified sources may be present. With respect to the relative contributions from different source sectors, the CH4 emissions from the region are dominated by urban activities. Landfill emissions represent 53% of the District’s total emission followed by livestock (16%) and natural gas (15%). These three dominant sectors account for 84% of the total anthropogenic emission in BAAQMD. This suggests that mitigation efforts need to focus on these three sources. Figure 1 shows the gridded anthropogenic CH4 emissions on the BAAQMD’s 1-km grid. In general, the spatial pattern of emissions follows the density of population while strong point sources are also distributed in the rural areas of the District. Detailed methods and emissions for each sector and county are described in the following sections.
54 ENVIRONMENTAL SCIENCES↗
Inventory of China's Energy-Related CO2 Emissions in 2008
Although China became the world's largest emitter of energy-related CO{sub 2} emissions in 2007, China does not publish annual estimates of CO{sub 2} emissions and most published estimates of China's emissions have been done by other international organizations. Undertaken at the request of the Energy Information Administration (EIA) of the US Department of Energy, this study examines the feasibility of applying the EIA emissions inventory methodology to estimate China's emissions from published Chinese data. Besides serving as a proof of concept, this study also helps develop a consistent and transparent method for estimating China's CO{sub 2} emissions using an Excel model and identified China-specific data issues and areas for improvement. This study takes a core set of data from the energy balances published in the China Energy Statistical Yearbook 2009 and China Petrochemical Corporation Yearbook 2009 and applies the EIA's eight-step methodology to estimate China's 2008 CO{sub 2} emissions. First, China's primary and secondary fuel types and consumption by end use are determined with slight discrepancies identified between the two data sources and inconsistencies in product categorization with the EIA. Second, energy consumption data are adjusted to eliminate double counting in the four potential areas identified by EIA; consumption data from China's Special Administrative Regions are not included. Physical fuel units are then converted to energy equivalents using China's standard energy measure of coal equivalent (1 kilogram = 29.27 MJ) and IPCC carbon emissions coefficients are used to calculate each fuel's carbon content. Next, carbon sequestration is estimated following EIA conventions for other petroleum products and non-energy use of secondary fuels. Emissions from international bunker fuels are also subtracted under the 'reference' calculation of estimating apparent energy consumption by fuel type and the 'sectoral' calculation of summing emissions across end-use sectors. Adjustments for the China-specific conventions of reporting foreign bunkers and domestic bunkers fueling abroad are made following IPCC definitions of international bunkers and EIA reporting conventions, while the sequestration of carbon in carbon steel is included as an additional adjustment. Under the sectoral approach, fuel consumption of bunkers and other transformation losses as well as gasoline consumption are reallocated to conform to EIA sectoral reporting conventions. To the extent possible, this study relies on official energy data from primary sources. A limited number of secondary sources were consulted to provide insight into the nature of consumption of some products and to guide the analysis of carbon sequestered in steel. Beyond these, however, the study avoided trying to estimate figures where directly unavailable, such as natural gas flaring. As a result, the basic calculations should be repeatable for other years with the core set of data from National Bureau of Statistics and Sinopec (or a similarly authoritative source of oil product data). This study estimates China's total energy-related CO{sub 2} emissions in 2008 to be 6666 Mt CO{sub 2}, including 234.6 Mt of non-fuel CO{sub 2} emissions and 154 Mt of sequestered CO{sub 2}. Bunker fuel emissions in 2008 totaled 15.9 Mt CO{sub 2}, but this figure is underestimated because fuel use by Chinese ship and planes for international transportation and military bunkers are not included. Of emissions related to energy consumption, 82% is from coal consumption, 15% from petroleum and 3% from natural gas. From the sectoral approach, industry had the largest share of China's energy-related CO{sub 2} emissions with 72%, followed by residential at 11%, transport and telecommunications at 8%, and the other four (commerce, agriculture, construction and other public) sectors having a combined share of 9%. Thermal electricity and (purchased) heat (to a lesser degree) are major sources of fuel consumption behind sectoral emissions, responsible for 2533 Mt CO2 and 321 Mt CO{sub 2}, respectively. The 2008 emissions estimated for China in this study falls within the range of other international estimates, and suggests that the EIA methodology can be adopted to estimate China's emissions if the proper adjustments are made. While these results are helpful in understanding China's annual emissions, several key areas of data challenges affect the accuracy of this estimate. Industrial process-based emissions - an important source of emissions given China's industry-intensive economy and size of its cement sector - have not been included in this calculation and could be the focus of further model refinement. The accuracy of the Chinese emissions estimate can be further improved by addressing two unreported international bunker categories and developing China-specific carbon sequestration coefficients for non-fuel use energy products.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
New Directions: GEIA’s 2020 Vision for Better Air Emissions Information
We are witnessing a crucial change in how we quantify and understand emissions of greenhouse gases and air pollutants, with an increasing demand for science-based transparent emissions information produced by robust community efforts. Today’s scientific capabilities, with near-real-time in-situ and remote sensing observations combined with forward and inverse models and a better understanding of the controlling processes, are contributing to this transformation and providing new approaches to derive, verify, and forecast emissions (Tong et al., 2011; Frost et al., 2012) and to quantify their impacts on the environment (e.g., Bond et al., 2013). At the same time, the needs for emissions information and the demands for their accuracy and consistency have grown. Changing economies, demographics, agricultural practices, and energy sources, along with mandates to evaluate emissions mitigation efforts, demonstrate compliance with legislation, and verify treaties, are leading to new challenges in emissions understanding. To quote NOAA Senior Technical Scientist David Fahey, "We are in the Century of Accountability. Emissions information is critical not only for environmental science and decision-making, but also as an instrument of foreign policy and international diplomacy." Emissions quantification represents a key step in explaining observed variability and trends in atmospheric composition and in attributing these observed changes to their causes. Accurate emissions data are necessary to identify feasible controls that reduce adverse impacts associated with air quality and climate and to track the success of implemented policies. To progress further, the international community must improve the understanding of drivers and contributing factors to emissions, and it must strengthen connections among and within different scientific disciplines that characterize our environment and entities that protect the environment and influence further emissions. The Global Emissions InitiAtive, GEIA (http://www.geiacenter.org/), is a center for emissions information exchange and competence building created in 1990 in response to the need for high quality global emissions data (Graedel et al., 1993). While the past two decades have seen considerable progress in developing, improving and assessing emission estimates, emissions continue to be a major contributor to overall uncertainty in atmospheric model simulations. Moving forward, GEIA aims to help build emissions knowledge in a rapidly evolving society by: 1) enhancing understanding, quantification, and analysis of emissions processes; 2) improving access to emissions information; and 3) strengthening the community of emissions groups involved in research, assessment, operations, regulation and policy.
Frost, G. J.↗
Integrated Component-Specific Measurements to Develop Emission Factors for Compressors and Gas Gathering Lines
The Department of Energy (DOE) National Energy Technology Laboratory (NETL) and other critical stakeholders (e.g., U.S. Environmental Protection Agency (USEPA), state regulators, and industry) seek to more accurately characterize and quantify methane emissions from compressors and gathering lines within the Gathering and Boosting (G&B) segment of the natural gas system network. These efforts will reduce uncertainties in emissions estimates in USEPA’s current Green House Gas Inventory (GHGI) under their Greenhouse Gas Reporting Program (GHGRP). For this study, four G&B stations in the Gulf Coast Area were visited four times over the course of one year. Components were classified according to equipment and component type and emissions from select components were measured using multiple measurement technologies. The following major equipment types were considered: i) compressors, ii) separators, iii) dehydrators, iv) coalescers, v) slug catchers, vi) yard piping, vii) tanks, and viii) gathering lines. Individual components on each equipment type were further divided into the following main categories: i) connectors, ii) valves, iii) pressure relief valves (PRV), iv) meters, v) gauges, vi) regulators, vii) pneumatic device vents, and viii) compressor vents (e.g., distance piece vents [DPVs] and rod packing vents [RPVs]). Components were screened for the presence of leaks and venting emissions with an optical gas imaging (OGI) camera, and methane emission rates were measured using a microdilution high volume sampler. Downwind emissions were measured using an Open Path Fourier Transform Infrared (OP-FTIR) Spectrometer. Data were evaluated to assess correlation with site, equipment, time, and operational variables. Key findings and recommendations from the study include: A) Different counting methodologies can result in vastly different component counts, thus influencing total emissions calculations using Subpart W population EFs. A standard methodology should be adopted for classifying and counting components in order to minimize inconsistencies across studies and instruct stakeholders responsible for GHGI reporting. B) Results support current component subcategories listed in USEPA Subpart W, including the subdivision of connectors (flanged and “other”) and pneumatic device vents (intermittent, continuous low bleed, and continuous high bleed). C) Emission rates vary across sites and a relationship between higher emissions and compressor age was observed for some components. D) Component emission rates varied between field campaigns. The type of components contributing to overall measured emissions also varied among field campaigns. However, the emission rate variability was larger between different components (of the same category) than the variability in one component over the four field campaigns. Therefore, when calculating emission factors, it is not necessary to take repeat measurements to capture component variability. E) Measuring the emission rate of a sample for an extended period of time is not necessary to capture the average component emission rate. F) For reporting purposes, excluding liquid lines from site-wide screening may have a negligible effect on component-related emissions. However, from a site management perspective, malfunctioning components were not uncommon on liquid lines and contributed substantially to fugitive emissions at some sites. G) Population EFs, whether including or excluding liquid lines, were a conservative estimate of fugitive emissions. Leaker EFs consistently underestimated emissions at the field sites. Measurements at more sites, particularity sites in other areas of the country, need to be completed before determining the accuracy of EFs.
03 NATURAL GAS↗
Quantification of greenhouse gas emissions from a biological waste treatment facility
Highlights: • Whole-site fugitive emissions were measured at a biowaste treatment facility. • Methane and nitrous oxide were quantified using a tracer gas dispersion technique. •The daily average whole-site methane emission was 25.6 kg CH{sub 4} h{sup −1}. • Whole-site fugitive emissions were up to eight times higher than on-site emissions. • The emissions had a significant influence on the overall environmental impact - Abstract: Whole-site emissions of methane and nitrous oxide, from a combined dry anaerobic digestion and composting facility treating biowaste, were quantified using a tracer dispersion technique that combines a controlled tracer gas release from the treatment facility with time-resolved concentration measurements downwind of the facility. Emission measurements were conducted over a period of three days, and in total, 80 plume traverses were obtained. On-site screening showed that important processes resulting in methane emissions were aerobic composting reactors, anaerobic digester reactors, composting windrows and the site’s biofilter. Average whole-site methane emissions measured during the three days were 27.5 ± 7.4, 28.5 ± 6.1 and 30.1 ± 11.4 kg CH{sub 4} h{sup −1}, respectively. Turning the windrows resulted in an increase in methane emission from about 26.3–35.9 kg CH{sub 4} h{sup −1}. Lower emissions (21.5 kg CH{sub 4} h{sup −1}) were measured after work hours ended, in comparison to emissions measured during the facility’s opening hours (30.2 kg CH{sub 4} h{sup −1}). Nitrous oxide emission was too small for a downwind quantification. Direct on-site measurements, however, suggested that the main part of the emitted nitrous oxide came from the biofilter (about 1.4 kg N{sub 2}O h{sup −1}). Whole-site emissions were compared to emissions previously measured at different point sources on-site. Whole-site fugitive emissions were three to eight times higher than the sum of emissions measured at on-site sources. The magnitude of the emissions had a significant influence on the overall environmental impact of the treatment facility, assessed by consequential life cycle assessment. Including the higher whole-site fugitive emissions led to an increase in global warming potential, from a saving of 97 kg CO{sub 2}-eq. tonne{sup −1} of treated waste (wet weight) to a loading of 71 kg CO{sub 2}-eq. tonne{sup −1}, ultimately flipping the environmental profile of the treatment facility.
09 BIOMASS FUELS↗
Russian anthropogenic black carbon: Emission reconstruction and Arctic black carbon simulation
Development of reliable source emission inventories is needed to advance the understanding of the origin of Arctic haze using chemical transport modeling. This paper develops a regional anthropogenic black carbon (BC) emission inventory for the Russian Federation, the largest country by land area in the Arctic Council. Activity data from combination of local Russia information and international resources, emission factors based on either Russian documents or adjusted values for local conditions, and other emission source data are used to approximate the BC emissions. Emissions are gridded at a resolution of 0.1° × 0.1° and developed into a monthly temporal profile. Total anthropogenic BC emission of Russia in 2010 is estimated to be around 224 Gg. Gas flaring, a commonly ignored black carbon source, contributes a significant fraction of 36.2% to Russia's total anthropogenic BC emissions. Other sectors, i.e., residential, transportation, industry, and power plants, contribute 25.0%, 20.3%, 13.1%, and 5.4%, respectively. Three major BC hot spot regions are identified: the European part of Russia, the southern central part of Russia where human population densities are relatively high, and the Urals Federal District where Russia's major oil and gas fields are located but with sparse human population. BC simulations are conducted using the hemispheric version of Community Multi-scale Air Quality Model with emission inputs from a global emission database EDGAR (Emissions Database for Global Atmospheric Research)-HTAPv2 (Hemispheric Transport of Air Pollution) and EDGAR-HTAPv2 with its Russian part replaced by the newly developed Russian BC emissions, respectively. The simulation using the new Russian BC emission inventory could improve 30–65% of absorption aerosol optical depth measured at the AERONET sites in Russia throughout the whole year as compared to that using the default HTAPv2 emissions. At the four ground monitoring sites (Zeppelin, Barrow, Alert, and Tiksi) in the Arctic Circle, surface BC simulations are improved the most during the Arctic haze periods (October–March). The poor performance of Arctic BC simulations in previous studies may be partly ascribed to the Russian BC emissions built on out-of-date and/or missing information, which could result in biases to both emission rates and the spatial distribution of emissions. Finally, this study highlights that the impact of Russian emissions on the Arctic haze has likely been underestimated, and its role in the Arctic climate system needs to be reassessed. The Russian black carbon emission source data generated in this study can be obtained via http://abci.ornl.gov/download.shtml or http://acs.engr.utk.edu/Data.php.
54 ENVIRONMENTAL SCIENCES↗
Pacific Northwest National Laboratory Facility Radionuclide Emission Points and Sampling Systems
Battelle–Pacific Northwest Division operates numerous research and development laboratories in Washington State. The U.S. Department of Energy (DOE) contracts to Battelle at Richland facilities on both the DOE Hanford Site and the Pacific Northwest National Laboratory (PNNL) Richland campus. These facilities have the potential for radionuclide air emissions. The PNNL contract with DOE also includes operations at the PNNL-Sequim campus in Sequim, where there is also the potential for radionuclide air emissions. This document is a periodic update that describes current PNNL facility emission units and sampling systems. The National Emission Standard for Hazardous Air Pollutants (NESHAP [40 Code of Federal Regulations 61, Subpart H]) requires an assessment of all emission units that have the potential for radionuclide air emissions. Emission units are registered with the State of Washington. Potential emissions from emission units are assessed annually by PNNL staff. Sampling, monitoring, and other regulatory compliance requirements are designated based on the potential to-emit dose criteria, a graded approach to facility-identified potential impact categories, and regulatory requirements. The purpose of this document is to describe the facility radionuclide air emission sampling program and provide current and historical facility emission unit system performance, operation, and design information. For sampled emission units, the building, exhaust unit, control technologies, and sample extraction details are provided. Additionally, applicable configuration drawings, figures, and photographs are included. For non-sampled emission units, emission estimation and radionuclide source details are provided. Site-wide permits for the lowest potential impact category are described. Deregistered/transitioned emission unit details are also provided as necessary for at least 5 years post-closure/transition. Currently, five emission units are sampled continuously for particulate radionuclides at PNNL managed facilities on the PNNL-Richland campus (3 of the 5) and on the Hanford Site (2 of the 5). Four of these units have sampling systems that comply with the American National Standards Institute/Health Physics Society (ANSI/HPS) N13.1–2011 standard for sampling from stacks and ducts of nuclear facilities, and the fifth is grandfathered and compliant with the older ANSI N13.1–1969 standard. In addition, the PNNL-managed Hanford Site 325 Building EP 325-01-S stack is sampled continuously for emissions of tritium. No emissions sampling is required for the single licensed emission unit on the PNNL-Sequim campus.
54 ENVIRONMENTAL SCIENCES↗
The Open-source Data Inventory for Anthropogenic CO 2 , version 2016 (ODIAC2016): a global monthly fossil fuel CO 2 gridded emissions data product for tracer transport simulations and surface flux inversions
The Open-source Data Inventory for Anthropogenic CO 2 (ODIAC) is a global high-spatial-resolution gridded emissions data product that distributes carbon dioxide (CO 2 ) emissions from fossil fuel combustion. The emissions spatial distributions are estimated at a 1 × 1 km spatial resolution over land using power plant profiles (emissions intensity and geographical location) and satellite-observed nighttime lights. This work describes the year 2016 version of the ODIAC emissions data product (ODIAC2016) and presents analyses that help guide data users, especially for atmospheric CO 2 tracer transport simulations and flux inversion analysis. Since the original publication in 2011, we have made modifications to our emissions modeling framework in order to deliver a comprehensive global gridded emissions data product. Major changes from the 2011 publication are (1) the use of emissions estimates made by the Carbon Dioxide Information Analysis Center (CDIAC) at the Oak Ridge National Laboratory (ORNL) by fuel type (solid, liquid, gas, cement manufacturing, gas flaring, and international aviation and marine bunkers); (2) the use of multiple spatial emissions proxies by fuel type such as (a) nighttime light data specific to gas flaring and (b) ship/aircraft fleet tracks; and (3) the inclusion of emissions temporal variations. Using global fuel consumption data, we extrapolated the CDIAC emissions estimates for the recent years and produced the ODIAC2016 emissions data product that covers 2000–2015. Our emissions data can be viewed as an extended version of CDIAC gridded emissions data product, which should allow data users to impose global fossil fuel emissions in a more comprehensive manner than the original CDIAC product. Our new emissions modeling framework allows us to produce future versions of the ODIAC emissions data product with a timely update. Such capability has become more significant given the CDIAC/ORNL's shutdown. The ODIAC data product could play an important role in supporting carbon cycle science, especially modeling studies with space-based CO 2 data collected in near real time by ongoing carbon observing missions such as the Japanese Greenhouse gases Observing SATellite (GOSAT), NASA's Orbiting Carbon Observatory-2 (OCO-2), and upcoming future missions. The ODIAC emissions data product including the latest version of the ODIAC emissions data (ODIAC2017, 2000–2016) is distributed from http://db.cger.nies.go.jp/dataset/ODIAC/ with a DOI (https://doi.org/10.17595/20170411.001).
54 ENVIRONMENTAL SCIENCES↗
Calculating Methane Emissions from Offshore Facilities Using Bottom-Up Methods
With changing demands in regulation, understanding methane emissions from offshore oil and gas production infrastructure has become increasingly important. Reported emissions from facilities in the Gulf of Mexico range from zero to thousands of tons of methane per hour, but these is currently no clear understanding of how this range compares to expected emissions from normally operating facilities. To generate realistic emission estimates, we create two bottom-up models that simulate emissions from facilities operating in the Gulf of Mexico. We estimate type 1 prototypical facilities (typically unmanned, older, lower-producing platforms in shallow water with little processing equipment, compressors, or storage tanks) to emit an average of 13 kg CH 4 h −1 , which corresponds to a loss of 2.7% of the average facility production. Type 2 prototypical facilities (continuously manned, higher production and operate in deeper water with processing equipment, oil storage tanks, compressors and power generation) emit an average of 88 kg CH 4 h −1 , which corresponds to a loss of 2.5% of production. The average measured emission from type 1 facilities was 18 kg CH 4 h −1 with a median production loss estimated at 8%. The average measured emission from type 2 facilities was 36 kg CH 4 h −1 with a median production loss estimated at 2.4%. Using emission factors that consider the long-tail emission distribution partly reconciles the difference between modelled and measured emission estimates, but we suggest the current the fugitive emission estimate may be an underestimate and more data on the number and size of fugitive emissions could explain differences between the modelled and measured emission estimate. We suggest the bottom-up approach described here that uses production data coupled with facility equipment could be used to identify facilities that have abnormally large measured emissions, caused by methodological failure or larger than expected fugitive emissions, which should be targeted for further evaluation resulting in remeasurement or identification of source type so that a more accurate estimates can be made on the absolute emission.
bottom-up↗
Quantification of oil and gas methane emissions in the Delaware and Marcellus basins using a network of continuous tower-based measurements
Abstract. According to the United States Environmental Protection Agency (US EPA), emissions from oil and gas infrastructure contribute 30 % of all anthropogenic methane (CH4) emissions in the US. Studies in the last decade have shown emissions from this sector to be substantially larger than bottom-up assessments, including the EPA inventory, highlighting both the increased importance of methane emissions from the oil and gas sector in terms of their overall climatological impact and the need for independent monitoring of these emissions. In this study we present continuous monitoring of regional methane emissions from two oil and gas basins using tower-based observing networks. Continuous methane measurements were taken at four tower sites in the northeastern Marcellus basin from May 2015 through December 2016 and five tower sites in the Delaware basin in the western Permian from March 2020 through April 2022. These measurements, an atmospheric transport model, and prior emission fields are combined using an atmospheric inversion to estimate monthly methane emissions in the two regions. This study finds the mean overall emission rate from the Delaware basin during the measurement period to be 146–210 Mg CH4 h−1 (energy-normalized loss rate of 1.1 %–1.5 %, gas-normalized rate of 2.5 %–3.5 %). Strong temporal variability in the emissions was present, with the lowest emission rates occurring during the onset of the COVID-19 pandemic. Additionally, a synthetic model–data experiment performed using the Delaware tower network shows that the presence of intermittent sources is not a significant source of uncertainty in monthly quantification of the mean emission rate. In the Marcellus, this study finds the overall mean emission rate to be 19–28 Mg CH4 h−1 (gas-normalized loss rate of 0.30 %–0.45 %), with relative consistency in the emission rate over time. These totals align with aircraft top-down estimates from the same time periods. In both basins, the tower network was able to constrain monthly flux estimates within ±20 % uncertainty in the Delaware and ±24 % uncertainty in the Marcellus. The results from this study demonstrate the ability to monitor emissions continuously and detect changes in the emissions field, even in a basin with relatively low emissions and complex background conditions.
54 ENVIRONMENTAL SCIENCES↗