Limitations of the radon tracer method (RTM) to estimate regional greenhouse gas (GHG) emissions – a case study for methane in Heidelberg

Abstract. Correlations of nighttime atmospheric methane (CH4) and 222radon (222Rn) observations in Heidelberg, Germany, were evaluated with the radon tracer method (RTM) to estimate the trend of annual nocturnal CH4 emissions from 1996–2020 in the footprint of the station. After an initial 30 % decrease in emissions from 1996 to 2004, there was no further systematic trend but small inter-annual variations were observed thereafter. This is in accordance with the trend of total emissions until 2010 reported by the EDGARv6.0 inventory for the surroundings of Heidelberg and provides a fully independent top-down verification of the bottom-up inventory changes. We show that the reliability of total nocturnal CH4 emission estimates with the RTM critically depends on the accuracy and representativeness of the 222Rn exhalation rates estimated from soils in the footprint of the site. Simply using 222Rn fluxes as estimated by Karstens et al. (2015) could lead to biases in the estimated greenhouse gas (GHG) fluxes as large as a factor of 2. RTM-based GHG flux estimates also depend on the parameters chosen for the nighttime correlations of CH4 and 222Rn, such as the nighttime period for regressions and the R2 cut-off value for the goodness of the fit. Quantitative comparison of total RTM-based top-down flux estimates with bottom-up emission inventories requires representative high-resolution footprint modelling, particularly in polluted areas where CH4 emissions show large heterogeneity. Even then, RTM-based estimates are likely biased low if point sources play a significant role in the station footprint as their emissions may not be fully captured by the RTM method, for example, if stack emissions are injected above the top of the nocturnal inversion layer or if point-source emissions from the surface are not well mixed into the footprint of the measurement site. Long-term representative 222Rn flux observations in the footprint of a station are indispensable in order to apply the RTM method for reliable quantitative flux estimations of GHG emissions from atmospheric observations.

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Limitations of the Radon Tracer Method (RTM) to estimate regional Greenhouse Gases (GHG) emissions – a case study for methane in Heidelberg

10.5194/acp-2021-661 ◽

2021 ◽

Author(s):

Ingeborg Levin ◽

Ute Karstens ◽

Samuel Hammer ◽

Julian DellaColetta ◽

Fabian Maier ◽

...

Keyword(s):

Greenhouse Gases ◽

Catchment Area ◽

Ghg Emissions ◽

Point Sources ◽

Atmospheric Methane ◽

Tracer Method ◽

Night Time ◽

Ch4 Emissions ◽

Annual Variations ◽

Atmospheric Observations

Abstract. Correlations of night-time atmospheric methane (CH4) and 222Radon (222Rn) observations in Heidelberg, Germany, were evaluated with the Radon Tracer Method (RTM) to estimate the trend of annual CH4 emissions from 1996–2020 in the catchment area of the station. After an initial 30 % decrease of emissions from 1996 to 2004, no further systematic trend but small inter-annual variations were observed thereafter. This is in accordance with the trend of emissions until 2010 reported by the EDGARv6.0 inventory for the surroundings of Heidelberg. We show that the reliability of total CH4 emission estimates with the RTM critically depends on the accuracy and representativeness of the 222Rn exhalation rate from soils in the catchment area of the site. Simply using 222Rn fluxes as estimated by Karstens et al. (2015) could lead to biases in the estimated greenhouse gases (GHG) fluxes as large as a factor of two. RTM-based GHG flux estimates also depend on the parameters chosen for the night-time correlations of CH4 and 222Rn, such as the night-time period for regressions as well as the R2 cut-off value for the goodness of the fit. Quantitative comparison of total RTM-based top-down with bottom-up emission inventories requires representative high-resolution footprint modelling, particularly in polluted areas where CH4 emissions show large heterogeneity. Even then, RTM-based estimates are likely biased low if point sources play a significant role in the station/observation footprint as their emissions are not captured by the RTM method. Long-term representative 222Rn flux observations in the catchment area of a station are indispensable in order to apply the RTM method for reliable quantitative flux estimations of GHG emissions from atmospheric observations.

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Variability and quasi-decadal changes in the methane budget over the period 2000–2012

10.5194/acp-2017-296 ◽

2017 ◽

Cited By ~ 2

Author(s):

Marielle Saunois ◽

Philippe Bousquet ◽

Benjamin Poulter ◽

Anna Peregon ◽

Philippe Ciais ◽

...

Keyword(s):

Biomass Burning ◽

Methane Emissions ◽

Data Driven ◽

Atmospheric Methane ◽

Top Down ◽

Bottom Up ◽

Ch4 Emissions ◽

Ensemble Mean ◽

The Mean ◽

The Tropics

Abstract. Following the recent Global Carbon project (GCP) synthesis of the decadal methane (CH4) budget over 2000–2012 (Saunois et al., 2016), we analyse here the same dataset with a focus on quasi-decadal and inter-annual variability in CH4 emissions. The GCP dataset integrates results from top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling frameworks) and bottom-up models, inventories, and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). The annual global methane emissions from top-down studies, which by construction match the observed methane growth rate within their uncertainties, all show an increase in total methane emissions over the period 2000–2012, but this increase is not linear over the 13 years. Despite differences between individual studies, the mean emission anomaly of the top-down ensemble shows no significant trend in total methane emissions over the period 2000–2006, during the plateau of atmospheric methane mole fractions, and also over the period 2008–2012, during the renewed atmospheric methane increase. However, the top-down ensemble mean produces an emission shift between 2006 and 2008, leading to 22 [16–32] Tg CH4 yr−1 higher methane emissions over the period 2008–2012 compared to 2002–2006. This emission increase mostly originated from the tropics with a smaller contribution from mid-latitudes and no significant change from boreal regions. The regional contributions remain uncertain in top-down studies. Tropical South America and South and East Asia seems to contribute the most to the emission increase in the tropics. However, these two regions have only limited atmospheric measurements and remain therefore poorly constrained. The sectorial partitioning of this emission increase between the periods 2002–2006 and 2008–2012 differs from one atmospheric inversion study to another. However, all top-down studies suggest smaller changes in fossil fuel emissions (from oil, gas, and coal industries) compared to the mean of the bottom-up inventories included in this study. This difference is partly driven by a smaller emission change in China from the top-down studies compared to the estimate in the EDGARv4.2 inventory, which should be revised to smaller values in a near future. Though the sectorial partitioning of six individual top-down studies out of eight are not consistent with the observed change in atmospheric 13CH4, the partitioning derived from the ensemble mean is consistent with this isotopic constraint. At the global scale, the top-down ensemble mean suggests that, the dominant contribution to the resumed atmospheric CH4 growth after 2006 comes from microbial sources (more from agriculture and waste sectors than from natural wetlands), with an uncertain but smaller contribution from fossil CH4 emissions. Besides, a decrease in biomass burning emissions (in agreement with the biomass burning emission databases) makes the balance of sources consistent with atmospheric 13CH4 observations. The methane loss (in particular through OH oxidation) has not been investigated in detail in this study, although it may play a significant role in the recent atmospheric methane changes.

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A Study on Selecting Greenhouse Gas Reduction Options: A Simulation Analysis for Vietnam

Sustainability ◽

10.3390/su132413530 ◽

2021 ◽

Vol 13 (24) ◽

pp. 13530

Author(s):

Anh Quynh Tang ◽

Takeshi Mizunoya

Keyword(s):

Economic Development ◽

Greenhouse Gas ◽

Emission Reduction ◽

Carbon Tax ◽

Ghg Emissions ◽

Value Added ◽

Top Down ◽

Ghg Mitigation ◽

Bottom Up ◽

Subsidy Rate

When it comes to greenhouse gas (GHG) mitigation, both bottom-up and top-down policies have limitations. Bottom-up policies are region-specific and cannot be applied at the national level. Top-down policies may not balance the considerations of economic growth and the environment. Therefore, a combined approach is necessary. This Vietnamese case study investigates optimal GHG mitigation options for both economic development and emission reduction by simulating four scenarios characterized by the different carbon tax and subsidy rates. Interventions, like replacing old buses with low-carbon buses and conventional electricity generation with solar power, are considered in a dynamic input–output framework. The objective function is Green GDP—industries’ total value added reflecting GHG emissions’ social cost. The simulation model comprises four cases: business as usual, low subsidy rate (up to 10%), medium subsidy rate (up to 20%), and high subsidy rate (up to 30%), which are analyzed on parameters, including economic development, GHG emissions, and development of innovative sectors, like transportation and electricity. In three cases with different subsidy rates, the optimal carbon tax is simulated at the rate of USD 1/tCO2 equivalent, the lowest rate among the world’s current carbon prices. In addition, the medium subsidy (up to 20%) option yields the most competent scheme, with the highest GHG emission reduction and economic development effectiveness.

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Quantifying greenhouse-gas emissions from atmospheric measurements: a critical reality check for climate legislation

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2011.0006 ◽

2011 ◽

Vol 369 (1943) ◽

pp. 1925-1942 ◽

Cited By ~ 36

Author(s):

Ray F. Weiss ◽

Ronald G. Prinn

Keyword(s):

Greenhouse Gas ◽

Atmospheric Transport ◽

Ghg Emissions ◽

Inverse Modelling ◽

Carbon Equivalent ◽

Emissions Reduction ◽

Atmospheric Measurements ◽

Top Down ◽

Bottom Up ◽

Regional Emissions

Emissions reduction legislation relies upon ‘bottom-up’ accounting of industrial and biogenic greenhouse-gas (GHG) emissions at their sources. Yet, even for relatively well-constrained industrial GHGs, global emissions based on ‘top-down’ methods that use atmospheric measurements often agree poorly with the reported bottom-up emissions. For emissions reduction legislation to be effective, it is essential that these discrepancies be resolved. Because emissions are regulated nationally or regionally, not globally, top-down estimates must also be determined at these scales. High-frequency atmospheric GHG measurements at well-chosen station locations record ‘pollution events’ above the background values that result from regional emissions. By combining such measurements with inverse methods and atmospheric transport and chemistry models, it is possible to map and quantify regional emissions. Even with the sparse current network of measurement stations and current inverse-modelling techniques, it is possible to rival the accuracies of regional ‘bottom-up’ emission estimates for some GHGs. But meeting the verification goals of emissions reduction legislation will require major increases in the density and types of atmospheric observations, as well as expanded inverse-modelling capabilities. The cost of this effort would be minor when compared with current investments in carbon-equivalent trading, and would reduce the volatility of that market and increase investment in emissions reduction.

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Evaluation of anthropogenic CH<sub>4</sub> emissions over China using bottom-up inventories

10.5194/essd-2020-210 ◽

2020 ◽

Author(s):

Xiaohui Lin ◽

Wen Zhang ◽

Monica Crippa ◽

Shushi Peng ◽

Pengfei Han ◽

...

Keyword(s):

Waste Treatment ◽

Comprehensive Evaluation ◽

Energy Sector ◽

Atmospheric Methane ◽

Activity Data ◽

Bottom Up ◽

Average Growth ◽

Ch4 Emissions ◽

Source Contributions ◽

Change Mitigation

Abstract. Atmospheric methane (CH4) is a potent greenhouse gas that is strongly influenced by several human activities. China, as one of the major agricultural and energy production countries, e.g., rice cultivation, ruminant feeding and coal production, contributes considerably to the global anthropogenic CH4 emissions. Understanding the characteristics of China's CH4 emissions is necessary for interpreting source contributions and for further climate change mitigation. However, the scarcity of data from some sources or years and spatially explicit information pose great challenges to completing an analysis of CH4 emissions. This study provides a comprehensive evaluation of China's anthropogenic CH4 emissions by synthesizing most of the currently available data (12 inventories). The results show that anthropogenic CH4 emissions differ widely among inventories, with values ranging from 41.9–57.5 Tg CH4 yr−1 in 2010. The discrepancy primarily resulted from the energy sector (27.3–60.0 % of total emissions), followed by the agricultural (26.9–50.8 %), and waste treatment (8.1–21.2 %) sectors. Temporally, emissions among inventories stabilized in the 1990s, but increased significantly thereafter, with annual average growth rates (AAGRs) of 1.8–3.9 % during 2000–2010, but slower AAGRs of 0.5–2.2 % during 2011–2015. Spatially, the growth of CH4 emissions could be attributed mostly to an increase in emissions from the energy sector (mainly from coal mining) in the northern and central inland regions, followed by waste treatment in the southern and eastern regions. The availability of detailed activity data for sectors or subsectors and the use of region-specific emission factors play important roles in understanding source contributions, and reducing the uncertainty of bottom-up inventories.

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Exploring Gaps between Bottom-Up and Top-Down Emission Estimates Based on Uncertainties in Multiple Emission Inventories: A Case Study on CH4 Emissions in China

Sustainability ◽

10.3390/su11072054 ◽

2019 ◽

Vol 11 (7) ◽

pp. 2054 ◽

Cited By ~ 6

Author(s):

Penwadee Cheewaphongphan ◽

Satoru Chatani ◽

Nobuko Saigusa

Keyword(s):

Coal Mining ◽

Rice Cultivation ◽

Emission Inventories ◽

Ch4 Emission ◽

Top Down ◽

Activity Data ◽

Bottom Up ◽

Ch4 Emissions ◽

Model Studies ◽

Livestock Sector

Bottom-up CH4 emission inventories, which have been developed from statistical analyses of activity data and country specific emission factors (EFs), have high uncertainty in terms of the estimations, according to results from top-down inverse model studies. This study aimed to determine the causes of overestimation in CH4 bottom-up emission inventories across China by applying parameter variability uncertainty analysis to three sets of CH4 emission inventories titled PENG, GAINS, and EDGAR. The top three major sources of CH4 emissions in China during the years 1990–2010, namely, coal mining, livestock, and rice cultivation, were selected for the investigation. The results of this study confirm the concerns raised by inverse modeling results in which we found significantly higher bottom-up emissions for the rice cultivation and coal mining sectors. The largest uncertainties were detected in the rice cultivation estimates and were caused by variations in the proportions of rice cultivation ecosystems and EFs; specifically, higher rates for both parameters were used in EDGAR. The coal mining sector was associated with the second highest level of uncertainty, and this was caused by variations in mining types and EFs, for which rather consistent parameters were used in EDGAR and GAINS, but values were slightly higher than those used in PENG. Insignificant differences were detected among the three sets of inventories for the livestock sector.

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Comparison of top-down and bottom-up estimates of sectoral and regional greenhouse gas emission reduction potentials

Energy Policy ◽

10.1016/j.enpol.2009.07.024 ◽

2009 ◽

Vol 37 (12) ◽

pp. 5125-5139 ◽

Cited By ~ 92

Author(s):

Detlef P. van Vuuren ◽

Monique Hoogwijk ◽

Terry Barker ◽

Keywan Riahi ◽

Stefan Boeters ◽

...

Keyword(s):

Greenhouse Gas ◽

Emission Reduction ◽

Greenhouse Gas Emission ◽

Top Down ◽

Gas Emission ◽

Bottom Up ◽

Reduction Potentials ◽

Greenhouse Gas Emission Reduction

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Informing urban climate planning with high resolution data: the Hestia fossil fuel CO2 emissions for Baltimore, Maryland

10.21203/rs.3.rs-16517/v3 ◽

2020 ◽

Author(s):

Geoffrey Scott Roest ◽

Kevin R Gurney ◽

Scot M Miller ◽

Jianming Liang

Keyword(s):

Greenhouse Gas ◽

Fossil Fuel ◽

Ghg Emissions ◽

Point Sources ◽

The Self ◽

Greenhouse Gas Mitigation ◽

Electricity Production ◽

Ghg Mitigation ◽

High Resolution Data ◽

Atmospheric Monitoring

Abstract Background: Cities contribute more than 70% of global anthropogenic carbon dioxide (CO2) emissions and are leading the effort to reduce greenhouse gas (GHG) emissions through sustainable planning and development. However, urban greenhouse gas mitigation often relies on self-reported emissions estimates that may be incomplete and unverifiable via atmospheric monitoring of GHGs. We present the Hestia Scope 1 fossil fuel CO2 (FFCO2) emissions for the city of Baltimore, Maryland – a gridded annual and hourly emissions data product for 2010 through 2015 (Hestia-Baltimore v1.6). We also compare the Hestia-Baltimore emissions to overlapping Scope 1 FFCO2 emissions in Baltimore’s self-reported inventory for 2014. Results: The Hestia-Baltimore emissions in 2014 totaled 1487.3 kt C (95% confidence interval of 1,158.9 – 1,944.9 kt C), with the largest emissions coming from onroad (34.2% of total city emissions), commercial (19.9%), residential (19.0%), and industrial (11.8%) sectors. Scope 1 electricity production and marine shipping were each generally less than 10% of the city’s total emissions. Baltimore’s self-reported Scope 1 FFCO2 emissions included onroad, natural gas consumption in buildings, and some electricity generating facilities within city limits. The self-reported Scope 1 FFCO2 total of 1,182.6 kt C was similar to the sum of matching emission sectors and fuels in Hestia-Baltimore v1.6. However, 20.5% of Hestia-Baltimore’s emissions were in sectors and fuels that were not included in the self-reported inventory. Petroleum use in buildings were omitted and all Scope 1 emissions from industrial point sources, marine shipping, nonroad vehicles, rail, and aircraft were categorically excluded.Conclusions: The omission of petroleum combustion in buildings and categorical exclusions of several sectors resulted in an underestimate of total Scope 1 FFCO2 emissions in Baltimore’s self-reported inventory. Accurate Scope 1 FFCO2 emissions, along with Scope 2 and 3 emissions, are needed to inform effective urban policymaking for system-wide GHG mitigation. We emphasize the need for comprehensive Scope 1 emissions estimates for emissions verification and measuring progress towards Scope 1 GHG mitigation goals using atmospheric monitoring.

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A comparative study of anthropogenic CH<sub>4</sub> emissions over China based on the ensembles of bottom-up inventories

Earth System Science Data ◽

10.5194/essd-13-1073-2021 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1073-1088

Author(s):

Xiaohui Lin ◽

Wen Zhang ◽

Monica Crippa ◽

Shushi Peng ◽

Pengfei Han ◽

...

Keyword(s):

Waste Treatment ◽

Energy Sector ◽

Coal Production ◽

Atmospheric Methane ◽

Activity Data ◽

Bottom Up ◽

Average Growth ◽

Ch4 Emissions ◽

Source Contributions ◽

Comprehensive Comparison

Abstract. Atmospheric methane (CH4) is a potent greenhouse gas that is strongly influenced by several human activities. China, as one of the major agricultural and energy production countries, contributes considerably to the global anthropogenic CH4 emissions by rice cultivation, ruminant feeding, and coal production. Understanding the characteristics of China's CH4 emissions is necessary for interpreting source contributions and for further climate change mitigation. However, the scarcity of data from some sources or years and spatially explicit information pose great challenges to completing an analysis of CH4 emissions. This study provides a comprehensive comparison of China's anthropogenic CH4 emissions by synthesizing the most current and publicly available datasets (13 inventories). The results show that anthropogenic CH4 emissions differ widely among inventories, with values ranging from 44.4–57.5 Tg CH4 yr−1 in 2010. The discrepancy primarily resulted from the energy sector (27.3 %–60.0 % of total emissions), followed by the agricultural (26.9 %–50.8 %) and waste treatment (8.1 %–21.2 %) sectors. Temporally, emissions among inventories stabilized in the 1990s but increased significantly thereafter, with annual average growth rates (AAGRs) of 2.6 %–4.0 % during 2000–2010 but slower AAGRs of 0.5 %–2.2 % during 2011–2015, and the emissions became relatively stable, with AAGRs of 0.3 %–0.8 %, during 2015–2019 because of the stable emissions from the energy sector (mainly coal production). Spatially, there are large differences in emissions hotspot identification among inventories, and incomplete information on emission patterns may mislead or bias mitigation efforts for CH4 emission reductions. The availability of detailed activity data for sectors or subsectors and the use of region-specific emission factors play important roles in understanding source contributions and reducing the uncertainty in bottom-up inventories. Data used in this article are available at https://doi.org/10.6084/m9.figshare.12720989 (Lin et al., 2021).

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Informing urban climate planning with high resolution data: the Hestia fossil fuel CO2 emissions for Baltimore, Maryland

10.21203/rs.3.rs-16517/v1 ◽

2020 ◽

Author(s):

Geoffrey Scott Roest ◽

Kevin R Gurney ◽

Scot M Miller ◽

Jianming Liang

Keyword(s):

Greenhouse Gas ◽

Co2 Emissions ◽

Fossil Fuel ◽

Urban Climate ◽

Ghg Emissions ◽

Point Sources ◽

Greenhouse Gas Mitigation ◽

Electricity Production ◽

High Resolution Data ◽

Atmospheric Monitoring

Abstract Background Cities contribute more than 70% of global anthropogenic carbon dioxide (CO2) emissions and are leading the effort to reduce GHG emissions through sustainable planning and development. However, urban greenhouse gas mitigation often relies on self-reported emissions estimates that may be incomplete and unverifiable via atmospheric monitoring. We present the Hestia Scope 1 fossil fuel CO2 emissions for the city of Baltimore, Maryland – a gridded annual and hourly emissions data product for 2010 through 2015.Results The emissions in the base year of 2011 totaled 1431.5 kt C, with the largest emissions coming from onroad (35.0% of total city emissions), commercial (18.3%), residential (16.7%), and industrial (12.6%) sectors. Scope 1 electricity production and marine shipping were each generally less than 10% of the city’s total emissions. Baltimore’s self-reported Scope 1 emissions of 1,182.6 kt C were 22.8% lower than Hestia-Baltimore emission in 2014, largely due to the omission of petroleum consumption in buildings and several sectors that largely fall outside the city’s regulatory purview – industrial point sources, marine shipping, nonroad vehicles, rail, and aircraft.Conclusions We emphasize the need for comprehensive, Scope 1-only emissions estimates for emissions verification and measuring progress towards greenhouse gas mitigation goals using atmospheric monitoring, but we also acknowledge that city planners may desire a greater mix of scope 1, 2, and 3 emissions with an emphasis on activities under local policy control.

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