scholarly journals 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

2019 ◽  
Vol 11 (7) ◽  
pp. 2054 ◽  
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.

scholarly journals Constraining sector-specific CO<sub>2</sub> and CH<sub>4</sub> emissions in the US

2017 ◽  
Vol 17 (6) ◽  
pp. 3963-3985 ◽  
Author(s):  
Scot M. Miller ◽  
Anna M. Michalak
Keyword(s):  
Co2 Emissions ◽  
Spatial Scales ◽  
Demographic Data ◽  
Anthropogenic Source ◽  
Emission Inventories ◽  
Top Down ◽  
National Scale ◽  
Activity Data ◽  
Ch4 Emissions ◽  
The Us

Abstract. This review paper explores recent efforts to estimate state- and national-scale carbon dioxide (CO2) and methane (CH4) emissions from individual anthropogenic source sectors in the US. Nearly all state and national climate change regulations in the US target specific source sectors, and detailed monitoring of individual sectors presents a greater challenge than monitoring total emissions. We particularly focus on opportunities to synthesize disparate types of information on emissions, including emission inventory data and atmospheric greenhouse gas data.We find that inventory estimates of sector-specific CO2 emissions are sufficiently accurate for policy evaluation at the national scale but that uncertainties increase at state and local levels. CH4 emission inventories are highly uncertain for all source sectors at all spatial scales, in part because of the complex, spatially variable relationships between economic activity and CH4 emissions. In contrast to inventory estimates, top-down estimates use measurements of atmospheric mixing ratios to infer emissions at the surface; thus far, these efforts have had some success identifying urban CO2 emissions and have successfully identified sector-specific CH4 emissions in several opportunistic cases. We also describe a number of forward-looking opportunities that would aid efforts to estimate sector-specific emissions: fully combine existing top-down datasets, expand intensive aircraft measurement campaigns and measurements of secondary tracers, and improve the economic and demographic data (e.g., activity data) that drive emission inventories. These steps would better synthesize inventory and top-down data to support sector-specific emission reduction policies.

scholarly journals Conflicting estimates of natural geologic methane emissions

Elem Sci Anth ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
B. F. Thornton ◽  
G. Etiope ◽  
S. Schwietzke ◽  
A. V. Milkov ◽  
R. W. Klusman ◽  
...  
Keyword(s):  
Fossil Fuel ◽  
Ice Cores ◽  
Methane Emissions ◽  
Ch4 Emission ◽  
Top Down ◽  
Bottom Up ◽  
Ch4 Emissions ◽  
Emission Estimate

Global bottom-up and top-down estimates of natural, geologic methane (CH4) emissions (average approximately 45 Tg yr–1) have recently been questioned by near-zero (approximately 1.6 Tg yr–1) estimates based on measurements of 14CH4 trapped in ice cores, which imply that current fossil fuel industries’ CH4 emissions are underestimated by 25%–40%. As we show here, such a global near-zero geologic CH4 emission estimate is incompatible with multiple independent, bottom-up emission estimates from individual natural geologic seepage areas, each of which is of the order of 0.1–3 Tg yr–1. Further research is urgently needed to resolve the conundrum before rejecting either method or associated emission estimates in global CH4 accounting.

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

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.

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

2017 ◽  
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.

scholarly journals Comparison and evaluation of anthropogenic emissions of SO<sub>2</sub> and NO<sub><i>x</i></sub> over China

2018 ◽  
Vol 18 (5) ◽  
pp. 3433-3456 ◽  
Author(s):  
Meng Li ◽  
Zbigniew Klimont ◽  
Qiang Zhang ◽  
Randall V. Martin ◽  
Bo Zheng ◽  
...  
Keyword(s):  
Power Plants ◽  
Comprehensive Evaluation ◽  
National Level ◽  
Model Performance ◽  
Chemical Transport ◽  
Emission Factors ◽  
Emission Inventories ◽  
Ozone Monitoring Instrument ◽  
Top Down ◽  
Bottom Up

Abstract. Bottom-up emission inventories provide primary understanding of sources of air pollution and essential input of chemical transport models. Focusing on SO2 and NOx, we conducted a comprehensive evaluation of two widely used anthropogenic emission inventories over China, ECLIPSE and MIX, to explore the potential sources of uncertainties and find clues to improve emission inventories. We first compared the activity rates and emission factors used in two inventories and investigated the reasons of differences and the impacts on emission estimates. We found that SO2 emission estimates are consistent between two inventories (with 1 % differences), while NOx emissions in ECLIPSE's estimates are 16 % lower than those of MIX. The FGD (flue-gas desulfurization) device penetration rate and removal efficiency, LNB (low-NOx burner) application rate and abatement efficiency in power plants, emission factors of industrial boilers and various vehicle types, and vehicle fleet need further verification. Diesel consumptions are quite uncertain in current inventories. Discrepancies at the sectorial and provincial levels are much higher than those of the national total. We then examined the impacts of different inventories on model performance by using the nested GEOS-Chem model. We finally derived top-down emissions by using the retrieved columns from the Ozone Monitoring Instrument (OMI) compared with the bottom-up estimates. High correlations were observed for SO2 between model results and OMI columns. For NOx, negative biases in bottom-up gridded emission inventories (−21 % for MIX, −39 % for ECLIPSE) were found compared to the satellite-based emissions. The emission trends from 2005 to 2010 estimated by two inventories were both consistent with satellite observations. The inventories appear to be fit for evaluation of the policies at an aggregated or national level; more work is needed in specific areas in order to improve the accuracy and robustness of outcomes at finer spatial and also technological levels. To our knowledge, this is the first work in which source comparisons detailed to technology-level parameters are made along with the remote sensing retrievals and chemical transport modeling. Through the comparison between bottom-up emission inventories and evaluation with top-down information, we identified potential directions for further improvement in inventory development.

scholarly journals Comparison of Top-Down and Bottom-Up Road Transport Emissions through High-Resolution Air Quality Modeling in a City of Complex Orography

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1372
Author(s):  
Felipe Cifuentes ◽  
Carlos M. González ◽  
Erika M. Trejos ◽  
Luis D. López ◽  
Francisco J. Sandoval ◽  
...  
Keyword(s):  
Specific Activity ◽  
Road Transport ◽  
Urban Environments ◽  
Top Down ◽  
Activity Data ◽  
Bottom Up ◽  
Quality Modeling ◽  
Traffic Volumes ◽  
Vehicular Speed ◽  
Transport Emissions

Vehicular emissions are a predominant source of pollution in urban environments. However, inherent complexities of vehicular behavior are sources of uncertainties in emission inventories (EIs). We compare bottom-up and top-down approaches for estimating road transport EIs in Manizales, Colombia. The EIs were estimated using a COPERT model, and results from both approaches were also compared with the official top-down EI (estimated from IVE methodology). The transportation model PTV-VISUM was used for obtaining specific activity information (traffic volumes, vehicular speed) in bottom-up estimation. Results from COPERT showed lower emissions from the top-down approach than from the bottom-up approach, mainly for NMVOC (−28%), PM10 (−26%), and CO (−23%). Comparisons showed that COPERT estimated lower emissions than IVE, with higher differences than 40% for species such as PM10, NOX, and CH4. Furthermore, the WRF–Chem model was used to test the sensitivity of CO, O3, PM10, and PM2.5 predictions to the different EIs evaluated. All studied pollutants exhibited a strong sensitivity to the emission factors implemented in EIs. The COPERT/top-down was the EI that produced more significant errors. This work shows the importance of performing bottom-up EI to reduce the uncertainty regarding top-down activity data.

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

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).

scholarly journals Top-down estimates of European CH<sub>4</sub> and N<sub>2</sub>O emissions based on four different inverse models

2015 ◽  
Vol 15 (2) ◽  
pp. 715-736 ◽  
Author(s):  
P. Bergamaschi ◽  
M. Corazza ◽  
U. Karstens ◽  
M. Athanassiadou ◽  
R. L. Thompson ◽  
...  
Keyword(s):  
Eastern Europe ◽  
Inverse Modelling ◽  
N2o Emissions ◽  
Emission Inventories ◽  
Bottom Up ◽  
Uncertainty Range ◽  
Ch4 Emissions ◽  
Inverse Models ◽  
Ch4 And N2o ◽  
North Western

Abstract. European CH4 and N2O emissions are estimated for 2006 and 2007 using four inverse modelling systems, based on different global and regional Eulerian and Lagrangian transport models. This ensemble approach is designed to provide more realistic estimates of the overall uncertainties in the derived emissions, which is particularly important for verifying bottom-up emission inventories. We use continuous observations from 10 European stations (including 5 tall towers) for CH4 and 9 continuous stations for N2O, complemented by additional European and global discrete air sampling sites. The available observations mainly constrain CH4 and N2O emissions from north-western and eastern Europe. The inversions are strongly driven by the observations and the derived total emissions of larger countries show little dependence on the emission inventories used a priori. Three inverse models yield 26–56% higher total CH4 emissions from north-western and eastern Europe compared to bottom-up emissions reported to the UNFCCC, while one model is close to the UNFCCC values. In contrast, the inverse modelling estimates of European N2O emissions are in general close to the UNFCCC values, with the overall range from all models being much smaller than the UNFCCC uncertainty range for most countries. Our analysis suggests that the reported uncertainties for CH4 emissions might be underestimated, while those for N2O emissions are likely overestimated.

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