scholarly journals Uncertainty of the Electricity Emission Factor Incorporating the Uncertainty of the Fuel Emission Factors

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5697
Author(s):  
Kun Mo LEE ◽  
Min Hyeok LEE
Keyword(s):  
Power Generation ◽  
Emission Factor ◽  
Bootstrap Method ◽  
Ghg Emissions ◽  
Original Data ◽  
Emission Factors ◽  
Ghg Emission ◽  
Data Set ◽  
The Mean ◽  
The Bootstrap Method

Greenhouse gas (GHG) emission from electricity generation has been recognized as one of the most significant contributors to global warming. The GHG emission factor of electricity (hereafter, electricity emission factor) can be expressed as a function of three different (average, minimum, and maximum) fuel emission factors, monthly fuel consumption, and monthly net power generation. Choosing the average fuel emission factor over the minimum and maximum fuel emission factors is the cause of uncertainty in the electricity emission factor, and thus GHG emissions of the power generation. The uncertainties of GHG emissions are higher than those of the electricity emission factor, indicating that the uncertainty of GHG emission propagates in the GHG emission computation model. The bootstrapped data were generated by applying the bootstrap method to the original data set which consists of a 60-monthly average, and minimum and maximum electricity emission factors. The bootstrapped data were used for computing the mean, confidence interval (CI), and percentage uncertainty (U) of the electricity emission factor. The CI, mean, and U were [0.431, 0.443] kg CO2-eq/kWh, 0.437 kg CO2-eq/kwh, and 2.56%, respectively.

scholarly journals Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4965
Author(s):  
Kun Mo Lee ◽  
Min Hyeok Lee ◽  
Jong Seok Lee ◽  
Joo Young Lee
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Emission Factor ◽  
Bootstrap Method ◽  
Ghg Emissions ◽  
Parametric Bootstrap ◽  
Random Variable ◽  
Ghg Emission ◽  
Nonparametric Bootstrap ◽  
Mcs Method

Uncertainty of greenhouse gas (GHG) emissions was analyzed using the parametric Monte Carlo simulation (MCS) method and the non-parametric bootstrap method. There was a certain number of observations required of a dataset before GHG emissions reached an asymptotic value. Treating a coefficient (i.e., GHG emission factor) as a random variable did not alter the mean; however, it yielded higher uncertainty of GHG emissions compared to the case when treating a coefficient constant. The non-parametric bootstrap method reduces the variance of GHG. A mathematical model for estimating GHG emissions should treat the GHG emission factor as a random variable. When the estimated probability density function (PDF) of the original dataset is incorrect, the nonparametric bootstrap method, not the parametric MCS method, should be the method of choice for the uncertainty analysis of GHG emissions.

The Bootstrap-Method: Discussion and Proposal for Improvement / Das bootstrap-Verfahren: Diskussion und Verbesserungsvorschlag

1998 ◽  
Vol 217 (1) ◽  
Author(s):  
Hans Schneeberger
Keyword(s):  
Bootstrap Method ◽  
Law School ◽  
Mean Deviation ◽  
Alternative Method ◽  
Doubling Method ◽  
The Mean ◽  
The Bootstrap Method

SummaryWith Efron’s law-school example the bootstrap method is compared with an alternative method, called doubling. It is shown, that the mean deviation of the estimator is always smaller for the doubling method.

scholarly journals Boreal forest fire CO and CH<sub>4</sub> emission factors derived from tower observations in Alaska during the extreme fire season of 2015

2021 ◽  
Vol 21 (11) ◽  
pp. 8557-8574
Author(s):  
Elizabeth B. Wiggins ◽  
Arlyn Andrews ◽  
Colm Sweeney ◽  
John B. Miller ◽  
Charles E. Miller ◽  
...  
Keyword(s):  
Co2 Emission ◽  
Emission Factor ◽  
Emission Factors ◽  
Trace Gas ◽  
Fire Season ◽  
Ch4 Emission ◽  
Emission Ratio ◽  
Dry Biomass ◽  
The Mean ◽  
Co Emission

Abstract. Recent increases in boreal forest burned area, which have been linked with climate warming, highlight the need to better understand the composition of wildfire emissions and their atmospheric impacts. Here we quantified emission factors for CO and CH4 from a massive regional fire complex in interior Alaska during the summer of 2015 using continuous high-resolution trace gas observations from the Carbon in Arctic Reservoirs Vulnerability Experiment (CRV) tower in Fox, Alaska. Averaged over the 2015 fire season, the mean CO / CO2 emission ratio was 0.142 ± 0.051, and the mean CO emission factor was 127 ± 40 g kg−1 dry biomass burned. The CO / CO2 emission ratio was about 39 % higher than the mean of previous estimates derived from aircraft sampling of wildfires from boreal North America. The mean CH4 / CO2 emission ratio was 0.010 ± 0.004, and the CH4 emission factor was 5.3 ± 1.8 g kg−1 dry biomass burned, which are consistent with the mean of previous reports. CO and CH4 emission ratios varied in synchrony, with higher CH4 emission factors observed during periods with lower modified combustion efficiency (MCE). By coupling a fire emissions inventory with an atmospheric model, we identified at least 34 individual fires that contributed to trace gas variations measured at the CRV tower, representing a sample size that is nearly the same as the total number of boreal fires measured in all previous field campaigns. The model also indicated that typical mean transit times between trace gas emission within a fire perimeter and tower measurement were 1–3 d, indicating that the time series sampled combustion across day and night burning phases. The high CO emission ratio estimates reported here provide evidence for a prominent role of smoldering combustion and illustrate the importance of continuously sampling fires across time-varying environmental conditions that are representative of a fire season.

scholarly journals Comparative Evaluation of Biomass Power Generation Systems in China Using Hybrid Life Cycle Inventory Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Huacai Liu ◽  
Xiuli Yin ◽  
Chuangzhi Wu
Keyword(s):  
Power Generation ◽  
Life Cycle ◽  
Life Cycle Inventory ◽  
Comparative Evaluation ◽  
Energy Savings ◽  
Ghg Emissions ◽  
Ghg Emission ◽  
Inventory Analysis ◽  
Emission Reductions ◽  
Power Generation Systems

There has been a rapid growth in using agricultural residues as an energy source to generate electricity in China. Biomass power generation (BPG) systems may vary significantly in technology, scale, and feedstock and consequently in their performances. A comparative evaluation of five typical BPG systems has been conducted in this study through a hybrid life cycle inventory (LCI) approach. Results show that requirements of fossil energy savings, and greenhouse gas (GHG) emission reductions, as well as emission reductions of SO2and NOx, can be best met by the BPG systems. The cofiring systems were found to behave better than the biomass-only fired system and the biomass gasification systems in terms of energy savings and GHG emission reductions. Comparing with results of conventional process-base LCI, an important aspect to note is the significant contribution of infrastructure, equipment, and maintenance of the plant, which require the input of various types of materials, fuels, services, and the consequent GHG emissions. The results demonstrate characteristics and differences of BPG systems and help identify critical opportunities for biomass power development in China.

Analysis on Influence Factors of Greenhouse Gas Emissions of Magnesium Production Process

2016 ◽  
Vol 847 ◽  
pp. 321-327
Author(s):  
Yan Cui Cao ◽  
Feng Gao ◽  
Zhi Hong Wang ◽  
Xian Zheng Gong ◽  
Xiao Qing Li
Keyword(s):  
Energy Consumption ◽  
Production Process ◽  
Greenhouse Gas ◽  
Influence Factors ◽  
Ghg Emissions ◽  
Emission Factors ◽  
Energy Structure ◽  
Ghg Emission ◽  
Index Method ◽  
Magnesium Production

Magnesium is a promising lightweight and green metallic engineering material, but the environmental impact of primary magnesium production stage, especially greenhouse gas (GHG) emissions cannot be ignored. In this study, the life cycle energy consumption and GHG emissions caused by the production of primary magnesium in the years of 2003-2013 in China were calculated; the factor decomposition was conducted to analyze the GHG emissions of magnesium production process by using logarithmic mean Divisia index method (LMDI), including energy GHG emission factors, energy structure, energy consumption per ton of primary magnesium, production, emissions per unit of dolomite and ferrosilicon, and dolomite and ferrosilicon consumptions per ton of primary magnesium. The results showed that GHG emissions of primary magnesium production increased 260.29*104 t CO2eq in total from 2003 to 2013. The variety magnesium production contributed the biggest part of GHG emissions, accounting for 418.17%. The energy structure took second place on the contribution of GHG emissions, accounting for-161.49%. The nest part was energy consumption per ton of primary magnesium, accounting for-138.97%. While, the contribution of energy GHG emission factors, emissions per unit of dolomite and ferrosilicon, and dolomite and ferrosilicon consumptions per ton of primary magnesium was relatively small, which were 0.88%, 0.00% -2.72% -4.73% and-11.13%, respectively. Thus, it is the key methods to reduce GHG emissions by optimizing the energy structure and decreasing the energy consumption.

scholarly journals Atmospheric number size distributions of soot particles and estimation of emission factors

2006 ◽  
Vol 6 (4) ◽  
pp. 1021-1031 ◽  
Author(s):  
D. Rose ◽  
B. Wehner ◽  
M. Ketzel ◽  
C. Engler ◽  
J. Voigtländer ◽  
...  
Keyword(s):  
Size Distribution ◽  
Atmospheric Aerosols ◽  
Rural Areas ◽  
Urban Areas ◽  
Emission Factor ◽  
Emission Factors ◽  
Rural Site ◽  
Passenger Cars ◽  
Data Set ◽  
Soot Particles

Abstract. Number fractions of externally mixed particles of four different sizes (30, 50, 80, and 150 nm in diameter) were measured using a Volatility Tandem DMA. The system was operated in a street canyon (Eisenbahnstrasse, EI) and at an urban background site (Institute for Tropospheric Research, IfT), both in the city of Leipzig, Germany as well as at a rural site (Melpitz (ME), a village near Leipzig). Intensive campaigns of 3–5 weeks each took place in summer 2003 as well as in winter 2003/04. The data set thus obtained provides mean number fractions of externally mixed soot particles of atmospheric aerosols in differently polluted areas and different seasons (e.g. at 80 nm on working days, 60% (EI), 22% (IfT), and 6% (ME) in summer and 26% (IfT), and 13% (ME) in winter). Furthermore, a new method is used to calculate the size distribution of these externally mixed soot particles from parallel number size distribution measurements. A decrease of the externally mixed soot fraction with decreasing urbanity and a diurnal variation linked to the daily traffic changes demonstrate, that the traffic emissions have a significant impact on the soot fraction in urban areas. This influence becomes less in rural areas, due to atmospheric mixing and transformation processes. For estimating the source strength of soot particles emitted by vehicles (veh), soot particle emission factors were calculated using the Operational Street Pollution Model (OSPM). The emission factor for an average vehicle was found to be (1.5±0.4)·1014 #(km·veh). The separation of the emission factor into passenger cars ((5.8±2)·1013} #(km·veh)) and trucks ((2.5±0.9)·1015 #(km·veh)) yielded in a 40-times higher emission factor for trucks compared to passenger cars.

scholarly journals Potentials of Gas Emission Reduction (GHG) by the Glass Sheet Industry through Energy Conservation

2021 ◽  
Vol 226 ◽  
pp. 00047
Author(s):  
Washington Purba ◽  
Erkata Yandri ◽  
Roy Hendroko Setyobudi ◽  
Hery Susanto ◽  
Satriyo Krido Wahono ◽  
...  
Keyword(s):  
Emission Reduction ◽  
Emission Factor ◽  
Ghg Emissions ◽  
Electric Energy ◽  
Electrical Energy ◽  
Flue Gases ◽  
Ghg Emission ◽  
Gas Emission ◽  
Intergovernmental Panel ◽  
Calcination Process

Sheet Glass Industry is one industry that uses 75 % natural gas energy and 25 % electricity. Using the Intergovernmental Panel on Climate Change, IPCC-2006 emission calculation method, the average greenhouses gas (GHG) emissions obtained from the calcination process obtained 112 211 t CO2 yr–1 per plant and an average emission factor (EFkl) of 0.18 CO2 t–1 yr–1 of pull. With the technology of converting heat into electrical energy, residual combustion as flue gases has the potential to be used to produce electrical energy. Referring to the analysis and calculation; one of factories has potential to generate 0.8 MW to 3 MW electric energy. It’s efficiency of 10 % to 40 % so that it can be calculated as a component of GHG emission reductions whose value is 4.6 t CO2 yr–1 to 18.7 t CO2 yr–1 per plant. With this reduction, each of the GHG emission and emission factors per plant dropped to 93 442 t CO2 yr–1 and 0.16 CO2 t-pull–1.

scholarly journals Atmospheric number size distributions of soot particles and estimation of emission factors

2005 ◽  
Vol 5 (5) ◽  
pp. 10125-10154 ◽  
Author(s):  
D. Rose ◽  
B. Wehner ◽  
M. Ketzel ◽  
C. Engler ◽  
J. Voigtländer ◽  
...  
Keyword(s):  
Size Distribution ◽  
Atmospheric Aerosols ◽  
Rural Areas ◽  
Urban Areas ◽  
Emission Factor ◽  
Emission Factors ◽  
Rural Site ◽  
Passenger Cars ◽  
Data Set ◽  
Soot Particles

Abstract. Number fractions of externally mixed particles of four different sizes (30, 50, 80, and 150 nm in diameter) were measured using a Volatility Tandem DMA. The system was operated in a street canyon (Eisenbahnstrasse, EI) and at an urban background site (Institute for Tropospheric Research, IfT), both in the city of Leipzig, Germany as well as at a rural site (Melpitz (ME), a village near Leipzig). Intensive campaigns of 3–5 weeks each took place in summer 2003 as well as in winter 2003/2004. The data set thus obtained provides mean number fractions of externally mixed soot particles of atmospheric aerosols in differently polluted areas and different seasons (e.g. at 80 nm on working days, 60% (EI), 22% (IfT), and 6% (ME) in summer and 26% (IfT), and 13% (ME) in winter). Furthermore, a new method is used to calculate the size distribution of these externally mixed soot particles from parallel number size distribution measurements. A decrease of the externally mixed soot fraction with decreasing urbanity and a diurnal variation linked to the daily traffic changes demonstrate, that the traffic emissions have a significant impact on the soot fraction in urban areas. This influence becomes less in rural areas, due to atmospheric mixing and transformation processes. For estimating the source strength of soot particles emitted by vehicles (veh), soot particle emission factors were calculated using the Operational Street Pollution Model (OSPM). The emission factor for an average vehicle was found to be (1.5±0.4)·1014 #/(km·veh). The separation of the emission factor into passenger cars ((5.8±2)·1013 #/(km·veh)) and trucks ((2.5±0.9)·1015 #/(km·veh)) yielded in a 40-times higher emission factor for trucks compared to passenger cars.

Emission Factors for Dioxin for Coal Burning, Secondary Smelting and Alloying of Aluminum, and Cement Kiln Processes

2015 ◽  
Vol 1102 ◽  
pp. 27-32
Author(s):  
Yu Yen Cheng ◽  
Mei Fang Lu ◽  
Jim Jui Min Lin
Keyword(s):  
Power Generation ◽  
Emission Factor ◽  
Raw Materials ◽  
Emission Factors ◽  
Raw Material ◽  
Cement Kiln ◽  
Generation Process ◽  
Secondary Aluminum ◽  
Dioxin Emission ◽  
Conducting Research

While conducting research for dioxin emission factor for stationary source emission, it is found that some factors come from overestimation. The cause of discrepancy for estimating process comes often from lacking understanding of the process. The result of this study for secondary aluminum refining emission factors indicates that if raw materials are scraps, the factors averaged at 541 ng I-TEQ/Ton-raw materials. If waste aluminum is used as the raw material, factors averaged at 1338 ng I-TEQ/Ton-raw material. The factors based on site sampling for coal-fired power generation process is 24.84-549.62 ng I-TEQ/Ton-fuel. It varies according to coal sources. Emission factor for cement producing process is 95.4-102.66 ng I-TEQ/Ton-raw material. Due to fewer differences in operating traits, raw materials and fuel application for cement producing process, emission factors have smaller differences. The reliability for emission variables is relatively higher.

How Accurate is an Effort Model?

2008 ◽  
pp. 62-77
Author(s):  
Emilia Mendes
Keyword(s):  
Relative Error ◽  
Cross Validation ◽  
Model Building ◽  
Original Data ◽  
Estimation Accuracy ◽  
Effort Estimation ◽  
Data Set ◽  
Building Process ◽  
The Mean ◽  
Accuracy Measures

Building effort models or using techniques to obtain a measure of estimated effort does not mean that the effort estimates obtained will be accurate. As such, it is also important and necessary to assess the estimation accuracy of the effort models or techniques under scrutiny. For this, we need to employ a process called cross-validation. Cross-validation means that part of the original data set is used to build an effort model, or is used by an effort estimation technique, leaving the remainder of the data set (data not used in the model-building process) to be used to validate the model or technique. In addition, in parallel with conducting cross-validation, prediction accuracy measures are also obtained. Examples of de facto accuracy measures are the mean magnitude of relative error (MMRE), the median magnitude of relative error (MdMRE), and prediction at 25% (Pred[25]).

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