Monte Carlo analysis of uncertainties in the Netherlands greenhouse gas emission inventory for 1990–2004

Ramírez, Andrea; Keizer, Corry de; Sluijs, J.P. van der; Olivier, Jos; Brandes, L. J.

doi:10.1016/j.atmosenv.2008.07.059

Cited by 67 publications

(39 citation statements)

References 7 publications

(7 reference statements)

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“…Prova disso é que o MMC tem sido uma ferramenta essencial em diversas áreas da ciência, dentre elas: economia [6]; medicina [7]; medicina nuclear [8]; biologia [9]; ecologia [10,11]; física do clima [12]; climatologia [13,14]; engenharias [15,16]; modelagem de colisões veiculares [17][18][19][20].…”

Section: Método De Monte Carlounclassified

Análise probabilística de colisões veiculares pelo método de Monte Carlo

Carvalho¹,

Armigliato²,

Almeida³

et al. 2016

Rev. Bras. Crimin.

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ResumoEste trabalho apresenta um modelo para análise probabilística de perícias de colisão de veículos automotores, apresentando um modelo computacional flexível para avaliação da confiabilidade por simulação Monte Carlo. Os procedimentos desenvolvidos buscaram a representação estatística dos parâmetros ambientais e psicomotores, tais como coeficientes de atrito e ritmo de Reação, em uma simulação, com 10.000 iterações, da confiabilidade em um modelo bidimensional aplicado a um estudo de caso de colisão frontal envolvendo dois veículos de passeio, obtendo intervalos unilaterais e bilaterais para as variáveis estudadas. AbstractThis paper presents a model for probabilistic analysis skills collision of motor vehicles, featuring a flexible computational model for the evaluation of reliability by Monte Carlo simulation. The developed procedures sought statistical representation of environmental and psychomotor parameters such as friction coefficient and reaction rate in a simulation with 10,000 iterations, reliability in a twodimensional model applied to a frontal collision case study involving two vehicles, obtaining unilateral and bilateral ranges for the variables studied.

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Section: Método De Monte Carlounclassified

Análise probabilística de colisões veiculares pelo método de Monte Carlo

Carvalho¹,

Armigliato²,

Almeida³

et al. 2016

Rev. Bras. Crimin.

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“…Monte Carlo method has been widely used in the uncertainty analysis, such as the construction of a greenhouse gas emission model in paddy fields [23], the assessment of CH 4 emission for coal bed [24] and the uncertainty assessment of national scale inventories about greenhouse gases for Netherlands, Finland and some other countries [18]. In the process of using Monte Carlo simulation, we sampled from the distributions of all of the model inputs, then calculated emissions using the sampled values and so derived model outputs.…”

Section: Monte Carlo Methodsmentioning

confidence: 99%

“…Wang et al [17] conducted quantitative calculation of uncertainty in regional greenhouse gas inventory in 2012. Yang et al [18] carried out the uncertainty analysis on the estimation of CO 2 and CH 4 inventory of Nanjing and Yangtze River Delta emission in 2014. In the guidebooks of good practice guide and uncertainty management for national greenhouse gases emission inventories, there were two kinds of merger methods of uncertainty recommended: error propagation equation and Monte Carlo method.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis for Evaluating CH4 Emission from Solid Waste Landfill of Hefei City Based on Monte Carlo Method

Chen¹,

Jiao²,

Li³

et al. 2017

JRST

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ABSTRACT:It is an important content to analyze the uncertainty of emission from greenhouse gases in compiling the emission inventory. Reliable uncertainty estimates can also be used to increase the quality of emission inventories. This paper took the emission of CH 4 from the solid waste landfill in Hefei City in 2014 as an example, and employed the mass balance method to compute its emission. Monte Carlo method was used to carry out the sensitivity analysis and compute uncertainty. Through computing, 95% confidence interval of CH 4 emission from landfill in Hefei City was (1.89~3.40) × 10 4 t. The mean value was 2.61 × 10 4 t with uncertainty range of -27.59%~30.27%. Among all the parameters, the decomposable proportion of DOC (DOC f ) showed the greatest impact on the emission of CH 4 from the landfill and its contribution to emission result was 26.33%. Followed by the degradable organic carbon (DOC) was 26.29%, the amount of solid waste (MSW) was 13.63%, methane correction factor (MCF) was 6.93% and the proportion of methane in landfill gas (F) was 6.58%. The results showed that compared with traditional error propagation method, it improved the accuracy and quality of assessment and provided a scientific basis to improve the quality of inventory as well.

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“…Uncertainty is expressed as the range (confidence interval) in which there is a 95% probability that the actual value of E i be within that range. It can be used with any type of distribution function for these three input variables (Neter et al 2005;Ramirez et al 2008).…”

Section: Approaches To Evaluate Atmospheric Emission Inventoriesmentioning

confidence: 99%

Assessing precision and accuracy of atmospheric emission inventories

Huertas

Díaz

2012

Int. J. Environ. Sci. Technol.

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Assuming that state-of-the-art air quality models are accurate, then the precision and accuracy of their results directly depend on the precision and accuracy of their geographical, meteorological and emission input data. There are important applications, such as open pit mining, in which emission data are the main source of uncertainty. In such cases, historical air quality experimental data are typically available. The present work proposes a backward air quality simulation approach to assess the accuracy of emission inventories for these applications, with the goal of identifying sources that are over or underestimated. This approach consists of finding constants of the linear combination of the estimated emission that maximize R 2 and make the slope equal to one in the linear correlation analysis when the results from the air quality model are compared to the experimental measurements of air quality. This methodology was applied to the case of the mining region in northern Colombia. As one of the largest open pit coal mining regions in the world, this region consists of seven independent mines with no relevant additional sources of emission. Use of the proposed methodology allowed quantification of the amount by which companies over or underestimated their emission, as well as quantification of uncertainties due to sources not considered in the model but that locally affect each monitoring station.

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Monte Carlo analysis of uncertainties in the Netherlands greenhouse gas emission inventory for 1990–2004

Cited by 67 publications

References 7 publications

Análise probabilística de colisões veiculares pelo método de Monte Carlo

Análise probabilística de colisões veiculares pelo método de Monte Carlo

Uncertainty Analysis for Evaluating CH4 Emission from Solid Waste Landfill of Hefei City Based on Monte Carlo Method

Assessing precision and accuracy of atmospheric emission inventories

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