Evaluation and implementation of the uncertainty in measurement for the determination of stationary source emissions: a review

Jhon J. Cárdenas-Monsalve, Andrés F. Ramírez-Barrera, Edilson Delgado-Trejos


This paper presents a review of methodologies commonly cited in the literature for estimating the uncertainty, like the non-stochastic methodology proposed by the Guide to the Expression of Uncertainty in Measurement (GUM), which provides an estimation framework with limitations for the implementation, such as: computation of partial derivatives, linear model assumptions and uncertainty source identification with its probability distributions. On the other hand, other methods to estimate uncertainty are discussed, such as: Monte Carlo, Fuzzy Sets, Generalized Intervals, Bayesian Inference, Polynomial Chaos and Bootstrap, which in contrast with GUM presents limitations regarding computational cost and require more specialized knowledge for their implementation. The aim of this work consists of reporting the level of application and promulgation of methods for estimating the uncertainty of emissions caused by stationary sources, where it was found that most of the works focusses on the elaboration of inventories of Greenhouse Gases (GHG), and very few those oriented to the uncertainty associated with the emission measurements of stationary sources using direct reading monitoring, or those defined by the Environmental Protection Agency of the United States (US EPA). Finally, strengths and weakness are discussed in order to promote new researches in this knowledge area.


Measurement uncertainty; Guide to the Expression of Uncertainty in Measurement GUM; Monte Carlo method; stochastic methods

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