Evaluation and implementation of measurement uncertainty for determining stationary source emissions: a review
This paper presents a review of commonly-cited methods for estimating uncertainty in the literature. One of them is the non-stochastic approach 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 probability distributions. Other methods to estimate uncertainty are discussed as well; they include Monte Carlo, Fuzzy Sets, Generalized Intervals, Bayesian Inference, Polynomial Chaos, and Bootstrap, which in contrast to GUM present limitations regarding computational cost and require more specialized knowledge to be implemented. The aim of this work is to report the level of application and dissemination of methods for estimating the uncertainty of emissions caused by stationary sources. Most of the works in this field were found to be focused on the creation of inventories of Greenhouse Gases (GHG), and very few of them on the uncertainty associated with measuring the emissions of stationary sources using direct reading monitoring or those defined by the Environmental Protection Agency of the United States (US EPA). Finally, strengths and weaknesses are discussed in order to promote new research in this knowledge area.
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