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Predictive Models to Assess the Uptake of Organic Microcontaminants and Antibiotic Resistant Bacteria and Genes by Crops
Published in María del Carmen Durán-Domínguez-de-Bazúa, Amado Enrique Navarro-Frómeta, Josep M. Bayona, Artificial or Constructed Wetlands, 2018
Josep M. Bayona, Stefan Trapp, Benjamín Piña, Fabio Polesel
A common approach for uncertainty analysis is the definition of probability distributions for relevant model parameters and the propagation of uncertainty to model predictions using the Monte Carlo method. This approach has been used for uncertainty analysis of models used in risk assessment (Takaki et al. 2014) and a dynamic mechanistic model for ionisable PPCPs (Polesel et al. 2015). These studies revealed that up to two-order of magnitude variability can be expected in predicted BCFs for the same chemical, independently of the model considered. Such variability can be attributed to environmental (e.g., soil fOC) and chemical properties (half-life in soil, partition coefficients). In the context of plant uptake of wastewater-borne PPCPs, additional variability in BCFs will be likely associated to: (i) chemical input pulses (via, e.g., irrigation), which are region-, season- and plant-dependent; and (ii) degradation in plants, which is to date largely unknown (Hurtado et al. 2016).
Measurement Error or Data Trend? How to Conduct Meaningful Experiments
Published in Josua P. Meyer, Michel De Paepe, The Art of Measuring in the Thermal Sciences, 2020
Nevertheless, calculation of the whole error analysis might be much more complicated than in the simple example above. Analytical calculation of the Taylor series method leads to very long equations especially if a cross-correlation has to be considered. One might be tempted to avoid the effort and to set up a spreadsheet or a computer program instead. This way, one can change the input values of the data reduction equations by small portions and monitor the sensitivity of the results which will indicate the propagation of uncertainty based on datasets. Actually, this is not far away from the other method of error propagation analysis, the Monte Carlo method (MCM).
Measurement Uncertainty
Published in Richard Leach, Stuart T. Smith, Basics of Precision Engineering, 2017
With a Monte Carlo method, both the propagation of standard uncertainties and the propagation of uncertainty distributions can be simulated. The Monte Carlo method consists of the generation of a large number of simulated measurements according to a defined distribution, applying the calculation using the model function for each individual simulated measurement and considering the distribution of the output values. Simulations are made using the input variable ξi that is given by
An Analytic Benchmark for Neutron Boltzmann Transport with Downscattering—Part III: Uncertainty Propagation and Multigroup Covariance Matrices
Published in Nuclear Science and Engineering, 2023
Ketaki Joshi, Nicholas Branam, Isaac Meyer, Ben Forget, Abdulla Alhajri, Vladimir Sobes
Studies on the propagation of uncertainty from uncertain input quantities to computational model predictions set the basis for the trustworthiness and applicability of these models. Currently, in neutronics modeling there are two methods to propagate uncertainties from nuclear data to k-eigenvalue calculations. The first method is a deterministic, first-order propagation of uncertainty through the “sandwich rule,”