Uncertainty is the fraction of error associated with the determination of the final concentration of species prepared as inputs for PMF source apportionment model. Including: Analytical, personal and other errors. It is important for the recpetor models based on multivariate factor analysis to take in account the errors. There are a lot of methods for the calculation of the uncertainties but the most famous one is by Polissar et. al., 1998 (Polissar, A.V., Paatero, P., Hopke, P.K., Malm, W.C., Sisler, J.F., 1998. Atmospheric aerosol over Alaska 2. Elemental composition and sources. J. Geophys. Res. 103, 19045-19057.)
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Quality and cost are directly impacted by measurement uncertainty. Many industries including research, manufacturing, finance, and healthcare rely on reports that contain quantitative data from measurement results. Product quality, experiment results, financial decisions, and medical diagnosis can all be directly impacted by errors introduced from the omission of measurement uncertainty. Without awareness or consideration of the impact measurement uncertainty has on quality, the greater the probability of increased operating costs and failure rates.
Measurement uncertainty is critical to risk assessment and decision making. Organizations make decisions every day based on reports containing quantitative measurement data. If measurement results are not accurate, then decision risks increase.
The need for increased accuracy is not as important as the need to measure quality. Accuracy should only be adequate enough to effectively satisfy each organizations established requirements.
The measurement of uncertainty is not an easy task. The following link address the steps of measurement of uncertainty
https://www.isobudgets.com/7-steps-to-calculate-measurement-uncertainty/
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