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  • 1.
    Näykki, Teemu
    et al.
    Finnish Environment Institute, Finland.
    Virtanen, Atte
    Finnish Environment Institute, Finland.
    Kaukonen, Lari
    Finnish Environment Institute, Finland.
    Magnusson, Bertil
    RISE, SP – Sveriges Tekniska Forskningsinstitut, SP Kemi Material och Ytor, Kemi.
    Väisänen, Tero
    Finnish Environment Institute, Finland.
    Leito, Ivo
    University of Tartu, Estonia.
    Application of the Nordtest method for “real-time” uncertainty estimation of on-line field measurement2015In: Environmental Monitoring & Assessment, ISSN 0167-6369, E-ISSN 1573-2959, Vol. 187, no 10, article id 630Article in journal (Refereed)
    Abstract [en]

    Field sensor measurements are becoming more common for environmental monitoring. Solutions for enhancing reliability, i.e. knowledge of the measurement uncertainty of field measurements, are urgently needed. Real-time estimations of measurement uncertainty for field measurement have not previously been published, and in this paper, a novel approach to the automated turbidity measuring system with an application for “real-time” uncertainty estimation is outlined based on the Nordtest handbook’s measurement uncertainty estimation principles. The term real-time is written in quotation marks, since the calculation of the uncertainty is carried out using a set of past measurement results. There are two main requirements for the estimation of real-time measurement uncertainty of online field measurement described in this paper: (1) setting up an automated measuring system that can be (preferably remotely) controlled which measures the samples (water to be investigated as well as synthetic control samples) the way the user has programmed it and stores the results in a database, (2) setting up automated data processing (software) where the measurement uncertainty is calculated from the data produced by the automated measuring system. When control samples with a known value or concentration are measured regularly, any instrumental drift can be detected. An additional benefit is that small drift can be taken into account (in real-time) as a bias value in the measurement uncertainty calculation, and if the drift is high, the measurement results of the control samples can be used for real-time recalibration of the measuring device. The procedure described in this paper is not restricted to turbidity measurements, but it will enable measurement uncertainty estimation for any kind of automated measuring system that performs sequential measurements of routine samples and control samples/reference materials in a similar way as described in this paper.

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