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Application of the Nordtest method for “real-time” uncertainty estimation of on-line field measurement

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Abstract

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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References

  • Appleby, J. P., & Scarratt, D. J. (1989). Physical effects of suspended solids on marine and estuarine fish and shellfish with special reference to ocean dumping: a literature review. Canadian Technical Report of Fisheries and Aquatic Sciences No. 1681.

  • Arola, H. (Ed.). (2012). Jatkuvatoiminen sameusmittaus—Hyvät mittauskäytännöt ja aineistonkäsittely (Continuous turbidity measurement—best measurement practices and data processing), Environmental Administration Guidelines 2/2012 (In Finnish). Helsinki: Finnish Environment Institute.

    Google Scholar 

  • Azzaro, F. (2013). Automated nutrients analysis for buoys in sea-water and intercalibration. International Journal of Environmental Monitor Analysis, 1(6), 315–322.

    Article  Google Scholar 

  • Bourgeois, W., Burgess, J. E., & Stuetz, R. M. (2001). On-line monitoring of wastewater quality: a review. Journal of Chemical Technology and Biotechnology, 76(4), 337–348.

    Article  CAS  Google Scholar 

  • Clinch, J. R., & Worsfold, P. J. (1987). An automated spectrophotometric field monitor for water quality parameters: determination of nitrate. Analitica Chimica Acta, 200, 523–531.

    Article  CAS  Google Scholar 

  • Eurachem/CITAC Guide CG 4 (2012). Quantifying Uncertainty in Analytical Measurement, 3rd edition. Available via www.eurachem.org.

  • Eurolab (2007). Measurement uncertainty revisited: alternative approaches to uncertainty evaluation. Available via: www.eurolab.org/documents/Technical_Report_Measurement_Uncertainty_2007.pdf.

  • European Inland and Fisheries Advisory Commission. (1964). Water quality criteria for European freshwater fish: report on finely divided solids and inland fisheries. Journal of Air and Water Pollution, 9(3), 151–168.

    Google Scholar 

  • Glasgow, H. B., Burkholder, J. M., Reed, R. E., Lewitus, A. J., & Kleinman, J. E. (2004). Real-time remote monitoring of water quality: a review of current applications, and advancements in sensor, telemetry, and computing technologies. Journal of Experimental Marine Biology and Ecology, 300(1–2), 409–448.

    Article  Google Scholar 

  • Hovind, H., Magnusson, B., Krysell, M., Lund, & U., Mäkinen, I. (2011). Nordtest Technical Report 569 - Internal Quality Control - Handbook for Chemical laboratories, 4th edition, Nordic Innovation, Oslo, Norway. Available via www.nordtest.info.

  • International Organization for Standardization (1999). ISO 7027, Water quality — Determination of turbidity.

  • International Organization for Standardization (2012). ISO 11352, Water quality — Estimation of measurement uncertainty based on validation and quality control data.

  • Joint Committee for Guides in Metrology (2008). Evaluation of measurement data - Guide to the expression of uncertainty in measurement (GUM). JCGM 100:2008. Available via www.bipm.org.

  • Kerr, S. J. (1995). Silt, turbidity and suspended sediments in the aquatic environment: an annotated bibliography and literature review. Ontario: Ontario Ministry of Natural Resources, Southern Region Science & Technology Transfer Unit Technical Report TR-008. 277 pp.

    Google Scholar 

  • Leivuori, M., Björklöf, K., Näykki, T., & Väisänen, R. (2013). Laboratorioiden välinen pätevyyskoe 5/2013, Kenttämittaukset –vesien happi, lämpötila, pH ja sähkönjohtavuus (In Finnish), Finnish Environment Institute, Helsinki, Finland. Available via http://www.syke.fi/download/noname/%7BFD00FB6D-21B3-4D8F-8C35-424926B9E626%7D/92086.

  • Magnusson, B., & Koch, M. (2012). Use of characteristic functions derived from proficiency testing data to evaluate measurement uncertainties. Accreditation and Quality Assurance, 17, 399–403.

    Article  Google Scholar 

  • Magnusson, B., Näykki, T., Hovind, H., & Krysell, M. (2011). Nordtest Technical Report 537 - Handbook for the calculation of measurement uncertainty in environmental laboratories, 3rd edition, Nordic Innovation, Oslo, Norway. Available via www.nordtest.info.

  • MUkit website: http://www.syke.fi/en-US/Services/Calibration_services_and_contract_laboratory/MUkit__Measurement_Uncertainty_Kit.

  • Näykki, T., Virtanen, A., & Leito, I. (2012). Software support for the Nordtest method of measurement uncertainty evaluation. Accreditation and Quality Assurance, 17, 603–612.

    Article  Google Scholar 

  • Näykki, T., Jalukse, L., Helm, I., & Leito, I. (2013). Dissolved oxygen concentration interlaboratory comparison: what can we learn? Water, 5(2), 420–442.

    Article  Google Scholar 

  • Tschmelak, J., Proll, G., Riedt, J., Kaiser, J., Kraemmer, P., Bárzaga, L., Wilkinson, J. S., Hua, P., Hole, J. P., Nudd, R., Jackson, M., Abuknesha, R., Barceló, D., Rodriguez-Mozaz, S., López de Alda, M. J., Sacher, F., Stien, J., Slobodník, J., Oswald, P., Kozmenko, H., Korenková, E., Tóthová, L., Krascsenits, Z., & Gauglitz, G. (2005). Automated water analyser computer supported system (AWACSS) Part II: intelligent, remote-controlled, cost-effective, on-line, water-monitoring measurement system. Biosensors and Bioelectronics, 20, 1509–1519.

    Article  CAS  Google Scholar 

  • Valkama, P., Lahti, K., & Särkelä, A. (2007). Automaattinen veden laadun seuranta Lepsämänjoella. (Automated water quality monitoring in the River Lepsämänjoki), Terra, 119(3–4), 195–206. Department of Geosciences and geography, University of Helsinki, Finland.

  • Wang, Q., Li, Y., Obreza, T., & Munoz-Carpena, R. (2004). Monitoring stations for surface water quality. University of Florida, IFAS Extension, Fact Sheet SL 218. Available via http://ufdc.ufl.edu/IR00003153/00001.

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Acknowledgments

The Cleen Ltd MMEA programme and Maa-ja vesitekniikan tuki ry are acknowledged for their financial support.

This work has also been partially supported by Graduate School “Functional materials and technologies” receiving funding from the European Social Fund under project 1.2.0401.09-0079 at the University of Tartu, Estonia.

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Correspondence to Teemu Näykki.

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Näykki, T., Virtanen, A., Kaukonen, L. et al. Application of the Nordtest method for “real-time” uncertainty estimation of on-line field measurement. Environ Monit Assess 187, 630 (2015). https://doi.org/10.1007/s10661-015-4856-0

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