Cargando…
Automatic real-time uncertainty estimation for online measurements: a case study on water turbidity
Continuous sensor measurements are becoming an important tool in environmental monitoring. However, the reliability of field measurements is still too often unknown, evaluated only through comparisons with laboratory methods or based on sometimes unrealistic information from the measuring device man...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6445822/ https://www.ncbi.nlm.nih.gov/pubmed/30941608 http://dx.doi.org/10.1007/s10661-019-7374-7 |
_version_ | 1783408246328918016 |
---|---|
author | Kahiluoto, Joonas Hirvonen, Jukka Näykki, Teemu |
author_facet | Kahiluoto, Joonas Hirvonen, Jukka Näykki, Teemu |
author_sort | Kahiluoto, Joonas |
collection | PubMed |
description | Continuous sensor measurements are becoming an important tool in environmental monitoring. However, the reliability of field measurements is still too often unknown, evaluated only through comparisons with laboratory methods or based on sometimes unrealistic information from the measuring device manufacturers. A water turbidity measurement system with automatic reference sample measurement and measurement uncertainty estimation was constructed and operated in laboratory conditions to test an approach that utilizes validation and quality control data for automatic measurement uncertainty estimation. Using validation and quality control data for measurement uncertainty estimation is a common practice in laboratories and, if applied to field measurements, could be a way to enhance the usability of field sensor measurements. The measurement system investigated performed replicate measurements of turbidity in river water and measured synthetic turbidity reference solutions at given intervals during the testing period. Measurement uncertainties were calculated for the results using AutoMUkit software and uncertainties were attached to appropriate results. The measurement results correlated well (R(2) = 0.99) with laboratory results and the calculated measurement uncertainties were 0.8–2.1 formazin nephelometric units (FNU) (k = 2) for 1.2–5 FNU range and 11–27% (k = 2) for 5–40 FNU range. The measurement uncertainty estimation settings (such as measurement range selected and a number of replicates) provided by the user have a significant effect on the calculated measurement uncertainties. More research is needed especially on finding suitable measurement uncertainty estimation intervals for different field conditions. The approach presented is also applicable for other online measurements besides turbidity within limits set by available measurement devices and stable reference solutions. Potentially interesting areas of application could be the measurement of conductivity, pH, chemical oxygen demand (COD)/total organic carbon (TOC), or metals. |
format | Online Article Text |
id | pubmed-6445822 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-64458222019-04-17 Automatic real-time uncertainty estimation for online measurements: a case study on water turbidity Kahiluoto, Joonas Hirvonen, Jukka Näykki, Teemu Environ Monit Assess Article Continuous sensor measurements are becoming an important tool in environmental monitoring. However, the reliability of field measurements is still too often unknown, evaluated only through comparisons with laboratory methods or based on sometimes unrealistic information from the measuring device manufacturers. A water turbidity measurement system with automatic reference sample measurement and measurement uncertainty estimation was constructed and operated in laboratory conditions to test an approach that utilizes validation and quality control data for automatic measurement uncertainty estimation. Using validation and quality control data for measurement uncertainty estimation is a common practice in laboratories and, if applied to field measurements, could be a way to enhance the usability of field sensor measurements. The measurement system investigated performed replicate measurements of turbidity in river water and measured synthetic turbidity reference solutions at given intervals during the testing period. Measurement uncertainties were calculated for the results using AutoMUkit software and uncertainties were attached to appropriate results. The measurement results correlated well (R(2) = 0.99) with laboratory results and the calculated measurement uncertainties were 0.8–2.1 formazin nephelometric units (FNU) (k = 2) for 1.2–5 FNU range and 11–27% (k = 2) for 5–40 FNU range. The measurement uncertainty estimation settings (such as measurement range selected and a number of replicates) provided by the user have a significant effect on the calculated measurement uncertainties. More research is needed especially on finding suitable measurement uncertainty estimation intervals for different field conditions. The approach presented is also applicable for other online measurements besides turbidity within limits set by available measurement devices and stable reference solutions. Potentially interesting areas of application could be the measurement of conductivity, pH, chemical oxygen demand (COD)/total organic carbon (TOC), or metals. Springer International Publishing 2019-04-02 2019 /pmc/articles/PMC6445822/ /pubmed/30941608 http://dx.doi.org/10.1007/s10661-019-7374-7 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article Kahiluoto, Joonas Hirvonen, Jukka Näykki, Teemu Automatic real-time uncertainty estimation for online measurements: a case study on water turbidity |
title | Automatic real-time uncertainty estimation for online measurements: a case study on water turbidity |
title_full | Automatic real-time uncertainty estimation for online measurements: a case study on water turbidity |
title_fullStr | Automatic real-time uncertainty estimation for online measurements: a case study on water turbidity |
title_full_unstemmed | Automatic real-time uncertainty estimation for online measurements: a case study on water turbidity |
title_short | Automatic real-time uncertainty estimation for online measurements: a case study on water turbidity |
title_sort | automatic real-time uncertainty estimation for online measurements: a case study on water turbidity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6445822/ https://www.ncbi.nlm.nih.gov/pubmed/30941608 http://dx.doi.org/10.1007/s10661-019-7374-7 |
work_keys_str_mv | AT kahiluotojoonas automaticrealtimeuncertaintyestimationforonlinemeasurementsacasestudyonwaterturbidity AT hirvonenjukka automaticrealtimeuncertaintyestimationforonlinemeasurementsacasestudyonwaterturbidity AT naykkiteemu automaticrealtimeuncertaintyestimationforonlinemeasurementsacasestudyonwaterturbidity |