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Quality Control and Measurement Uncertainty

The amount of environmental data is increasing, and the data would be valuable to the society if they are delivered to the right processes at the right time. In the seminar, we show examples of available data, how they are produced and processed, and how the data can be used in new innovative applications.

This presentation is part of the Environmental Data for Applications Seminar held on the 23rd of September 2015. The seminar was organised by the MMEA (Measurement, Measuring and Environmental Assessment) research programme under the Cleen Ltd (SHOK). The presentations are based on the research results related to environmental data interoperability. The participants included key players and partners in the field of environmental monitoring in Finland.

More info at www.mmea.fi

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Quality Control and Measurement Uncertainty

  1. 1. Quality Control and Measurement Uncertainty Ympäristötiedosta palveluihin – seminaari, 23.9.2015 Mauno Rönkkö (UEF), Okko Kauhanen (UEF), Markus Stocker (UEF), Mikko Kolehmainen (UEF), Harri Hytönen (Vaisala), Olli Ojanperä (Vaisala), Esko Juuso (UO), Markku Ohenoja (UO), Ville Kotovirta (VTT), Maija Ojanen (VTT), Petri Koponen (VTT), Teemu Näykki (SYKE), Jari Koskiaho (SYKE), Niina Kotamäki (SYKE), Jari Silander (SYKE), Hanna Huitu (LUKE), Jussi Nikander (LUKE),…
  2. 2. 24.9.2015Mauno Rönkkö 2 Contents 1. Sources for Uncertainty in Environmental Monitoring 2. Quality Flagging by Nordic Meteorological Institutes 3. Extended Quality Flagging Scheme to Environmental Data 4. Automatic Monitoring – case Väänteenjoki 5. Water Quality Monitoring and MUkit 6. Conclusion
  3. 3. Sources for Uncertainty in Environmental Monitoring 24.9.2015Mauno Rönkkö 3
  4. 4. 1. Case #1: Incomplete Understanding [1/8] 24.9.2015Mauno Rönkkö 4 http://www.mmea.fi
  5. 5. 1. Case #2: Indirect Measurements [2/8] •The Finnish Environmental Institute (SYKE) monitors total phosphorus of lakes and rivers in Finland. •Currently, there is no device that measures total phosphorus of a lake. •The amount of total phosphorus is (1) estimated based on the amount of suspended solids (2) which is estimated based on measured turbidity at (3) a given location on a lake/river. 24.9.2015Mauno Rönkkö 5 http://wwwi3.ymparisto.fi/i3/sakylapyhajarvi/sakylapyhajarvi.htm
  6. 6. 1. Case #3: Heterogeneous Measurement Methods [3/8] 24.9.2015Mauno Rönkkö 6 http://www.vaisala.com/en/products/ automaticweatherstations/Pages/default.aspx http://www.biltema.fi/fi/Toimisto---Tekniikka/Kellot- ja-Lampomittarit/Lampomittari/Langaton-saaasema- 84086/
  7. 7. 1. Case #4: Sampling [4/8] 24.9.2015Mauno Rönkkö 7 Is this a good enough sample?
  8. 8. 1. Case #5: Inconsistent Treatment of Measurement Errors [5/8] 24.9.2015Mauno Rönkkö 8 ERRORS (random errors + systematic errors): -Devices are individuals -Measurements drift over time -Devices are positioned badly -Devices are used in non-optimal conditions -Measurement noise is too large for measured quantity -Person measuring affects the measurements -Environmental conditions vary and affect measurements -Several different measurement devices used to get a dataset -Spatially and temporally different measurements are not preprocessed -Wrong devices are used in a specific measurement method -Different measurement methods with different devices are used -No calibration -Standards and not used or they are used improperly -Measurement data is treated with wrong statistical methods …
  9. 9. 1. Case #5: Inconsistent Treatment of Measurement Errors [6/8] 24.9.2015Mauno Rönkkö 9 ERRORS (the less recognized but most important): -Non-synchronized measurement clocks -Cognitive pitfalls
  10. 10. 1. Case #6: Semantically Inconsistent Interoperability [7/8] 24.9.2015Mauno Rönkkö 10
  11. 11. 1. Case #7: Poorly Understood Uncertainties and Validities [8/8] 24.9.2015Mauno Rönkkö 11
  12. 12. Quality Flagging by Nordic Meteorological Institutes 24.9.2015Mauno Rönkkö 12 F. Vejen (ed), C. Jacobsson, U. Fredriksson, M. Moe, L. Andresen, E. Hellsten, P. Rissanen, T. Palsdottir, and T. Arason. Quality Control of Meteorological Observa- tions. Automatic Methods Used in the Nordic Countries. Climate Report 8/2002, Norwegian Meteorological Institute, 2002.
  13. 13. 2. Why bother? 24.9.2015Mauno Rönkkö 13 THIS IS WATER CONSUMPTION!?
  14. 14. 2. Why bother? 24.9.2015Mauno Rönkkö 14 IS THIS WATER CONSUMPTION!?
  15. 15. 2. Why bother? 24.9.2015Mauno Rönkkö 15 THIS IS WATER CONSUMPTION!
  16. 16. 2. Data to information 24.9.2015Mauno Rönkkö 16 Measurement device Server Data storage Data analysis and refinement Human operator
  17. 17. 2. Quality checks 24.9.2015Mauno Rönkkö 17 QC0 QC1 QC2 HQC Measurement device Server Data storage Data analysis and refinement Human operator
  18. 18. 2. Quality checks 24.9.2015Mauno Rönkkö 18 QC1 QC2 HQC Measurement device Server Data storage Data analysis and refinement Human operator QC0: real-time quality control on individual data points about range, step and consistency
  19. 19. 2. Quality checks 24.9.2015Mauno Rönkkö 19 QC2 HQC Measurement device Server Data storage Data analysis and refinement Human operator QC0: real-time quality control on individual data points about range, step and consistency QC1: real-time quality control on individual data points using statistical methods, including missing and expected values
  20. 20. 2. Quality checks 24.9.2015Mauno Rönkkö 20 HQC Measurement device Server Data storage Data analysis and refinement Human operator QC0: real-time quality control on individual data points about range, step and consistency QC1: real-time quality control on individual data points using statistical methods, including missing and expected values QC2: non-real-time quality control on data sets including spatial and temporal analysis with corrective computations
  21. 21. 2. Quality checks 24.9.2015Mauno Rönkkö 21 Measurement device Server Data storage Data analysis and refinement Human operator QC0: real-time quality control on individual data points about range, step and consistency QC1: real-time quality control on individual data points using statistical methods, including missing and expected values QC2: non-real-time quality control on data sets including temporal and spatial analysis with corrective computations HQC: non-real-time quality inspection including visualization; the final word
  22. 22. 2. Measurement Data 24.9.2015Mauno Rönkkö 22 556 2014-09-27T09:00:30 62.8925, 27.678333 22.6 42 time humiditytemperature location device-id
  23. 23. 2. Measurement Data with a Quality Flag 24.9.2015Mauno Rönkkö 23 556 2014-09-27T09:00:30 62.8925, 27.678333 22.6 42 9330 time quality flag humiditytemperature location device-id
  24. 24. 2. The Flag Values 24.9.2015Mauno Rönkkö 24 556 2014-09-27T09:00:30 62.8925, 27.678333 22.6 42 9330 time quality flag humiditytemperature location device-id C = 1000EHQC + 100EQC2 + 10EQC1 + EQC0
  25. 25. 2. Multiple Data Points 24.9.2015Mauno Rönkkö 25 556 2014-09-27T09:00:30 62.8925, 27.678333 22.6 42 9330 C = 1000EHQC + 100EQC2 + 10EQC1 + EQC0 556 2014-09-27T09:00:30 62.8925, 27.678333 15.3 55 4000
  26. 26. Extending the Quality Flagging Scheme to Environmental Data 24.9.2015Mauno Rönkkö 26 M. Rönkkö, O. Kauhanen, M. Stocker, H. Hytönen, V. Kotovirta, E. Juuso, M. Kolehmainen. Quality Control of Environmental Measurement Data with Quality Flagging. IFIP Advances in Information and Communication Technology, 2015, Volume 448, Environmental Software Systems. Infrastructures, Services and Applications, pages 343-350.
  27. 27. 3. Generic Interpretation 24.9.2015Mauno Rönkkö 27 Flag Original interpretation Generic interpretation 0 No check performed Value not checked 1 Observation is ok Approved value 2 Suspected small difference Suspicious value 3 Suspected big difference Anomalous value 4 Calculated value Corrected value 5 Interpolated value Imputed value 6 (Not defined originally) Erroneous value 7 (Not defined originally) Frozen value 8 Missing value Missing value 9 Deleted value Deleted value
  28. 28. 3. Example Service Architecture 24.9.2015Mauno Rönkkö 28
  29. 29. 3. Quality Control of Water Consumption Data 24.9.2015Mauno Rönkkö 29
  30. 30. 3. Quality Control of Water Consumption Data 24.9.2015Mauno Rönkkö 30 QC1: measured and checked once a minute QC2: runs every 2 hours, used for spotting leaks and malfunctions HQC: done once a month, aims at resolving frozen data values
  31. 31. Automatic Monitoring - case Väänteenjoki 24.9.2015Mauno Rönkkö 31
  32. 32. 24.9.2015Mauno Rönkkö 32 4. Automatic monitoring – case Väänteenjoki • The challenge in automating water quality monitoring is that the measurement data have not only significant seasonal variation, but also erroneous values • Thus, without proper quality control and reliable uncertainty estimation, the data has little value • As a solution, we have implemented a computation service based on an Enterprise Service Bus Architecture. The service provides means for online quality control and integration of uncertainty estimation • Case study: In the Karjaanjoki River Basin the Väänteenjoki site equipped with an OBS3+ turbidity sensor (Campbell Scientific inc.) • OBS3+ sensor emits a near-infrared light into the water, measures the light that scatters back from the suspended particles, and transforms this information into turbidity values in Nephelometric Turbidity Units (NTU)
  33. 33. 24.9.2015Mauno Rönkkö 33 4. Automatic monitoring – case Väänteenjoki • The “raw” turbidity recorded by the OBS3+ sensor had to be calibrated against the turbidity determined from water samples taken near the sensor • Calibration equation was determined by linear regression between the values of the water samples and the simultaneous values recorded by the sensor • Then, because turbidity does not denote the content of substance in water, the calibrated turbidity data had to be converted to concentrations of susp.solids and total P • We have implemented a computational service that automates and integrates uncertainty estimation to the sequence of operations
  34. 34. Water Quality Monitoring and MUkit 24.9.2015Mauno Rönkkö 34
  35. 35. 24.9.2015Mauno Rönkkö 35
  36. 36. 24.9.2015Mauno Rönkkö 36
  37. 37. 24.9.2015Mauno Rönkkö 37
  38. 38. 24.9.2015Mauno Rönkkö 38
  39. 39. Conclusion 24.9.2015Mauno Rönkkö 39
  40. 40. 5. Conclusion [1/2] •What you cannot measure, you cannot control. •Sources for uncertainties Incomplete understanding, Indirect measurements, Heterogeneous measurement methods, Sampling, Inconsistent treatment of measurement errors, Semantically inconsistent interoperability, Poorly understood uncertainties and validities •Quality Flagging – scheme by the Nordic Meteorological Institutes – Quality checks at various stages; Real-time and non-real-time checks •Quality Flagging of Environmental Data – Generic interpretation – ESB based architecture 24.9.2015Mauno Rönkkö 40
  41. 41. 5. Conclusion [2/2] •Automatic monitoring – case Väänteenjoki – proper quality control and reliable uncertainty estimation required – implemented a computation service based on an ESB •Water quality monitoring and MUkit – Based on the Nordtest TR 537 guide and on the standard SFS-EN ISO 11352 – Automated turbidity measuring system for ”real-time” uncertainty estimation using AutoMUkit – Several international publications available! 24.9.2015Mauno Rönkkö 41
  42. 42. Mauno Rönkkö mauno.ronkko@uef.fi tel. +358 40 355 2202 www.uef.fi

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