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N. Caradot , H. Sonnenberg, M. Riechel, A. Matzinger and P. Rouault Kompetenzzentrum Wasser Berlin

The influence of local calibration on the quality of UV-VIS spectrometer measurements in urban stormwater monitoring. N. Caradot , H. Sonnenberg, M. Riechel, A. Matzinger and P. Rouault Kompetenzzentrum Wasser Berlin. Online CSO monitoring in Berlin. Separate sewer system. 10 km.

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N. Caradot , H. Sonnenberg, M. Riechel, A. Matzinger and P. Rouault Kompetenzzentrum Wasser Berlin

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  1. The influence of local calibration on the quality of UV-VIS spectrometer measurements in urban stormwater monitoring N. Caradot, H. Sonnenberg, M. Riechel, A. Matzinger and P. Rouault Kompetenzzentrum Wasser Berlin

  2. Online CSO monitoring in Berlin Separate sewer system 10 km Combined sewer system CSO monitoring station N

  3. Online CSO monitoring in Berlin

  4. Spectrometer calibration and uncertainties User local calibration Manufacturer global calibration Local concentrations TSS, COD, etc. Absorbance measurement Concentrations TSS, COD, etc. • default manufacturer configuration • for typical municipal waste or river • More than 50% error (Austrian study, Gamerith et al., 2011) • Online probes need to be calibrated to local conditions !!!

  5. error spectro us=3% error from lab ul=10% Calculation using Monte-Carlo analysis 10,000 regressions Mean a and b SD a and b Spectrometer calibration and uncertainties (COD) Y=a1.x+b1 Y=a2.x+b2 Y=a.x+b … ± u(y) • Calibration error: • Error from calibration curve (confidence interval) • Error from new prediction RMSE

  6. CSO COD load: sources of uncertainty • RMSE contributes to > 70% of load uncertainty • underlines the importance of the collection of samples to build reliable local calibration function • … what is the optimal sampling effort to calibrate the probes ?

  7. Sampling during CSO events parallel to online measurements Flow trigger (> 0.3 m³/s) Grab sampling each 5 minutes 15 CSO events with a minimum of 5 samples (75 samples) between 2010 and 2012 Data available for spectrometer calibration in Berlin

  8. Calibration parameter + uncertainty  All events Using all 75 samples (i.e. 15 events) • total COD load is 29 t

  9. Calibration parameter + uncertainty  Chronology of events At least 20 samples (i.e. 4 events) : • stable coefficients and uncertainty • stable load The effort to gain more than 20 samples is less effective and not necessary !!!

  10. Calibration parameter + uncertainty  Berlin and Graz Same results in Graz and Berlin !!! At least 20 samples (i.e. 4 events) : • stable coefficients and uncertainty • stable load The effort to gain more than 20 samples is less effective and not necessary !!!

  11. Calibration parameter + uncertainty  Combination of events Same results using combination of events: At least 20 samples (i.e. 4 events) : • stable load: 29 t • stable uncertainty: 20 %

  12. Calibration parameter + uncertainty  Combination of events Using Global calibration from the manufacturer: • total COD load is 19 t •  high underestimation of about 30%

  13. Conclusion • UV-VIS probes need to be calibrated to local conditions !!! • e.g. Berlin: global calibration30% underestimation for COD load • Even with local calibration : significant uncertainties ~ 20% (conc. and load) • Good estimation of calibration parameters with more than 20 grab samples (4 events) • Effort and sampling costs to gain more than 20 samplesless effective • Parameters and loads stable with an increasing number of samples !!! • Results representative of the local Berlin case study : no general rule !!! validation of results on other case studies in progress! • Berlin • Graz • Lyon • Bogota

  14. Thank you for your attention ! More information : nicolas.caradot@kompetenz-wasser.de

  15. 1. Subset creation • Input data : samples = pairs (spectrometer probe values; related lab values) • Each sample belongs to an event (CSO or river impact) • Within one event : chronology of samples maintained to avoid unrealistic combinations  Generation of subsets of samples for all possible combinations of events

  16. 2. Local calibration For each subset : calibration function (linear regression) between probe and lab values.

  17. 3. Concentration and load calculation Calculation of calibrated COD concentrations + total load over all the events (CSO) COD = a1 . x + b1 COD = a14 . x + b14

  18. 3. Concentration and load calculation Calculation of calibrated COD concentrations + total load over all the events (CSO) COD = a1 . x + b1 Annual CSO Load M Uncertainty U(M) Generation of 50 random M values (Monte Carlo) - normal distribution - SD = u(M) = RMSE

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