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N. Caradot , H. Sonnenberg, M. Riechel, A. Matzinger and P. Rouault Kompetenzzentrum Wasser Berlin PowerPoint Presentation
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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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slide1

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

slide2

Online CSO monitoring in Berlin

Separate sewer system

10 km

Combined sewer system

CSO monitoring station

N

slide4

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 !!!
slide5
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

slide6

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 ?
slide7
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

slide8

Calibration parameter + uncertainty  All events

Using all 75 samples (i.e. 15 events)

  • total COD load is 29 t
slide9

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 !!!

slide10

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 !!!

slide11

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 %
slide12

Calibration parameter + uncertainty  Combination of events

Using Global calibration from the manufacturer:

  • total COD load is 19 t
  •  high underestimation of about 30%
slide13

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
slide14

Thank you for your attention !

More information : nicolas.caradot@kompetenz-wasser.de

slide15

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

slide16

2. Local calibration

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

slide17

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

slide18

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