the evaluation of rainfall influence on cso characteristics: the berlin case study s. sandoval*, a....
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The evaluation of rainfall influence on CSO characteristics: the Berlin case study
S. Sandoval*, A. Torres*,
E. Pawlowsky-Reusing **,
M. Riechel*** and N. Caradot*** * Pontificia Universidad Javeriana, Bogotá, Colombia** Berliner Wasserbetriebe, Berlin, Germany*** Kompetenzzentrum Wasser Berlin, Berlin, Germany
Combined sewer system
Separate sewer system
CSO monitoring station N
CSO monitoring in BerlinSub catchment:
• 126000 inhabitants
• 800 ha impervious area
10 km
Combined sewer system
Separate sewer system Rain gauges
CSO monitoring station N
CSO monitoring in BerlinSub catchment:
• 126000 inhabitants
• 800 ha impervious area
Rainfall
• Annual rain 570 mm/a
• > 10 mm: 13/a
10 km
CSO monitoring
• Average contribution of wastewater to
– CSO volume = 11%
– CSO COD load = 16%
• 84% contribution from other sources !
rain runoff wash-off
resuspension of sewer sediments
• Very strong variability of volume and concentrations
What is the influence of rainfall on CSO characteristics ?
Is it possible to predict CSO characteristics from rainfall ?
Canonical Correlation Analysis CCA
• Linear relationship between two multidimensional data sets:– X (input rainfall characteristics) and Y (output CSO characteristics) – Row: events / Columns: characteristics
• A couple of vectors a and b is found by maximizing correlation (a.X , b.Y)
a1 X1 + a2 X2 + … + an Xn ~ b1 Y1 + b2 Y2 + … + bnYn
• Evaluation of correlation with canonical loadings: – linear correlations between each characteristic and CV
Canonical loading Xi = corr (Xi, CVx)
Canonical loading Yi = corr (Yi, CVy)
Canonical variate xCVx
Canonical variate yCVy
Canonical loadings
Rainfall X
CV5
Duration 0.24
Max intensity -0.43
Depth 0.07
Mean intensity -0.52
DW duration -0.21
Canonical loadings
CSO Y CV5
Duration 0.00
Max. Flow -0.54
Volume -0.40
Mean Flow -0.64
M_COD -0.63
M_TSS -0.51
M_CODd -0.72
mean_TSS -0.24
mean_COD -0.41
mean_CODf -0.47
mean_EC -0.23
Waste ratio (V) -0.29
Waste ratio (M) 0.11
Canonical loadings
Rainfall X
CV6
Duration 0.58
Max intensity 0.35
Depth 0.84
Mean intensity -0.17
DW duration 0.22
Canonical loadings
CSO Y CV6
Duration 0.64
Max. Flow 0.39
Volume 0.72
Mean Flow 0.09
M_COD 0.12
M_TSS 0.23
M_CODd 0.13
mean_TSS -0.55
mean_COD -0.57
mean_CODf -0.62
mean_EC -0.71
Waste ratio (V) -0.71
Waste ratio (M) -0.60
Max intensityMean intensity
Max flowMean flow
Pollutant loads
DurationDepth
DurationVolume
Mean concentrations
Canonical Variate 1 Canonical Variate 2
Partial Least Square regression PLS
• Linear relationship between a multidimensional input variable X (rainfall characteristics) and individual output Y (CSO characteristic)
– Row: events
– Columns: characteristics
• The PLS method projects original data onto a more compact space of latent variables
• A set of coefficients ai is found by maximizing the covariance between X and Y
Y = a1 X1 + a2 X2 + … + an Xc
Identification of most important rain characteristics (high coefficients)
• For each CSO variable (e.g. max. flow)
• Generation of 1000 sets of random rainfall and CSO values within their uncertainty interval 1000 PLS models
Quality of prediction : coefficient of determination R2 Identification of most important X variables
Max intensity DW duration Rain duration
Identification of most relevant explenatory variables
Probability of being the most important
rainfall variable
Duration
Max intensity
DW duration
Max intensityMean intensityDuration Depth
Max flowMean flowDuration Volume
DW durationMax intensity
Pollutant loads
DurationDepth
Mean concentrations
Conclusion
• PLS and CCA highlight the influence of rainfall on CSO characteristics
• For PLS, low determination coefficients were obtained (< 0.6)
• not suitable for prediction purposes,
• useful for exploring the qualitative influence of rainfall on CSO
• Future researches
• Test of other analysis methods (e.g. Artificial Neural Networks)
• Relation between rainfall, CSO and resulting river impacts
30 km
3 km
Combined sewer system
Separate sewer system
Area of water bodies
a
b cd
a River monitoring station
CSO monitoring station
N
Integrated monitoring stations