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Supporting Information Assessment of the fate of organic micropollutants in novel wastewater treatment plant configurations through an empirical mechanistic model Anton Taboada-Santos a,1, *, Chitta Ranjan Behera b,1 , Gürkan Sin b , Krist V. Gernaey b , Miguel Mauricio a , Marta Carballa a and Juan M. Lema a a) Department of Chemical Engineering, School of Engineering, Universidade de Santiago de Compostela, E- 15782, Santiago de Compostela, Spain. b) Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Building 229, 2800 Kgs. Lyngby, Denmark 1) Equally contributed to the work. *Anton Taboada-Santos: corresponding author E-mail: [email protected] Tel: +34 881 816021; Fax: +34 881 816702 E-mail addresses: [email protected] (A. Taboada-Santos), [email protected] (Chitta Ranjan Behera), [email protected] (Gürkan Sin), [email protected] (Krist V. Gernaey), [email protected]

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Supporting Information

Assessment of the fate of organic micropollutants in novel wastewater

treatment plant configurations through an empirical mechanistic model

Anton Taboada-Santosa,1, *, Chitta Ranjan Beherab,1, Gürkan Sinb, Krist V. Gernaeyb, Miguel

Mauricioa, Marta Carballaa and Juan M. Lemaa

a) Department of Chemical Engineering, School of Engineering, Universidade de Santiago de

Compostela, E- 15782, Santiago de Compostela, Spain.

b) Process and Systems Engineering Center (PROSYS), Department of Chemical and

Biochemical Engineering, Technical University of Denmark, Building 229, 2800 Kgs.

Lyngby, Denmark

1) Equally contributed to the work.

*Anton Taboada-Santos: corresponding author

E-mail: [email protected]

Tel: +34 881 816021; Fax: +34 881 816702

E-mail addresses: [email protected] (A. Taboada-Santos), [email protected] (Chitta

Ranjan Behera), [email protected] (Gürkan Sin), [email protected] (Krist V. Gernaey),

[email protected] (Miguel Mauricio), [email protected] (M. Carballa),

[email protected] (J.M. Lema).

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TCSAHTNHHCBADBIFLXEE2E1E2

TMPDZPROXCBZSMXERYDCFIBPNPX

0 10 20 30 40 50 60 70 80 90 100Relative presence in liquid and solid phase (%)

Org

anic

mic

rpol

luta

nt

Figure S1. Relative presence of OMPs in the liquid ( ) and solid ( ) phases of urban wastewater. Sorbed concentrations were calculated from the KD values reported in Table 1.

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Figure S2. Mass balance of COD, TN and example of OMP of each type in the HRAS-based STP. B HR: biotransformation in high-rate activated sludge reactor, BPNA: biotransformation in partial nitration-anammox reactor, RTH: removal in thermal hydrolysis, BAD: biotransformation in anaerobic digestion.

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Figure S3. Mass balance of COD, TN and example of OMP of each type in the RBF+HRAS-based STP. BHR: biotransformation in high-rate activated sludge reactor, BPNA: biotransformation in partial nitration-anammox reactor, RTH: removal in thermal hydrolysis, BAD: biotransformation in anaerobic digestion.

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Figure S4. Mass balance of COD, TN and example of OMP of each type in the CEPT-based STP. B PNA: biotransformation in partial nitration-anammox reactor, RTH: removal in thermal hydrolysis, BAD: biotransformation in anaerobic digestion.

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Figure S5. Mass balance of COD, TN and example of OMP of each type in the conventional STP. BAS: biotransformation in activated sludge reactor, BAD: biotransformation in anaerobic digestion.

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SENSTITIVITY ANALYSIS

A global sensitivity analysis was carried out to determine what parameters have a higher

influence on the effluent and sludge OMP content. The method of standardised regression

coefficients (SRC) was chosen, consisting on fitting a first order linear multivariable model

between the predictions and the parameter values (θ i) by a least squares method (Saltelli et

al., 2008):

yk=bk , 0+∑ibk , i · θi (12)

where yk are the content of OMP k in a given stream, bk,0 and bk,i are the linear regression

coefficients and θi the parameters, with index k varying from 1 to the number of OMP and

index i from 1 to the number of parameters. To assume the model linear, the squared

coefficient of correlation (R2) between the Monte Carlo simulation output (Y) and the values

produced with the regression model with the estimated SRC (Eq. 9) regressed linear output

should be above 0.7 (Vangsgaard et al., 2012), which was confirmed for all the cases

analysed. After standardisation of the outputs and parameters, the absolute magnitude of

the regression coefficients indicates the sensitivity of the outputs to a given parameter and,

therefore, can be used to rank the parameters with a higher influence on the predictions.

Only those parameters with an expected influence larger than 5% were retained for further

analysis.

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Table S1. Factors with an influence larger than 5% on the OMP removal efficiency from wastewater.

OMP Wastewater treatment plant configurationHRAS RBF+HRAS CEPT Conventional

AHTNkbiol

HRAS/PN-AMX reactorKD HRAS reactor

kbiol HRAS/PN-AMX reactor

KD HRAS reactor

kbiol PN-AMX reactor

kbiol CAS reactor

ADBI kbiol HRAS/PN-AMX reactors

kbiol HRAS/PN-AMX reactors

kbiol PN-AMX reactor

kbiol CAS reactor

HHCBkbiol

HRAS/PN-AMX reactorsKD HRAS reactor

kbiol HRAS/PN-AMX reactors

KD HRAS reactor

kbiol PN-AMX reactor

kbiol CAS reactor

TCSkbiol

HRAS/PN-AMX reactorsKD HRAS reactor

kbiol HRAS/PN-AMX reactors

KD HRAS reactor

kbiol PN-AMX reactor

KD

CAS reactor

DCF kbiol HRAS/PN-AMX reactors

kbiol HRAS/PN-AMX reactors

kbiol PN-AMX reactor

kbiol CAS reactor

IBP kbiol HRAS/PN-AMX reactors

kbiol HRAS/PN-AMX reactors

kbiol PN-AMX reactor

kbiol CAS reactor

NPX kbiol HRAS reactor

kbiol HRAS reactor - kbiol

CAS reactor

ERY kbiol HRAS reactor

kbiol HRAS reactor - kbiol

CAS reactor

ROX kbiol HRAS reactor

kbiol HRAS reactor

kbiol PN-AMX reactor

kbiol CAS reactor

TMP kbiol HRAS/PN-AMX reactors

kbiol HRAS/PN-AMX reactors

kbiol PN-AMX reactor

kbiol CAS reactor

SMX kbiol HRAS/PN-AMX reactors

kbiol HRAS/PN-AMX reactors

kbiol PN-AMX reactor

kbiol CAS reactor

FLX kbiol/KD HRAS reactorKD AD kbiol/KD HRAS reactor KD AD kbiol

CAS reactor

CBZ kbiol HRAS reactor kbiol HRAS reactor KD AD kbiol CAS reactor

DZP kbiol HRAS/PN-AMX reactors

kbiol HRAS/PN-AMX reactors KD AD kbiol

CAS reactor

E1 kbiol HRAS/PN-AMX reactors

kbiol HRAS/PN-AMX reactors

kbiol PN-AMX reactor

kbiol CAS reactor

E2 kbiol HRAS/PN-AMX reactors

kbiol HRAS/PN-AMX reactors

kbiol PN-AMX reactor

kbiol CAS reactor

EE2 kbiol HRAS/PN-AMX reactors

kbiol PN-AMX reactor

kbiol PN-AMX reactor

kbiol CAS reactor

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Table S2. Factors with an influence larger than 5% on the OMP presence in digested sludge.

OMP Wastewater treatment plant configurationHRAS RBF+HRAS CEPT Conventional

AHTN kbiol/KD HRAS reactor - -kbiol

CAS reactorKD AD

ADBI kbiol/KD HRAS reactor kbiol/KD HRAS reactorKD AD KD AD

kbiol/ KD

CAS reactorKD AD

HHCB kbiol/KD HRAS reactor KD HRAS reactor - -TCS kbiol/KD HRAS reactor - - KD CAS reactorDCF - - -

IBP kbiol/KD HRAS reactorKD AD

kbiol/KD HRAS reactorKD AD KD AD KD AD

NPX KD HRAS reactorKD AD KD AD KD AD KD AD

ERY KD HRAS reactorKD AD KD AD KD CEPT

KD AD KD AD

ROX kbiol/KD HRAS reactorKD AD KD AD KD AD

kbiol/ KD

CAS reactorKD AD

TMP KD AD KD AD KD AD KD AD

SMX KD HRAS reactorKD AD KD AD KD CEPT

KD AD KD AD

FLX KD HRAS reactorKD AD KD AD KD AD

kbiol/ KD

CAS reactorKD AD

CBZ KD HRAS reactorKD AD

KD HRAS reactorKD AD

KD CEPTKD AD

KD

CAS reactorKD AD

DZP KD HRAS reactorKD AD KD AD KD CEPT

KD AD

KD

CAS reactorKD AD

E1 kbiol/KD HRAS reactorKD AD KD AD KD AD

kbiol

CAS reactorKD AD

E2 kbiol HRAS reactor KD AD - kbiol

CAS reactorEE2 KD AD KD AD KD AD KD AD