pressurized liquid extraction and orbitrap mass

Faculty of Bioscience Engineering

Academic year 2013 – 2014

Pressurized liquid extraction and Orbitrap mass spectrometry

analysis of pharmaceutical residues in wastewater treatment

plant sludge

Audisny Apristiaramitha Teddy

Promoter : Prof. dr. ir. Kristof Demeestere

Tutor : ir. Leendert Vergeynst

Master’s dissertation submitted in partial fulfillment of the requirements for the

degree of

Master in Environmental Sanitation

i

COPYRIGHTS

The author and the promoter give permission to use this thesis for consultation and to copy parts of it

for personal use. Every other use is subject to copyright laws, more specifically the source must be

extensively specified when using from this thesis.

Gent, August 2014

The Author The Promoter The Tutor

Audisny A Teddy Prof. Dr. ir. Kristof Demeestere ir. Leendert Vergeynst

ii

ACKNOWLEDGEMENT

First of all I would like to express my gratitude to my tutor ir. Leendert Vergeynst who is tirelessly

guide and enlighten me during this work. His willingness to share all knowledge he knows enriches

and broadens my way of thinking. Prof. Dr. ir. Kristof Demeestere, his inputs and insight help me to

always pay attention into detailed. ing. Lies Harinck for her contribution on running the instrumental

analysis during the laboratory experiment. Also to Prof. Dr. ir Herman Van Langenhove for his advice

and the entire EnVOC members for their warmest hospitality after all this time, thank you very much.

I sincerely thank to LPDP, Ministry of Finance, Republic of Indonesia for providing scholarship that

financially ensure the sustainability of education especially for children of the nation who pursue their

study either domestic or abroad.

CEST team, who are willing to help and assist for academic matter during my stay here in Belgium.

My classmates and Indonesian Student Association, of whom I value friendship and companion.

Rindia Maharani Putri, Msc who I can always counting on polishing my knowledge of chemistry.

And lastly, ir. Teddy Ramarga and Henny Sudewanti who are always support me in every stage of life,

unconditionally.

Audisny Apristiaramitha Teddy

Gent, 21st August 2014

iii

TABLE OF CONTENTS

COPYRIGHTS .......................................................................................................................... i

ACKNOWLEDGEMENT ....................................................................................................... ii

TABLE OF CONTENTS ....................................................................................................... iii

LIST OF TABLES .................................................................................................................. vi

LIST OF FIGURES ............................................................................................................... vii

LIST OF ABBREVIATIONS .............................................................................................. viii

ABSTRACT ............................................................................................................................. ix

1 INTRODUCTION ............................................................................................................. 1

2 LITERATURE REVIEW ................................................................................................. 3

2.1 Pharmaceutical residues in the process of wastewater treatment . . . . . . . . . . . . . . . . . . 3

2.1.1 Fate of pharmaceuticals in wastewater treatment plants ..................................................... 3

2.1.2 Sorption as a mechanism of pharmaceutical removal in WWTPs ...................................... 3

2.1.2.1 Hydrophobicity ........................................................................................................................... 4

2.1.2.2 Electrostatic interactions and the effect of pH ........................................................................... 6

2.1.2.3 Temperature effect ..................................................................................................................... 7

2.1.2.4 Sludge characteristics ................................................................................................................. 8

2.1.3 Solid-water partition coefficient Kd as an expression of the sorption equilibrium ............. 8

2.1.4 Relationship between organic-carbon partitioning coefficient (Koc) and solid-water

partitioning (Kd) ............................................................................................................................. 11

2.2 Analysis of pharmaceutical residues in WWTPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.2.1 Analytical process ............................................................................................................. 12

2.2.2 Pressurized liquid extraction (PLE) .................................................................................. 14

2.2.2.1 Temperature .............................................................................................................................. 15

2.2.2.2 Pressure .................................................................................................................................... 16

2.2.2.3 Type of solvent ......................................................................................................................... 17

2.2.2.4 Cycle series and cycle time ...................................................................................................... 18

2.2.2.5 Application of PLE for pharmaceuticals analysis .................................................................... 19

3 OBJECTIVES AND SCOPE OF STUDY ..................................................................... 23

iv

4 MATERIALS AND METHODS .................................................................................... 24

4.1 Materials and chemicals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.2 Sampling of sludge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.3 Dewatering of the sludge: fi ltration and lyophilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

4.4 Pressurized liquid extraction (PLE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.4.1 Initial PLE conditions ........................................................................................................ 26

4.4.2 Conditions for the optimization of the PLE procedure ..................................................... 27

4.4.2.1 Condition in the extraction cells and PLE settings ................................................................... 27

4.4.2.2 Extraction solvent ..................................................................................................................... 28

4.5 Clean-up and pre-concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.5.1 Solid phase extraction (SPE) ............................................................................................. 28

4.5.2 Evaporation ....................................................................................................................... 29

4.6 Liquid chromatography – high resolution mass spectrometry (LC-HRMS). . 29

4.7 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.8 Determination of process efficiency, recovery and matrix effect. . . . . . . . . . . . . . . . 32

4.9 Quality Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.9.1 Relative standard deviation (RSD) on repeated measurements ........................................ 34

4.9.2 Procedure for the determination of pharmaceutical concentrations in sludge .................. 35

5 Results and Discussion .................................................................................................... 36

5.1 Evaluation of the initial PLE method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

5.2 Method optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5.2.1 Experimental design .......................................................................................................... 40

5.2.2 Evaluation of the method optimization ............................................................................. 43

5.2.3 Modification of the solid mixture ...................................................................................... 43

5.2.3.1 Effect of washing the sand with Na2EDTA .............................................................................. 43

5.2.3.2 Effect of Na2EDTA in the extraction cell ................................................................................. 45

5.2.3.3 Effect of NH4Ac in the extraction cell ..................................................................................... 46

5.2.4 PLE settings ....................................................................................................................... 46

5.2.4.1 Temperature .............................................................................................................................. 46

5.2.4.2 The number of cycles ............................................................................................................... 47

5.2.4.3 Extraction time ......................................................................................................................... 48

5.2.5 Extraction solvent composition ......................................................................................... 48

5.2.5.1 Effect of pH .............................................................................................................................. 48

5.2.5.2 Effect of organic solvent composition ...................................................................................... 50

5.2.6 Clean- up and pre-concentration ....................................................................................... 52

5.2.6.1 SPE ........................................................................................................................................... 52

5.2.6.2 Evaporation .............................................................................................................................. 53

v

5.3 Comparison of the initial procedure, procedure F and literature . . . . . . . . . . . . . . . . . 53

5.4 Concentration of pharmaceuticals in the sludge sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

6 Conclusions and Recommendations .............................................................................. 59

6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

6.2 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

References ............................................................................................................................... 61

Appendix ................................................................................................................................. 68

vi

LIST OF TABLES

Table 2.1 Literature data of log Kow for pharmaceutical compounds. ....................................................... 5!

Table 2.2 Literature data of Kd of pharmaceuticals on the secondary long sludge age and the effect of

pH. ............................................................................................................................................................. 7!

Table 2.3 Reported Kd (L/kg SS) values for several pharmaceuticals in WWTPs sludge. ....................... 9!

Table 2.4 Literature data of log Koc of pharmaceuticals in WWTP sludge (Barron et al., 2009). .......... 12!

Table 2.5 The recoveries (%) of pharmaceuticals under varied temperature. ......................................... 16!

Table 2.6 The recoveries (%) of pharmaceuticals under varied pressure (Ding et al., 2011). ................ 17!

Table 2.7 The recoveries (%) under different types of solvent combinations (Ding et al., 2011). ......... 18!

Table 2.8 The recoveries (%) under varied cycles series and time (Ding et al., 2011). .......................... 19!

Table 2.9 Literature data of PLE optimization ........................................................................................ 20!

Table 4.1 List of chemicals. ..................................................................................................................... 25!

Table 4.2 Initial PLE conditions. ............................................................................................................. 27!

Table 4.3 McIlvaine buffer composition at respective pH condition (McIlvaine, 1921). ....................... 28!

Table 4.4 Parameters for ESI positive. .................................................................................................... 30!

Table 4.5 Solvent gradient during separation. ......................................................................................... 30!

Table 5.1 Process efficiency, recovery and matrix effect for 40 compounds in the initial condition. .... 37!

Table 5.2 Parameters for each condition during method optimization. ................................................... 42!

Table 5.3 Process efficiency, matrix effect, and recovery for 40 pharmaceuticals under condition F. ... 55!

Table 5.4 Concentration (!g/kg dry matter) of pharmaceuticals in sludge sample. .............................. 57!

vii

LIST OF FIGURES

Figure 2.1 Interaction involved between pharmaceuticals and sludge a) dipole-dipole interaction b)

electrostatic interaction (Schwarzenbach et al., 2003) .............................................................................. 4!

Figure 2.2 Example of sorption isotherm model a) maprotiline Freudlich sorption isotherm b)

bisoprolol Langmuir sorption isotherm (Hörsing et al., 2011) ................................................................ 11!

Figure 4.1 Schematic workflow for the analysis of pharmaceutical residues in WWTP sludge. ............ 24!

Figure 4.2 Lyophilization vacuum instrument. ........................................................................................ 26!

Figure 4.3 PLE extraction cell (www. dionex.com). ............................................................................... 27!

Figure 4.4 Solid phase extraction process (Lucci et al., 2012). ............................................................... 29!

Figure 4.5 Procedure of pharmaceuticals stock solution. ........................................................................ 31!

Figure 4.6 Spiking procedure. ................................................................................................................. 33!

Figure 4.7 Decision tree of detected pharmaceuticals. ............................................................................ 34!

Figure 5.1 Evaluation of the initial PLE conditions in terms of (a) process efficiency (b) recovery (c)

matrix effect. ............................................................................................................................................ 36!

Figure 5.2 Relationship between initial recovery (RSDRE <30%) and Log Kow ...................................... 39!

Figure 5.3 Schematic diagram of method optimization. .......................................................................... 41!

Figure 5.4 Evaluation procedure for method optimization. ..................................................................... 43!

Figure 5.5 Effect of Na2EDTA washed sand (Ccondition B/Ccondition A). ........................................................ 44!

Figure 5.6 Effect of Na2EDTA washed sand on quinolones. .................................................................. 45!

Figure 5.7 Effect of Na2EDTA (Ccondition D/Ccondition C). ............................................................................. 45!

Figure 5.8 Effect of NH4Ac (Ccondition E /Ccondition D). ................................................................................. 46!

Figure 5.9 The effect of extraction temperature (C80/C100). ..................................................................... 47!

Figure 5.10 The effect of cycles at a total extraction time of 10 minutes (C2cycles/C1cycle). ...................... 47!

Figure 5.11 The effect of extraction time at 2 cycles (C10minutes/C5minutes). ............................................... 48!

Figure 5.12 The effect of acidified solvent at pH 2 (Ccondition C/Ccondition B). .............................................. 49!

Figure 5.13 Effect at pH 3, 4, 5, 6 (CpH/CconditionD). .................................................................................. 50!

Figure 5.14 Ratio of various composition and organic solvent extraction .............................................. 51!

Figure 5.15 Matrix effect with various extraction solvent compositions. ............................................... 51!

Figure 5.16 Process efficiency of condition F + SPE. ............................................................................. 52!

Figure 5.17 Matrix effect of condition F + SPE and condition F + evaporation. .................................... 53!

Figure 5.18 Process efficiency obtained with conditions A and F. ......................................................... 54!

Figure 5.19 Matrix effects obtained with conditions A and F. ................................................................ 54!

viii

LIST OF ABBREVIATIONS

ACN Acetonitrile

ASE Accelerated solvent extraction

EDTA Ethylenediaminetetraacetic acid

ESI Electrospray ionisation

FWHM Full width at half maximum

GC Gas chromatography

HESI Heated electrospray ionisation

HRMS High-resolution mass spectrometry

HRT Hydraulic retention time

LC-MS Liquid chromatography - mass spectrometry

LOD Limit of detection

m/z Mass-to-charge ratio

ME Matrix effect

MeOH Methanol

OECD Organization for Economic Co-operation and Development

PE Process efficiency

PLE Pressurized liquid extraction

PTFE Polytetrafluoroethylene

RE Recovery

RR Response ratio

RSD Relative standard deviation

SFE Supercritical fluid extraction

SPE Solid phase extraction

SRT Sludge retention time

SS Suspended solid

UHPLC Ultra high-performance liquid chromatography

USE Ultrasonic extraction

WWTP Wastewater treatment plant

ix

ABSTRACT

Presented is a study towards the occurrence of 40 pharmaceutical residues in sludge of wastewater

treatment plants (WWTP). The analytical method is based on the development of pressurized liquid

extraction (PLE) followed by instrumental analysis via liquid chromatography-Orbitrap mass

spectrometry (Orbitrap LC-MS). Different conditions of the PLE method, including the modification

of the solid mixture, PLE settings, and extraction solvent were optimized to increase the recovery on

one hand and simultaneously reduce matrix effects. Overall, modifications such as washing the sand

with Na2EDTA, changing the pH and the composition of the extraction solvent have showed an

important role for an increased in extraction efficiency. Solid phase extraction (SPE) and evaporation

as a post-extraction of following PLE has proved to magnify the matrix effects leading to stronger

signal suppression. Application of the developed analytical method on sludge of the WWTP of Aalst,

Belgium, revealed concentrations ranging from 1.3 – 2.5 x 102 µg/kg dry matter with low uncertainty

( < 20%) on the process efficiency. This study encloses the concentration of amantadine and evafirenz,

which can be considered as the first quantification of antiviral drugs in WWTP sludge.

Keywords : pharmaceuticals, pressurized liquid extraction, Orbitrap mass spectrometry, wastewater

treatment, sludge.

1

1 INTRODUCTION

The demand of pharmaceuticals is rising in order to overcome health problems that continuously

evolve. Tons of medicines are produced for human and animal consumption worldwide (Fent et al.,

2006; Glassmeyer et al., 2009). Pharmaceuticals consumption by humans covers for several

purposes such as for diagnosis, treatment, and prevention of illness (Dıaz-Cruz et al., 2003). The

OECD (Organization for Economic Co-operation and Development) has reported an increase in

consumption of antidepressants and antidiabetics in most of European countries by approximately

twice higher in 2010 compared to the consumption in 2000 (OECD, 2012). A greater consumption

is however found in veterinary pharmaceuticals administered for preventing illness, as growth

promoter and as parasiticides (Dıaz-Cruz et al., 2003). In addition, these veterinary pharmaceuticals

are usually applied for fish farming and livestock-breeding (Halling-Sørensen et al., 1998).

Apart from their benefits, there has been occurred awareness and concern about the environmental

presence of pharmaceutical residues. These are delivered through several possible sources such as

household disposal, hospital wastewater, and effluent of pharmaceutical production facilities

(Larsson et al., 2007; Li et al., 2008; Lin and Tsai, 2009; Santos et al., 2009). Pharmaceuticals

mostly are discarded via (un)altered excretion in urine and feces, either with or without metabolism,

and finally enter the sewage system (Yamamoto et al., 2009). Other than that, pharmaceuticals can

also enter other environmental compartments, such as by the livestock manure application in

agricultural activity (Santos et al., 2009).

Pharmaceuticals that enter the sewage system from various sources are finally treated in the

wastewater treatment plants (WWTPs). Nevertheless, WWTPs are generally not designed to

remove often biorecalcitrant pharmaceutical micropollutants as they only use conventional

activated sludge system that mainly remove biodegradable carbon, phosphorus, nitrogen and

microbiological organisms (Jelic et al., 2011; Verlicchi et al., 2012). As a result, some of the

advanced treatment steps such as ozonation, granulated activated carbon, and advanced oxidation

are introduced to improve the pharmaceuticals removal (Fatta-Kassinos et al., 2011; Fent et al.,

2006; Larsen et al., 2004).

Since only a few WWTPs use this tertiary treatment step, pharmaceuticals are still frequently

discovered both in the effluent and surface water. Generally, pharmaceutical compounds are found

in about 50 to 100 % over all conducted measurements in the aquatic environment (Miège et al.,

2008) which indicates low removal efficiency in WWTPs. Therefore, WWTPs are considered as a

major pathway through which pharmaceuticals enter the environment (Joss et al., 2005).

2

Beside the effluent as an output of WWTPs, sludge is another one, which possibly contains the

pharmaceutical residues as a result of solid-liquid partitioning (Jelic et al., 2011; Li et al., 2013;

Yang et al., 2012). A considerable concentration was found in the sludge of WWTPs particularly

for antibiotics, antidepressants, and anti-inflammatory (Baker & Kasprzyk-Hordern, 2011; Barron

et al., 2008; Dorival-García et al., 2013; Golet et al., 2002; Vazquez-Roig et al., 2010). Hence, this

can illustrate an important indication of pollution level.

Although the risk of pharmaceutical residues to humans is not clearly stated, toxicity assessment on

different trophic levels of aquatic organisms can be taken as a potential environmental risk (Fent et

al., 2006). A continuous deliverance of pharmaceuticals at low concentrations could affect on an

increased toxicity even without high persistence unlike other pollutants such as pesticides,

detergents, and fuels (Dorne et al., 2007; Glassmeyer et al., 2009; Halling-Sørensen et al., 1998).

The occurrence of pharmaceutical residues both in effluents and on sludge becomes critical, since

they can be reused for many purposes or directly released to the environment. In certain countries,

the excess sludge can be used for biogas production, soil amendment in agriculture, or it is removed

by incineration and disposed in landfills, while the effluents are often discharged to the river or

seawater (Fatta-Kassinos et al., 2011; Jelic et al., 2011). Kelessidis and Stasinakis (2012) have

reported that on average 41% of sewage sludge was reused for agricultural purposes in 27 European

countries, which becomes a problem if it contains considerable pharmaceuticals concentration.

Therefore, quantitative analysis of pharmaceutical residues on the sludge is essential, rather than

only focusing to the analysis of effluent and influent.

A variety of data are provided from the literature related to the behavior of pharmaceuticals in

sludge. Therefore, the chapter of the literature review is intended to represent these data, which are

based on the literature of experimental studies. This chapter also comprises an in-depth discussion

on the fate, the sorption mechanism and several parameters that influence the removal of

pharmaceuticals during the process of wastewater treatment. In addition, the analytical process in

order to quantify pharmaceutical residues in sludge will be elaborated in this chapter.

A scattered and limited study on the analytical development and optimization in the literature

becomes a basic reason of why this study is conducted. Moreover, this study is aimed to identify the

main parameters affecting the analytical performance and to achieve the desired evaluation

parameter regarding to the determination of pharmaceutical residues in the sludge of the WWTP, as

more explicitly described in the chapter of the scope and objectives of this study. The experimental

strategy will be discussed in the chapter of material and methods. The results of the experiments are

presented, interpreted and discussed in the chapter of results and discussion. The conclusion will be

stated to wrap up the overall results and followed with the last section of recommendations for

further study.

3

2 LITERATURE REVIEW

2.1 Pharmaceutical residues in the process of wastewater treatment

2.1.1 Fate of pharmaceuticals in wastewater treatment plants

WWTPs commonly implement a process on the basis of a biological treatment method. Activated

sludge was formed as a result of the bacteria activity, which is responsible for pollutants removal.

Despite of many activated sludge process configurations have been developed and integrated, the

basic design is still generally applied in many WWTPs (Gernaey et al., 2004). As such, three main

processes that are often used are (i) primary clarifying, (ii) carbon/nutrient/phosphorus removal, and

(iii) secondary settling (Joss et al., 2005). Some micropollutants including pharmaceuticals are

partially removed by degradation together with the nutrient/carbon removal as a consequence of

nitrifying bacteria activity (Fernandez-Fontaina et al., 2012). Moreover, sorption on the sludge

occurs and is governed by the temperature, pH, ionic strength and presence of complexing agents

(Stasinakis, 2012). In principle, pharmaceuticals can also be removed by hydrolysis and

volatilization, however Li and Zhang (2010) have not found any striking effect of these processes,

while sorption and degradation are considered as major factors of pharmaceuticals removal during

the activated sludge process.

2.1.2 Sorption as a mechanism of pharmaceutical removal in WWTPs

Sorption is an important and major process during the removal of pharmaceuticals, which cause the

less mobility in the solid phase (Yu et al., 2013). Sorption can be distinguished on the basis of

interaction into absorption and adsorption. Hydrophobic interactions are mainly occurred during the

process of absorption, affected by the interaction between e.g. aliphatic/aromatic groups of the

pharmaceuticals with the lipophilic cell membrane of the sludge. Meanwhile, electrostatic

interactions often involved in the process of adsorption. The interaction is between charged

pharmaceuticals and the surface charge of the micro-organisms (Ternes et al., 2004). Several

interactions such as Van der Waals and electrostatic interactions are involved during the sorption

process (Golet et al., 2002; Ternes et al., 2004). Van der waals interactions are weak intermolecular

interactions, regardless the chemical structure of these molecule might have (Lide, 1913;

Schwarzenbach et al., 2003).

4

One of the Van der Waals interactions is the dipole-dipole attraction (Figure 2.1 a), created by the

interaction between permanent dipoles from each molecule in such a way that forms head-tail shape.

The polarizability of a particular compound is related to the uneveness of electron distribution

which results into intermolecular attraction. On the other hand, electrostatic interactions (Figure 2.1

b) occur when there is a strong intermolecular interaction between permanent electron donors and

acceptors (Schwarzenbach et al., 2003).

Figure 2.1 Interaction involved between pharmaceuticals and sludge a) dipole-

dipole interaction b) electrostatic interaction (Schwarzenbach et al . , 2003)

Sorption of pharmaceuticals onto sludge can be influenced from several factors such as

hydrophobicity, which is expressed by the octanol-water partitioning coefficient (Kow). Abiotic

conditions such as temperature and pH, the structure of the compounds, or the characteristics of the

sludge give different impacts on the solid-water partitioning.

2.1.2.1 Hydrophobicity

The hydrophobicity of a compound can be expressed as the octanol-water partitioning coefficient

(Kow). Kow is defined as the ratio of the concentration of a compound in the octanol phase over its

concentration in a water phase at equilibrium (Equation 1). Kow values for several pharmaceuticals

are presented in Table 2.1. Ibuprofen has a log Kow of 4.5, which indicates high hydrophobicity that

in general can be associated with higher concentration on the solid phase. As such, Martín et al.

(2012) proved its concentration ranging from 687 to 2988 µg/kg dry matter which is considered as

high concentration. On the other hand, amoxiciline and paracetamol have low Kow values (0.33;

0.51) or less hydrophobicity, thus less sorption can be expected. This fact reinforced with their

solid-water partitioning (Log Kd) in the sludge with respectively 0.025 and -0.4. The details on

solid-water partitioning will be further discussed in section 2.1.3. Nevertheless, hydrophobicity not

really suits to fully describe the sorption behavior, since pharmaceuticals are mostly polar or ionic

compounds (Ternes et al., 2004).

a) b)

5

Kow: Partition coefficient octanol-water phase (-)

Cow: Concentration of compound in octanol phase (!g/L)

Cw: Concentration in water phase (!g/L)

Table 2.1 Literature data of log Kow for pharmaceutical compounds.

Compounds Log Kow

Acyclovir -1.59a

Alprazolam 2.15a

Amantadine 2.44b

Amitriptyline 4.92b

Amoxicilline 0.33f

Carbamazepine 2.4c

Chloramphenicol 1.14a

Ciprofloxacin 0.28a

Diazepam 2.8e

Diclofenac 4.8c

Efavirenz 4.6a

Enrofloxacine 1.1d

Flumequine 1.7d

Fluoxetine 4.05a

Gatifloxacine 2.6a

Ibuprofen 4.5c

Indomethacine 4.27a

Lamivudine -1.4a

Levofloxacine 2.1a

Metronidazole -0.02a

Moxifloxacine 2.9a

Naproxen 3.2c

Nevirapine 2.5g

Oseltamivir acid -2.1g

Oseltamivir ethylester 0.36g

Oxytetracycline -1.3e

Paracetamol 0.51f

Paroxetine 3.6a

Kow=Cow

Cw

(Equation 1)

6

Compounds Log Kow

Rimantadine 3.6a

Risperidon 2.5a

Sarafloxacine 0.84h

Sulfadoxine 0.7a

Sulfamethazine 0.89d

Sulfamethoxazole 0.9c

Temazepam 2.19a

Tetracycline -1.2e

Trimethoprim 0.91f

Venlafaxine 2.74a

Zidovudine -0.1g

a) Drugbank, 2013 b) Logkow.cisti.nrc.ca, 2013 c) Martín et al., 2012 d) Tolls, 2001

e) Vazquez-Roig et al., 2010 f) Williams et al., 2009 g) Prasse et al., 2010 h) Li et al., 2013

2.1.2.2 Electrostatic interactions and the effect of pH

Electrostatic interactions are the other sorption mechanism besides hydrophobicity. Charged

functional groups, due to protonation or deprotonation, can interact with charges on the sludge

surface. Therefore, the pH of the matrix will affect the partitioning of compounds respective to their

pKa value. Sludge is usually present in negative charge, thus a compound with a high pKa value is

more likely present in a positive charge at ambient pH condition. As a result, sorption of mainly

positively charged organic compounds on the sludge is happening (da Silva et al., 2011). According

to Zhou et al. (2013), maximal adsorption of fluoroquinolones was reached at the neutral pH (pH 6-

8) and a lower percentage of adsorption at acid or basic pH was observed. The level of adsorption is

fluctuated over the pH variation as a consequence of three different forms of fluoroquinolones:

negative, positive and zwitterion. Ciprofloxacin as one of the fluoroquinolones has three forms in

the solution: cationic, anionic and zwitterionic. Cationic form dominated in acidic condition, while

in alkaline condition anionic form most likely present. The zwitterionic is present at the neutral pH

(6-8), at which maximum sorption is happening.

Horsing et al. (2011) have done an experiment to investigate the impact of pH variation on the Kd

value of different pharmaceuticals, as compiled in the Table 2.2. Fluoxetine, that has a high pKa,

averagely has a high Kd at pH 6, 7 and 8 because at these conditions, fluoxetine still has positive or

neutral charge and thus easily sorbs onto the sludge.

7

Table 2.2 Literature data of Kd of pharmaceuticals on the secondary long sludge

age and the effect of pH.

Compounds pKa Average Kd (L/kg)

pH 6 pH 7 pH 8

Diclofenac 4.15a (8 ± 0.6) x 10

2 (48 ± 8.1) x 10

3 (1.2 ± 0.6) x 10

3

Fluoxetine 10.1b (6.1± 0.4) x 10

3 (3.7±0.7) x 10

3 (8.7 ±1.6) x 10

3

Risperidone 3.11;

8.24c

(6.5 ± 0.6) x 102 (4.2 ± 0.4) x 10

2 (6.2 ± 0.3) x 10

2

Sulfamethoxazole 2.65;

6.75d

(3 ± 2) x 102 (3 ± 1) x 10

2 (2.7 ± 0.8) x 10

2

Trimethoprim 7.12a (4.3 ± 0.3) x 10

2 (35 ±0.9) x 10 (2.8 ± 0.2) x 10

2

a) drugbank, 2013

b) Nakamura et al., 2008

c) www.drugs.com, 2013 d) Tolls, 2001

2.1.2.3 Temperature effect

The partitioning of compounds between a solid and an aqueous phase is more favorable when the

free Gibbs energy reaches negative value (Ten Hulscher and Cornelissen, 1996; Von Eopen et al.,

1991). This free Gibss energy value is depending on enthalpy, entropy and temperature as shown in

Equation 2.

!G: Change of free Gibbs energy (J)

!H: Change of enthalpy (J)

T: Temperature (K)

!S: Change of entropy (J/K)

Based on Equation 2, the Van’t Hoff Equation (Equation 3) is derived, which explains the change in

equilibrium when the temperature changes (Atkins and de Paula, 2006).

lnK2

K1

!

"#

$

%&=

'(H !

R

1

T2

'1

T1

!

"#

$

%&

K1: Initial partition coefficient (-)

K2:: Final partition coefficient (-)

!H: Enthalphy change of a compound (J/mol)

T1: Initial Temperature (K)

T2: Final Temperature (K)

R: Rydberg constant (J/K. mol)

!G = !H "T!S (Equation 2)

(Equation 3)

8

The polarity of compounds may influence the degree of enthalpy and entropy. For example, an

apolar compound has a high entropy change as it leaves the aqueous phase and then absorbs onto

the solid phase. On the other hand, a more polar chemical has less entropy changes since it mainly

involves electrostatic interaction with the solid surface. Thus, instead of entropy changes, polar

chemicals are generating a greater enthalpy change (Delle, 2001; Von Eopen et al., 1991). This

event can be correlated to Equation 2, where the temperature play roles in determining the total free

Gibbs energy that eventually represents the spontaneity of the reaction.

Temperature can also be related to the compound density in the aqueous phase. When the

temperature is elevated, the density of a compound is increased, and hence higher sorption onto the

solid phase is obtained (Delle, 2001). Zhou et al. (2013) have reported the effect of temperature on

the adsorption of fluoroquinolones. The data show that adsorption spontaneously occurs at low

temperature.

2.1.2.4 Sludge characteristics

Sludge characteristics also contribute in determining the solid-water partitioning. Surface charges

are different among different types of sludge, which makes a different level of interaction with

charged organic compounds. As reported by Horsing et al. (2011), a large different degree of

sorption was found on primary versus secondary sludge. Sludge characteristics are also influenced

by the redox potential. The condition during the wastewater treatment process may contribute in

determining the sludge characteristics. The aerobic condition in the process of nitrification provides

a higher oxidation potential, which allows the compounds to degrade, thus less sorption will be

expected (Suarez et al., 2010). However, Xue et al. (2010) has tested the degree of pharmaceutical

adsorption and the influence of different treatment conditions, and it showed that in the anaerobic

tank the adsorption runs more rapid.

2.1.3 Solid-water partition coefficient Kd as an expression of the sorption

equilibrium

In order to describe the partitioning of a compound between the water phase and the solid phase, a

Kd value (solid-water partition coefficient) is determined. Kd (Equation 4) is defined as the ratio of

the concentration of the sorbed analyte over the concentration in the surrounding water phase at

equilibrium (Martín et al., 2012). The Kd includes two sorption mechanisms: absorption and

adsorption.

Kd=Cs

Cw

(Equation 4)

9

Kd: solid-water partition coefficient (L/kg suspended solid (SS))

Cs: Concentration of the compound on the solid phase (µg/ kg SS)

Cw: Concentration of the compound in the water phase (µg/L)

Table 2.3 summarizes experimentally determined Kd values of some pharmaceutical compounds.

According to the data, ciprofloxacin and tetracycline have the highest partitioning toward the solid

phase among the rest of the compounds due to their high Kd values. Referring to section 2.1.2.1,

ciprofloxacine and oxytetracycline both have low Kow with respectively, 0.28 and -1.3. The Kd of

oxytetracycline is also low, of which implies that the hydrophobicity is proportional with the solid-

water partitioning. However, the Kd of ciprofloxacine is not as comparably low as its Kow. It could

be said that for ciprofloxacine, hydrophobicity is not mainly governed the process of solid-water

partitioning, another mechanism such as electrostatic interaction might be happened.

Table 2.3 Reported Kd (L/kg SS) values for several pharmaceuticals in WWTPs

sludge.

Pharmaceutical compounds Log Kd

Amitriptyline 2.86c

Amoxicillin 0.025c

Carbamazepine 1.4b

Ciprofloxacine 4.3d

Diazepam 1.3e

Diclofenac 1.26-2.18a

Fluoxetine 0.7g

Ibuprofen 2.66g

Indomethacine 1.45c

Naproxen 1.03-1.71a

Oxytetracycline -1.70g

Paracetamol -0.4c

Risperidone 2.73-2.98f

Sulfamethazine 1.3-2.04c

Sulfamethoxazole 0.77-1.79a

Tetracycline 3.90h

Trimethoprim 1.41e

a) Carballa et al., 2008

b) Barron et al., 2009

c) Pomiès et al., 2013

d) Golet et al., 2002

e) Ternes et al., 2004

f) Stevens-Garmon et al., 2011

g) Jones et al., 2002 h) Kim et al., 2005

10

Kd values are experimentally determined based on sorption isotherms. The principle of a sorption

isotherm is by conducting an experiment at a constant temperature and allows the sorption of

chemical target onto the solid phase (sludge) at various concentrations in the water phase. In order

to describe the sorption equilibrium of a compound, Freundlich (Equation 5) and Langmuir

(Equation 6) equations are used. The Freundlich isotherm (Figure 2.2 a) is using an assumption that

sorption runs on a heterogeneous surface with a non-linear sorption behaviour (Yu et al., 2013). If a

given sorption isotherm data do not fit with Freundlich model, then Langmuir isotherm (Figure 2.2

b) model may be used by assume that there are limited number of saturated sorption site

(Schwarzenbach et al., 2003; Tolls, 2001). Each compound individually responds towards the best

correlation with either the Freundlich or Langmuir equation. The 1st order of Freundlich (Equation 5,

n = 1) gives the linear equation as it is shown in Equation 4 (Hörsing et al., 2011).

where

Kf: Freundlich constant (!g1-1/n

. cm3/n

/g)

Cs: Concentration sorbed onto the suspended solid (!g/g SS)

Cw: Concentration in the water (!g/cm3)

n: Freundlich exponent (-)

! : Total number of surface sites per mass of suspended solid (!g /g SS)

Kl: Langmuir coefficient (cm3/!g)

Cs = K fCw

1

n

Cs=!max

!Kl!C

w

1+Kl!C

w

(Equation 5)

(Equation 6)

a)

11

Figure 2.2 Example of sorption isotherm model a) maprotil ine Freudlich sorption

isotherm b) bisoprolol Langmuir sorption isotherm (Hörsing et al . , 2011)

2.1.4 Relationship between organic-carbon partitioning coefficient (Koc) and

solid-water partitioning (Kd)

Sorption of organic compounds onto the organic matter is thermodynamically favorable. Hence,

solid-water partitioning increases linearly to the increase of organic matter. The total mass of

organic matter (Mom) is consisting of carbon, oxygen and nitrogen; and the fraction of carbon is still

dominating the total mixture (equation 7). Thus, to express a relationship between organic matter

and solid-water partitioning, the terms of organic carbon fraction (foc) and organic carbon

partitioning coefficient (Koc) are often used (equation 8). Koc is able to evaluate the ability of

organic compounds (in this case pharmaceuticals) to be sorbed onto the organic carbon

(Schwarzenbach et al., 2003). A high value of Koc generally means that the compound will tend to

adsorb onto organic matter (http://ec.europa.eu/environment/, 2014).

However, the value of Koc is only an estimation guide to interpret the sorption behavior regardless

cation exchange, bridging, hydrogen bonding, and the polarity of the functional groups (Barron et

al., 2009). The literature data of Koc is compiled in Table 2.4.

Moc = foc !Mom

Koc =Kd

foc=Coc

Cw

where

Moc: Mass of organic carbon (kg)

Mom: Total mass of organic matter (kg)

foc: Fraction of organic carbon (kgoc/kgom)

Koc: Organic carbon partition coefficient (L/kgoc)

(Equation 7)

(Equation 8)

b)

12

Kd: Solid-water partitioning coefficient (L/kgom)

Coc: Concentration of compound in organic carbon (!g/kgoc)

Cw: Concentration of compound in water phase (!g/L)

Koc has a strong relationship with Kow. Therefore, Kow is widely used as a parameter to determine

Koc (Stevens-Garmon et al., 2011). The equation that explains the relationship between Kow and Koc

is written as follows:

where a and b are constants calculated from the experiment data.

Table 2.4 Literature data of log Koc of pharmaceuticals in WWTP sludge (Barron et

al . , 2009).

Compounds Log Koc

Amitriptyline 3.53

Carbamazepine 2.14

Diazepam 2.91*

Diclofenac 2.53

Indometacine 2.84

Naproxen 2.06

Paracetamol 1.79

Sulfamethazine 1.69

Sulfamethoxazole 1.54

Temazepam 2.84*

Trimethoprim 2.35

*) Log Koc in agricultural soil

2.2 Analysis of pharmaceutical residues in WWTPs

2.2.1 Analytical process

The analysis of pharmaceuticals in environmental matrices is commonly conducted in several steps

starting from sampling, followed by sample pre-treatment/pre-concentration, and finally

pharmaceuticals separation and quantification by the instrumental analysis.

Sampling is performed in various ways depending on the type of matrix and the analysis purposes.

Vergeynst et al, (2014) conducted a sampling campaign by collecting WWTP influent and effluent

using an automatic sampler.

LogKoc = a logKow + b(Equation 9)

13

Sample pre-treatment is a critical step, at which analyte pre-concentration and sample clean-up

takes place. Solid phase extraction (SPE) is a common technique to remove interferences and to

concentrate the analytes in the sample (Vergeynst et al., 2014).

GC (gas chromatography) and LC (liquid chromatography) are commonly use nowadays as

analytical separation techniques to determine micropollutants such as pharmaceuticals at

environmental residue concentrations. The advantages of GC-based techniques are their high

chromatographic resolution and selectivity (Cochran, 2002; Hada et al., 2000). However, LC is

often preferred for the separation of polar and thermo unstable compounds and it also has a shorter

analysis time as compared to GC.

For the sensitive and selective detection of the compounds in a complex matrix such as wastewater

or sludge, tandem-MS (mass spectrometry) is often used in combination with either GC or LC

(Fatta et al., 2007; Petrovic and Barceló, 2007).

Different approaches have been used to sample sludge by either retrieving the sludge sampled

directly from the primary settler (Radjenovi" et al., 2009), from the aeration tank (Senta et al.,

2013), or post-treated sludge (dry sludge) (Chen et al., 2013). The main difference of sludge and

water analysis lies on the sample pre-treatment, where solid-liquid extraction is necessary for

sludge samples prior to instrumental analysis. Like in water analysis, pre-concentration is often

applied such as SPE to increase the concentration of the analytes in the extracts and to purify the

extract from interferences.

In the past years, several techniques have been developed and implemented to extract various kinds

of solid samples, including WWTPs sludge. A traditional extraction technique such as Soxhlet was

introduced. However, due to large time and solvent consumption, this type of extraction is no

longer of preference (Petrovic et al., 1998; Shen and Shao, 2005).

Supercritical fluid extraction (SFE) is another solid-liquid extraction technique, which works by

using a supercritical substance to facilitate extraction of organic compounds from solid samples.

SFE works at a high temperature and pressure to allow the solvent reach the area above its critical

point, thus it is called as a supercritical fluid. Polar organic modifiers such as methanol are

frequently used to increase the polarity of the supercritical substance (e.g carbon dioxide), hence

increasing the extraction efficiency (Dean, 1998; Richter et al., 1996).

Ultrasonic extraction (USE), on the other hand, uses ultrasonic vibrations to have a closer contact

between the solvent and the sample. The sonic horn will be put together with the solvent and the

sample. Then, afterwards further clean up is usually required prior to the analysis. On top of that,

those two techniques (SFE and USE) have some limitations such as not the automatable and labor

intensive character in case of USE, and the matrix dependent method development and limited

applicability for SFE (Dean, 1998).

14

A recent technique, pressurized liquid extraction (PLE), has been developed with the aim to

overcome the other extraction technique’s limitations (Wells, 2003) and it grows to be one of the

most appealing techniques for solid-liquid extraction. As such, the solvent used in PLE does not

need to reach above its critical point like in SFE. Thus, the solvents used for conventional

extraction are possible to be used in PLE. In addition, numerous research has been conducted in

extracting samples such as medicinal plants (Benthin et al., 1999), food samples (Carabias-Martínez

et al., 2005), or sediments (Petrovic et al., 2002).

2.2.2 Pressurized liquid extraction (PLE)

PLE or by the trade name accelerated solvent extraction (ASE) is a solid-liquid extraction method

that uses organic solvent at high pressure and temperature. Various environmental and biological

samples such as soil, sludge or biota that represent a specific ecosystem have recently been

extracted by the PLE method (Benthin et al., 1999; O’Connor et al., 2007 ).

The principle of PLE is referred to the general chemical equilibrium. The equation of the extraction

partition coefficient at equilibrium is expressed as follows (Equation 10):

Xa: Concentration in solid (µg/kg SS)

Xb: Concentration in extraction solvent (µg/L)

K: Partitioning coefficient (L/kg SS)

From this, it can be concluded that the lower K the better the extraction. Thus, in order to achieve

better efficiency, the parameters that influence the K value need to be optimized such as

temperature, type of solvent, and extraction time.

A PLE apparatus consists of several parts such as a pump, extraction cells, oven, solvent reservoirs,

nitrogen, and collection vials (Figure 2.1). The extraction cell moves into the heating oven and then

the solvent is delivered by the pump, while the temperature start to elevates. The extraction cell is

where the sample is placed together with other solid modifiers and dispersants if necessary. It is

made of stainless steel, thus it can resist a high temperature. In addition, it is also resistant to low

concentrations of mineral acids and strong bases. Both caps at the top and the bottom side are able

to cover the cell tightly, which prevents sample leaking (www.dionex.com). During the process, if

the pressure was exceeds the preset value, the static valve opens and releases the pressure.

Subsequently, the solvent is delivered and if needed, multiple cycles can be performed by

introducing each time fresh solvent. As such, the extraction efficiency can be improved. At the end

of the extraction process, after the extract is collected in the vials, nitrogen gas is flowed in order to

purge the residual solvent (Wells, 2003).

K =Xa

Xb

(Equation 10)

15

Figure 2.1 Schematic diagram of a PLE system (adapted from Wells, 2003).

One advantage of having PLE as an extraction method for extracting solid samples is its short

extraction time and the possibility of automation. Approximately, 15-30 minutes are needed to

extract the content of one extraction cell, which is considered as short time if it is compared to

Soxhlet that might need a couple of hours (Richter et al., 1996). Moreover, PLE can reduce the

consumption of solvent that will gain both an environmental and economic benefit. Therefore, PLE

is more efficient than Soxhlet that is developed prior to PLE for extracting solid samples (Nieto et

al., 2007). During PLE, there are some factors that influence the extraction efficiency. Those are

temperature, pressure, extraction solvent, number of cycles and extraction time. These factors will

be further discussed in 2.2.2.1 until 2.2.2.4.

2.2.2.1 Temperature

Temperature is an important factor for extraction efficiency. When it increases, the solvent will

disturb the interactions involved between the analyte and the matrix such as Van der Waals

interactions, electrostatic interactions or hydrogen bonding (Dean, 1998). The process runs until it

reaches equilibrium, giving a new partitioning as when a high temperature is applied. The change of

the equilibrium constant in function of temperature is expressed by the Van’t Hoff equation, as

showed in Equation 3.

The viscosity will decrease at higher temperature allowing the solvent to penetrate more easily into

the matrix, resulting into a faster diffusion. Consequently, both the mass transfer rate and the

capacity of the solvent to solubilize analytes will increase and, hence, give great impact in

increasing extraction efficiency (Dean, 1998).

16

Several studies (Ding et al., 2011; García-Galán et al., 2013; Golet et al., 2002; Jeli" et al., 2009

Petrovi" et al., 2009; Vazquez-Roig et al., 2010) have investigated the effect of temperature on the

extraction recovery for several pharmaceutical compounds from a sewage sludge matrix. The

results of the recoveries at various temperatures are shown in Table 2.5.

Generally, the recovery increases when a higher temperature is applied, except for paracetamol and

sulfadoxin that have a fluctuating recovery when the temperature elevated. Too high temperature

could also induce thermal degradation for some compounds. As an example, sulfamethoxazole

experienced a loss for its amount until 95% during the extraction at 200oC (Gobel et al., 2005).

Table 2.5 The recoveries (%) of pharmaceuticals under varied temperature.

Compounds Temperature (°C)

50 55 70 75 100

Carbamazepinea

n.d. 88 n.d. 91 90

Ciprofloxacinec 20 n.d. 25 n.d. 35

Diazepamc 40 n.d. 58 n.d. 72

Diclofenacc 25 n.d. 30 n.d. 44

Ibuprofenc 17 n.d. 28 n.d. 30

Oxytetracyclinea

n.d. 54 n.d. 66 65

Paracetamola

n.d. 82 n.d. 72 83

Sulfadoxinb 128 n.d. n.d. 93 160

Sulfamethazinea

n.d. 77

n.d. 86

91

Sulfamethoxazole

a

n.d. 62

n.d. 64

68

Tetracycline

c

57 n.d. 60 n.d. 63

Trimethropimc 3 n.d. 58 n.d. 55

n.d. : not determined

a Ding et al., 2011

b García-Galán et al., 2013

c Vazquez-Roig et al., 2010

2.2.2.2 Pressure

The pressure applied should be sufficiently high in order to allow the solvent to have a temperature

below its boiling point. As temperature elevates during the process of extraction, pressure must be

maintained to keep the solvent stays as a liquid. Moreover, the pressure also needs to be able to

facilitate the solvent to reach the analytes in the matrix pores.

17

Therefore, using high pressure allows the solvent to better penetrate into the matrix, in the area that

normal atmospheric pressure could not do. The exerted pressure also causes air bubbles to dissolve

which will increase the contact between solvent and analytes (Richter et al., 1996; Wells, 2003).

In addition, once the pressure is high enough to achieve the above-mentioned conditions, variation

in the pressure will have very little impact on the analyte recovery, and is therefore considered as

not critical (Thermo scientific, 2013). The effect of pressure on the recovery of some

pharmaceutical compounds from sewage sludge samples is shown in Table 2.6.

Table 2.6 The recoveries (%) of pharmaceuticals under varied pressure (Ding et al . ,

2011).

Compounds Pressure (bar)

55 80 100 130

Carbamazepine

89 86 92 82

Chlortetracycline

54

64

57

48

Oxytetracycline

35 28 38 35

Paracetamol

78 73 96 91

Sulfamethazine

89

78

92

81

Sulfamethoxazole

70

65

71

67

Tetracycline

52 41 62 44

2.2.2.3 Type of solvent

The type of solvent used to extract the analytes can also influence the extraction efficiency. In order

to able to extract as much as possible the target compounds, the polarity of the solvent should be

close to the polarity of the target compounds (Jelic et al., 2009). Since a broad range of compound

classes must be extracted, mixing solvents that have different polarity can be a solution to obtain a

higher extraction efficiency (Barron et al., 2008).

Other considerations in selecting solvents are its compatibility with the post extraction technique,

and the cost of solvent. Ding et al. (2011) tested different combinations of solvent mixtures to

investigate the recovery of pharmaceutical compounds. According to Table 2.7, a higher portion of

the organic solvent (methanol or acetonitrile) is generally giving higher recovery. Higher portion of

organic solvent could lowering the total polarity of the solvent , thus it is more effective to extract

less polar organic compounds, however the efficiency is still depending on the chemical properties

of compounds.

Furthermore, it was observed that solvents that work well in conventional extraction techniques

generally also work in PLE. Thus, general solvent extraction such as water and buffered aqueous

mixtures can be used in PLE (Dionex, 2013).

18

Adding some acid solution into the solvent mixture could be another alternative to increase the

recovery for certain compounds such as diclofenac and paracetamol (Nieto et al., 2007). By adding

acid into the solvent mixture, it can protonates the acidic functional group of organic content in the

sludge, and hence reducing electrostatic interaction between the sludge and the cation site of

pharmaceutical (Ding et al., 2011).

Table 2.7 The recoveries (%) under different types of solvent combinations (Ding

et al . , 2011).

Compounds

Acetonitrile:water Methanol:water

7:3 7:3 5:5 3:7 7:3 5:5 3:7

Carbamazepine

83

84

57

32

52

31

25

Chlortetracycline

52

30

36

20

47

35

25

Oxytetracycline

54

14

34

17

42

35

19

Sulfamethazine

96

94

56

40

77

48

37

Sulfamethoxazole

72

91

63

34

69

59

43

Tetracycline

70

45

35

19

41

24

20

2.2.2.4 Cycle series and cycle time

The cycle series also determines the efficiency of extraction by introducing fresh solvent during the

process (Dionex, 2013). A new equilibrium between the solvent and the matrix occurs in every new

additional cycle, giving a new driving force for the analytes to be extracted. A longer cycle time

may enhance the process of diffusion of the analytes (Jelic et al., 2009). Table 2.8 presents the

effect of the number of cycles on the recovery of pharmaceuticals during PLE.

According to Table 2.8, the effect of additional cycles generally gives an improvement for the

compounds. Notable improvement is showed by paracetamol, carbamazepine, and

sulfamethoxazole, while the rest compounds experienced no major difference.

Regardless of a compound dependent, the effect of additional cycles is more obvious than the effect

of additional extraction time. This is clearly showed when the longer extraction time applied either

under 2 or 3 cycles.

19

Table 2.8 The recoveries (%) under varied cycles series and time (Ding et al . ,

2011).

Compounds Cycle (n) x extraction time (minutes)

2 x 15 3 x 15 2 x 25 3 x 25

Paracetamol

55

75

59

74

Carbamazepine

67

87

76

83

Chlortetracycline

52

53

53

54

Oxytetracycline

49

53

47

54

Sulfamethazine

89

92

86

90

Sulfamethoxazole

52

73

45

80

Tetracycline

50

52

61

57

2.2.2.5 Application of PLE for pharmaceuticals analysis

Some authors have investigated the effect of a number of extraction parameters and tried to obtain

the optimal conditions to extract pharmaceuticals from WWTP sludge and soil. Table 2.9 is

showing variable values for different compounds as their best condition to be applied in the PLE.

According to Table 2.9, the pressure of 100 bar is applied for most of the compounds. The

temperature ranges between 50 and 120°C, but mostly 100°C is used. Cycles applied for most of

compounds are between 2 and 5 cycles, and between 5 and 25 minutes for extraction time per cycle

series. Generally, 2 or 3 cycle series and 5 minutes of cycle time are used as the optimum

parameters. Solvents used are mostly combination of polar and less polar substances, for example

acetonitrile with water or methanol with water. For some of the compounds such as carbamazepine,

adding acid to set the pH at 2 appears to be one of the best solvent conditions. Another alternative is

to work with a buffered solution since it is more stable towards the surrounding pH alteration,

which works well for example for flumequine.

20

Table 2.9 Literature data of PLE optimization

Compounds T (°C) Pressure

(bar)

Cycles (n)

x

time (minute)

Solvent (v/v) Matrix Recovery

(%) Reference

Carbamazepine

75 100 3 x 15 Acetonitrile:water (7:3) pH 2 WWTP sludge 88 Ding et al., 2010

50 100 2 x 5 Acetone:citric acid (50:50) Soil

(sandy and clay)

94 Chitescu et al., 2011

60 100 2 x 5 Methanol:water (50:50) WWTP digested

sludge

120 Barron et al., 2008

100 100 2 x 15 Methanol:water (50mM H3PO4)

(50:50)

WWTP sludge 112 Nieto et al., 2007

100 100 3 x 5 Methanol:water (1:2) WWTP sludge 84 Radjenovic et al., 2009

Ciprofloxacin 50 100 2 x 5 Acetone:citric acid (50:50) Soil

(sandy and clay)


Diazepam 90 100 3 x 7 H2O Soil 79 Vazquez et al., 2010

Diclofenac 50 100 2 x 5 Methanol:citric acid (50:50) Soil

(sandy and clay)


60 100 2 x 5 Methanol:water (50:50)

WWTP digested

sludge

120 Barron et al., 2008

100 100 2 x 15 Methanol:water (50mM H3PO4 )

(50:50)

WWTP sludge 82 Nieto et al., 2007

21


(bar)

Cycles (n)

x

time (minute)


(%) Reference

100 100 3 x 5 Methanol:water (1:2) WWTPs sludge 60 Radjenovic et al.,2009

Flumequine 86 69 5 x 5 Methanol: McIlvaine buffer

(50:50) pH 3

WWTP sludge 99-100 Dorival-García et al.,

2013

Fluoxetine 100 100 3 x 5 Methanol:water (1:2) WWTPs sludge 15 Radjenovic et al.,2009

Indomethacine 60 100 2 x 5 Methanol:water (50:50)

WWTPs digested

sludge

120 Barron et al.,2008


Metronidazol 100 100 3 x 5 Methanol:water (1:2) WWTPs sludge 81 Jelic et al., 2009

Oxytetracycline 75 130 3 x 25 Acetonitrile:water (7:3) WWTPs sludge 52 Ding et al.,2010

Paracetamol 75 100 3 x 15 Acetonitrile:water (7:3) WWTPs sludge 85 Ding et al.,2010

60 100 2 x 5 Methanol:water (50:50) WWTPs digested

sludge


100 100 2 x 15 Methanol:water(50mM H3PO4)

(50:50)

WWTPs sludge 109 Nieto et al., 2007


22


(bar)

Cycles (n)

x

time (minute)


(%) Reference

Sulfadoxine 100 100 3 x 5 Methanol:water (25:75) WWTPs sludge 56 Garcia-Galan et al.,

2013

Sulfamethazine 100 100 3 x 15 Acetonitrile:water (7:3) WWTPs sludge 95 Ding et al.,2010


WWTPs digested

sludge


Sulfamethoxazol 50 - 100 100 3 x 25 Acetonitrile:water (7:3) pH 2

WWTPs sludge 78 Ding et al.,2010


WWTPs digested

sludge

n.r Barron et al.,2008

100 100 3 x 5 Methanol:water (1:1) WWTPs sludge 64 Gobel et al., 2005


Tertracycline 100 100 2 x 25 Acetonitrile:water (7:3) WWTPs sludge 54 Ding et al.,2010

Trimethoprim 100 100 3 x 5 Methanol:water (1:2) WWTPs sludge 57 Radjenovic et al.,2009

Venlafaxine 120 100 3 x 5 Methanol: water (1:1) pH 2

acetic acid

Wastewater

suspended

particulate matter

91 Baker and Kazprzyk-

hordern, 2011

n.r : not reported

23

3 OBJECTIVES AND SCOPE OF STUDY

From the presented literature review, it is clear that pharmaceuticals enter WWTPs where they can be

removed by several mechanisms including biodegradation and sorption. Given the inefficient

performance of most WWTPs towards biorecalcitrant micropollutants, pharmaceutical residues can be

present in two outputs of the WWTPs: the effluent and the sludge. Thus, they are both further released

to the environment (e.g. effluent discharge in surface water and sludge application for agricultural

activity). The pharmaceuticals might occur in the effluent when they were not fully removed during

the process, while their presence in sludge is due to sorption. Knowledge on both aspects is necessary

to understand the operational mechanisms and removal effectiveness of WWTPs towards

pharmaceuticals as emerging environmental contaminants, and to minimize associated environmental

risks. Therefore, methods for analysis need to be developed in order to be able to quantify these

pharmaceutical residues in both matrices.

Although the analysis of pharmaceutical residues in the water phase (influent/effluent) has been often

studied, the analysis of WWTP sludge is performed less frequent. In addition, only a few studies have

considered multi-residue pharmaceuticals that belong to various therapeutic groups, whereas most of

the studies only focus on one group of pharmaceuticals. Thus, scattered and limited data on how to

improve the extraction of pharmaceutical residues from sludge imposes new systematic research with

multi-residue pharmaceutical targets.

The goal of this study is therefore to develop an analytical method to facilitate a better quantification

of multi-residue pharmaceuticals in WWTP sludge. The focus is put on the development and

optimization of the extraction step. To do so, modern PLE is used and its parameters are modified to

investigate the effects of adding a dispersing agent in the extraction cell, extraction temperature,

number of cycles, extraction solvent, and including a post-extraction step for clean-up and pre-

concentration. Orbitrap high-resolution mass spectrometry (HRMS), coupled to ultra-high

performance liquid chromatography (UHPLC), is used for the instrumental analysis including

separation and selective and sensitive quantification.

A quite novel aspect considered during method optimization is the minimization of the matrix effect

(i.e. enhancement or suppression), which is of particular importance when using mass spectrometry as

the detection method. By having lower matrix effects, interferences are minimized resulting in more

reliable and robust measurements. Matrix effects are rarely used as an evaluation parameter in the

literature, despite its crucial influence towards the accuracy of the overall measurement.

As a last objective, the extraction method that provides optimal recovery and matrix effects will be

used to determine the concentration of 40 pharmaceuticals in the sludge of the WWTP of Aalst,

Belgium.

24

4 MATERIALS AND METHODS

The experiments were done in the laboratory of the Environmental Organic Chemistry and

Technology research group, Ghent University, Belgium. The main steps are sampling, dewatering

sludge, extraction, and instrumental analysis. The procedure of sampling and dewatering sludge will

be respectively explained in section 4.2 and 4.3. The extraction conditions and procedure including

clean-up and pre-concentration will be elaborated in section 4.4 and 4.5. Furthermore, the instrumental

analysis is described in section 4.6.

A scheme of the workflow during this research is shown in Figure 4.1.

Figure 4.1 Schematic workflow for the analysis of pharmaceutical residues in WWTP

sludge.

Sampling

Dewatering sludge:

Filtration

+

lyophilization

Extraction: PLE

Analysis: LC-HRMS

(Liquid chromatography-

high resolution mass spectrometry)

25

4.1 Materials and chemicals

The chemicals used during the experiments are summarized in Table 4.1.

Table 4.1 List of chemicals.

Chemicals Supplier

Citric acid monohydrate P.A. ACROS organics

Water HPLC grade ACROS organics

Methanol HPLC grade Fisher chemicals

Sand, 50-70 mesh particles (quartz, sand, white quartz, SiO2) Sigma Aldrich

ethylenediaminetetraacetic acid disodium salt dihydrate 99.0-101.0% Sigma Aldrich

Sodium phosphate dibasic, ! 99% Sigma Aldrich

Liquid nitrogen Air liquide

Nitrogen gas Alphagaz

Formic acid, 99% Sigma Aldrich

4.2 Sampling of sludge

The sludge sample with a volume of 10 L was taken from the sludge recycle stream after the second

settler from the WWTP of Aalst (Belgium) on August 7th

2013. The WWTP Aalst treats a water

volume of 10000 inhabitant equivalent and has a hydraulic retention time (HRT) and sludge retention

time (SRT) of respectively 28 hours and 22 days.

4.3 Dewatering of the sludge: fi ltration and lyophilization

Prior to lyophilization the water content of the sludge was reduced by filtration. The sludge samples

were homogenated and filtered with 1 "m GF/D Whatmann glass fibre filters (VWR, Belgium).

Filtered sludge with a volume of 2 L was spiked with a pharmaceutical standard solution to 291.7 ng/g

dry weight, stirred well and then left for > 48 hours to let it sorb onto the sludge. All the samples, both

spiked and not-spiked, were transferred to round bottom flasks and subsequently frozen in a box of

styrofoam filled with liquid nitrogen. Lyophilization was performed overnight by a vacuum instrument

(Alpha 1-2 LD plus, Bioblock scientific) (Figure 4.2). Afterwards, the dried sludge was weighted and

stored at -20°C.

26

Figure 4.2 Lyophilization vacuum instrument.

4.4 Pressurized liquid extraction (PLE)

4.4.1 Initial PLE conditions

The initial PLE extraction conditions were chosen based on Barron et al. (2008) and Radjenovi! et al.

(2009). Firstly, 1 gram of dried sludge was mixed with 25 gram of sand. The mixture was grinded and

poured into the 22 ml stainless steel extraction cell. The cell’s top and bottom were covered with a

cellulose nitrate filter (27 mm diameter, Dionex) to prevent the mixture being leaked out from the cell.

The extraction cell is depicted in Figure 4.3.

The extraction solvent was prepared by mixing methanol (MeOH):water (1:2 v/v). The PLE system

was rinsed with 15 mL of solvent in 3 cycles to prevent contamination prior to start-up. The extraction

temperature was set at 100oC for 5 minutes (extraction time) and repeated twice (2 cycles). The system

has an automatic pressure sensor that maintains the constant pressure at 1500 psi. The initial PLE

conditions are summarized in Table 4.2

Approximately 40-45 ml of extract was diluted with water to a final volume of 50 mL. Subsequently, 1

ml of the extract was finally filtered through a 0.2 "m polytetrafluoroethylene (PTFE) syringe filter,

then the filtrate was brought to a vial and 10 "L of 10% formic acid was added.

27

Figure 4.3 PLE extraction cell (www. dionex.com).

Table 4.2 Initial PLE conditions.

Matrix 1 gram sludge and 25 gram sand

Temperature 100°C

Number of cycles 2

Extraction time 5 minutes

Solvent Methanol:H2O (1:2) v/v

4.4.2 Conditions for the optimization of the PLE procedure

4.4.2.1 Condition in the extraction cells and PLE settings

Na2EDTA washed sand was prepared by immersing sand in the 1 g/L of Na2EDTA solution and the

mixture was stirred continuously (Andreu et al., 2009; Vazquez-Roig et al., 2010). Subsequently, the

sand was filtered with 1 !m GF/D Whatmann glass fibre filter and dried in the oven overnight. In the

extraction cell, 200 mg Na2EDTA and 100 mg NH4Ac were also added together with the sample

mixture during the extraction optimization.

The effect of the temperature, cycles and extraction time were investigated by changing the

temperature to 80°C, the number of cycles and extraction time to 1 cycle and 10 minutes, respectively.

Changing the cycle is attributed with the percentages of solvent stream. Every one cycle, solvent can

penetrate into the extraction cell up to half of the total volume of the extraction cell. If the solvent

stream is intended to fully fill the extraction cell, then 50% solvent stream should be applied instead of

100%. If any additional cycles applied, then it should be multiplied with the number of cycles (e.g 2

cycles: 100% solvent stream). In this way, the old solvent will completely replaced by a new fresh

solvent.

28

4.4.2.2 Extraction solvent

To adjust the pH of the extraction solvent, for pH 2, water with 50 mM phosphoric acid (H3PO4) was

used. For pH 3, 4,5 and 6, McIlvaine buffer (Dorival-García et al., 2013; Golet et al., 2002; Nieto et al.,

2007) was prepared by combining citric acid and Na2HPO3 as described in Table 4.3.

To change the organic solvent composition, methanol was replaced with acetonitrile (ACN). The

composition between organic solvent and water was also change from 1:2 to 1:1 (v/v).

Table 4.3 McIlvaine buffer composition at respective pH condition (McIlvaine, 1921).

pH 0.1 M citric acid (ml) 0.2 M Na2HPO4 (ml)

3 51 199

4 96 154

5 129 121

6 158 92

4.5 Clean-up and pre-concentration

4.5.1 Solid phase extraction (SPE)

Pre-concentration was done after the PLE conditions were optimized. The aim is to concentrate the

extract before instrumental analysis by LC-HRMS. Solid phase extraction (OASIS HLB 200mg, 6 ml)

aims to concentrate the extract but also to clean-up simultaneously. There are 4 steps during SPE:

conditioning, sample addition, washing and elution (Figure 4.4). Conditioning was aimed to activate

the sorbent prior to the sample loading. During the sample loading, the analytes are sorbed onto the

sorbent while some interferences goes through along with the solvent. Washing is necessary in order

to remove the interferences that are still present on the sorbent. The last step is eluting the analytes

with a suitable solvent that is able to extract analytes from the sorbent to the liquid phase (Lucci et al.,

2012)

Before SPE was performed, 25 ml of extract was diluted in 500 ml of water and then filtered using

Whatmann filter grade GF/D and 0.45 !m Whatmann nylon membrane, respectively. Since the

extracts are contains quite large suspension, a direct loaded to SPE can cause blockage and faster

sorbent saturated. Thus, the extract was firstly diluted and then filtered with larger pores of filter in

order to get rid a larger suspended solid.

For conditioning, 6 ml of methanol was poured then followed by 6 ml of distilled water. Subsequently,

the PLE extracts were slowly loaded on the cartridges under vacuum. Afterwards, the cartridges were

washed with 4 x 6 ml of distilled water in order to remove the interferences. After washing, 5 ml

methanol was loaded to elute the sorbed analytes and then the samples were collected in tubes.

29

The samples were evaporated under N2 stream by using TurboVap LV (Caliper) until dry, then

reconstituted with 1 ml methanol:water (10:90) v/v. The solutions were subsequently vortexed (Bio

vortex V1, Hettic EBA 20) for 20 seconds then centrifuged with 2000 rpm for 2 minutes.

Figure 4.4 Solid phase extraction process (Lucci et al . , 2012).

4.5.2 Evaporation

Evaporation was conducted with the purpose only to concentrate the extract before analysis. Eight ml

of PLE extract was evaporated under a N2 stream until the volume was reduced to 1.5 ml.

Subsequently, it was reconstituted with 200 !L methanol and 20 !L of 0.1% formic acid and finally

diluted with water until 2 ml. The solutions were vortexed and subsequently centrifuged at 2000 rpm

for 6 minutes.

4.6 Liquid chromatography – high resolution mass spectrometry (LC-HRMS).

The quantitative analysis was performed by QExactive instrument (Thermo scientific) with an

Orbitrap mass analyzer and a heated electrospray ionization source (HESI-II). Electrospray ionization

(ESI) was performed in positive mode; the parameters for ESI positive are summarized in Table 4.4.

Full scan mode was in the range of m/z 150- 500 and the resolving power was 70000 at full width at

half maximum (FWHM).

30

Table 4.4 Parameters for ESI positive.

Parameters Value

Spray voltage +3.5 kV

Sheat gas flow rate 45 AU

Capillary temperature 350°C

Heater temperature 375°C

A hypersil gold 50 mm x 2.1 mm (Thermo scientific) column with a particle size of 1.9 !m was used

during the chromatographic separation.

Methanol (VWR, Belgium) + 0.1 % formic acid (Solvent A) and water (VWR, Belgium) + 0.1%

formic acid (Solvent B) were used as the mobile phase during the separation, while an equal amount of

isopropanol:acetonitrile:methanol:water (Solvent C) was used for cleaning the column.

The flow was 350 !L/min and started with 10% of solvent A and 90% of solvent B. Solvent A

increased gradually while solvent B decreased until the next 16 minutes, 100% of solvent A and 0%

solvent B were reached. Subsequently, 100% solvent C was flow over for 5 minutes. Subsequently,

10% of solvent A and 90% of solvent B were flow for the final 5 minutes. The solvent gradient during

the separation is summarized in the Table 4.5.

Peak integration and calibration were performed respectively using Exact Finder and statistical

computing software R 2.15.3.

Table 4.5 Solvent gradient during separation.

Time (min) % Solvent A % Solvent B % Solvent C

0.00 10 90 0

1.50 10 90 0

15.00 100 0 0

16.00 100 0 0

16.01 0 0 100

21.00 0 0 100

21.01 10 90 0

26.00 10 90 0

31

4.7 Calibration

Standard solutions were prepared by dilution of the stock solution to obtain concentrations ranging

from 0.01 to 1000 !g/L. A 2 mg/L of stock solution was made from 1 g/L individual solutions of each

pharmaceutical compound dissolved in MeOH:water (10:90) v/v with 0.1 g/L Na2EDTA and 0.1%

(v/v) of formic acid. The solvents for the individual solutions are listed in Appendix, Table A. The

stock and standard solutions were stored at 4°C.

A schematic diagram of the pharmaceuticals stock solutions is given in Figure 4.5.

Figure 4.5 Procedure of pharmaceuticals stock solution.

To determine the concentration of pharmaceuticals, external calibration was performed using the

standard solution in the range of 0.01 µg/L – 1000 µg/L. The calibration curve was created by plotting

the peak area as Y-axis and the concentration of standard solution as X-axis, then a regression line was

calculated using R 2.15.3 (R foundation, statistical and computing software) as shown in Equation 11.

Two regression lines were used and each line has a concentration range of a factor 1000. The first line

is from 1 µg/L to 1000 µg/L, and the second line is from the limit of detection (LOD) to 1000 x LOD.

Limit of detection is defined as the lowest concentration of certain compound that can be detected by

the instrument. This equation is then used to determine the concentration of pharmaceuticals in sludge

based on their peak area.

where

b = slope

a = constant

c = exponent constant

y = bxc+ a (Equation 11)

Stock solution:

2 mg/L of pharmaceuticals in

MeOH:H2O (10:90) v/v with 0.01 % v/v

Na2EDTA and 0.1 % v/v formic acid

1 g/L of 40 pharmaceuticals in the respective

solvent

Standard solution:

0.01 µg/L – 1000 µg/L

32

4.8 Determination of process efficiency, recovery and matrix effect.

In order to evaluate the performance and simultaneously tracking the loss of pharmaceuticals during

the process of analysis, three parameters are calculated, namely process efficiency (PE), matrix effects

(ME) and recovery (RE). Process efficiency can be described as an efficiency of the method in the

whole process counting from the extraction to the measurement step. The recovery represents the

extraction efficiency; as such a good extraction method will give recovery close to 100%.

To be clear, the terms of process efficiency is actually more or less defined as “recovery” in several

literatures, while in this study the recovery is specifically refers to the percentage of extraction

efficiency.

Matrix effect can be explained as the effect caused by the interferences, which in turn affect the

ionization efficiency and lead to inaccurate, insensitive, and not reproducible results (Jeli! et al., 2009).

Signal suppression and signal enhancement are two possibilities that can occur, due to change in

droplets formation and droplets evaporation during ESI analysis (Annesley, 2003). Ion suppression is

marked by the matrix effect <100%, while enhancement happens when the matrix effect >100%. Thus,

a process efficiency >100% can be obtained in the case of matrix enhancement.

To generate each of the evaluation parameters, spiking the sample with a pharmaceuticals standard

solution was done during the analysis process. To do so, 3 kinds of samples namely not-spiked, pre-

spiked and post-spiked samples were prepared.

A pre-spiked sample was prepared by adding the pharmaceutical standard solution in the thickened

sludge, before it was dried by lyophilization (Figure 4.6). On the other hand, post-spiked samples were

prepared by adding the 20 "g/ L of standard solution to the final extract before the instrumental

analysis (LC-HRMS).

The process efficiency was calculated by taking the difference of not-spiked and pre-spiked

concentration and then normalized by the standard solution added to the pre-spiked sample (Equation

12). The matrix effect, on the other hand, was computed by taking the difference of the not-spiked and

post-spiked concentration divided by the concentration of standard solution added in post-spiked

sample (Equation 13). The recovery (Equation 14) was determined from the value of both process

efficiency and the matrix effect. The uncertainty for process efficiency, matrix effect, and recovery are

formulated in Equation 15, 16, 17, respectively.

33

Figure 4.6 Spiking procedure.

Thickened sludge

Pre-spiked

Standard solution

Not-spiked

Lyophilization

PLE

Extract: Not-spiked Extract: Pre-spiked

Extract:

Post-spiked

Standard solution

LC-MS

PE =Cpre!spiked !Cnot!spiked

Cth!pre

"100%

ME =Cpost!spiked !Cnot!spiked

Cth!post

"100%

PE = RE !ME

SDPE =SDnot!spiked

2+ SDpre!spiked

2

Cth!pre

"100%

SDME =SDnot!spiked

2+ SDpost!spiked

2

Cth!post

"100%

(Equation 12)

(Equation 13)

(Equation 14)

(Equation 15)

(Equation 16)

34

SDRE=

SDPE

PE

!

"#

$

%&

2

+SD

ME

ME

!

"#

$

%&

2

'RE '100%

where

PE: Process efficiency (%)

ME: Matrix effects (%)

RE: Recovery (%)

Cnot-spiked: Concentration of pharmaceuticals measured in not-spiked sample (µg/L)

Cpre-spiked: Concentration of pharmaceuticals measured in pre-spiked sample (µg/L)

Cpost-spiked: Concentration of pharmaceuticals measured in post-spiked sample (µg/L)

Cth-pre: Theoretical pre-spiked concentration (µg/L)

Cth-post: Theoretical post-spiked concentration (µg/L)

SD: Standard deviation of the concentration (%)

4.9 Quality Assessment

4.9.1 Relative standard deviation (RSD) on repeated measurements

Experiments were performed in duplicate or in triplicate in order to assess the quality of a

measurement. Therefore, in order to consider an experiment as repeatable, the relative standard

deviation (RSD, Equation 18) on repeated measurements should be ! 30% (Figure 4.7). In the case

RSD>30%, the results were reported as not repeatable (n.r.), and if no signal appeared, the notation not

observable (n.o.) was reported.

Figure 4.7 Decision tree of detected pharmaceuticals.

RSD =SD

x!100%

Sample with n

replication

RSD>30% RSD!30%

Detected Not repeatable

(n.r.)

No signal

Not observable

(n.o.)

(Equation 17)

(Equation 18)

35

4.9.2 Procedure for the determination of pharmaceutical concentrations in sludge

The actual concentration of pharmaceuticals in the sludge was calculated from the calibrated

concentration (Section 4.7) divided by the respective process efficiency of the developed PLE method

(Equation 19).

Cs =Ccalib

PE!Vextract

Msludge

where

Cs : Concentration of pharmaceuticals in the sludge (µg/kg SS)

Ccalib: Calibrated concentration (µg/L)

PE: Process efficiency (%)

Vextract: Volume of the extract (L)

Msludge: Mass of dried sludge in the extraction cell (kg SS)

In order to quantify a detected compound, the threshold was applied that RSDPE have to be lower than

20% (Equation 20). This was done to differentiate residues on uncertainty basis. As such, a compound

which has RSDPE<20% implies that the compound can be quantified with low uncertainty.

where,

RSDPE: Relative standard deviation of process efficiency (%)

SDPE: Standard deviation of PE (%)

RSDPE=SD

PE

PE!100%

(Equation 19)

(Equation 20)

36

5 Results and Discussion

5.1 Evaluation of the initial PLE method

The first step in this study is to determine the extraction performance of the initial PLE procedure

which was applied to the sludge sample (Table 4.2). In this initial measurement, not-spiked, pre-

spiked, and post-spiked samples were measured. The results of PE, RE, and ME are presented in

Figure 5.1 and categorized into 7 groups. If the pre/post-spiked sample was labeled as not

observable (n.o.) or not repeatable (n.r) then the value cannot be determined (n.d.).

According to Figure 5.1 (a), 13 compounds were found to have a process efficiency < 20% and only

1 compound in the range 80-100%. These 13 compounds are mainly composed of quinolones and

sulfonamides, and other compounds such as trimethoprim, amitriptyline and paroxetine. The reason

behind those numbers can be further explained by correlating with the graph of recovery and matrix

effects (Figure 5.1 (b) and (c)). The low value of process efficiency is mainly due to low recovery,

which is clearly shown by 11 compounds that give a recovery below 20%.

Moreover, 27 compounds have a matrix effect out of range 85-115%; with 17 compounds ion

suppression (ME<85%) and 9 compounds ion enhancement (ME>115%). The desired range (ME:

85-115%) was obtained by 12 compounds, which are mostly quinolones.

Figure 5.1 Evaluation of the initial PLE conditions in terms of (a) process

efficiency (b) recovery (c) matrix effect.

!"

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'$"

()*" +#!" #!,$!" $!,%!" %!,&!" &!,'!!" -'!!"

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,-%".-,/0'

1&(-%00'%2-3%4-5'678'

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()*" +#!" #!,$!" $!,%!" %!,&!" &!,'!!"

!"#$%&'!()!*+,'$

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1&-(2&'3!456!

!"

#"

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%"

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!"#$%&'()'*+,&#

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1,2&34'%5%-20'678'

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37

The results per compound are given in Table 5.1. The compounds are grouped based on the

functional group and structure similarities. This was done because the same group of compounds

was expected to give similar results. For example, fluoxetine, efavirenz, and pleconaril are grouped

because they all have a fluoromethyl (-CF3) functional group, or amantadine and rimantadine are

both adamantane derivatives. The other groups are sulfonamides, oseltamivir analogue, tetracycline,

quinolones, and benzodiazepines. The rest are not grouped since they have no chemical structure

similarities.

The low recovery (RE<20%) of 11 compounds is composed of 5 compounds of the quinolones,

sulfamethoxazole, fluoxetine, tetracycline, amitriptyline, paroxetine and trimethoprim. The

recovery in the range of 60-80% is defined to be a good recovery at which consist of oseltamivir

analogue, benzodiazepines, sulfamethazine, nevirapine and carbamazepine. Moreover, acyclovir

provided an excellent recovery in in the range of 80-100%. There are 11 compounds such as

pleconaril, chlortetracycline, sarafloxacin and paracetamol that cannot be determined in pre-spiked

sample, because their concentrations are still lower than LOD.

Some compounds, such as ciprofloxacin, tetracycline, and moxifloxacin, have negative recovery

values. This might be due to high RSDPE, which are 103%, 201% and 202%, respectively. For these

compounds, the concentration in the not-spiked sample was relatively high causing only a small

difference between the concentration in not-spiked and pre-spiked sample. Hence, this resulted in a

high RSDPE due to measurement uncertainty.

Table 5.1 Process efficiency, recovery and matrix effect for 40 compounds in the

initial condition.

Compounds Group PE±SD (%) ME±SD (%) RE±SD (%)

Sulfadoxin 20±1 44±3 44±4

Sulfamethazine Sulfonamides 38±2 64±3 60±4

Sulfamethoxazole 1.3±0.1 38±4 3.3±0.4

Oseltamivir carboxylat Oseltamivir

analogue

44±12 63±4 70±20

Oseltamivir ethylesther 53±15 72±4 74±22

Efavirenz 52±18 125±15 42±15

Fluoxetine -CF3 10±3 90±7 11±3

Pleconaril n.d. 356±43 n.d.

Chlortetracycline

Tetracyclines

n.d. 131±4 n.d.

Oxytetracycline n.d. 51±5 n.d.

Tetracycline -2±4 87±6 -2±5

38


Besifloxacin

Quinolones

n.d. 99±5 n.d.

Ciprofloxacin -3±4 94±4 -4±4

Enrofloxacin 0.4±0.4 118±6 0.4±0.3

Flumequine 32±3 109±4 29±3

Gatifloxacin 1.4±0.3 89±6 1.5±0.3

Levofloxacin 1±3 98±6 1±3

Moxifloxacin -1±2 99±7 -1±2

Nalidixic Acid n.d. 105±7 n.d.

Sarafloxacin n.d. 99±7 n.d.

Amantadine Adamantane

derivatives

23±3 53±4 43±6

Rimantadine 23±1 67±4 34±3

Alprazolam 59±4 89±2 66±5

Diazepam Benzodiazepines 56±4 79±4 70±6

Temazepam 102±9 139±9 73±8

Acyclovir 30±2 32±9 95±27

Amitriptyline 8±7 82±5 10±8

Amoxicillin n.d. 149±5 n.d.

Carbamazepine 39±4 53±4 75±10

Diclofenac 74±22 126±15 59±19

Indomethacin Not grouped 91±23 164±15 56±15

Lamivudine 24±2 43±10 55±13

Metronidazole n.d. 94±5 n.d.

Nevirapine 43±3 56±4 76±7

Paracetamol n.d. 84±5 n.d.

Paroxetine 9±3 76±6 11±7

Risperidone n.d. 123±5 n.d.

Trimethoprim 4±1 62±3 7±1

Venlaflaxine 17±3 66±3 25±4

Zidovudine n.d. n.d. n.d.

n.d..) not determined

39

Another approach was done on the physical-chemical properties basis, such as hydrophobicity.

Hydrophobicity can be presented as octanol-water partition coefficient (Log Kow). According to

Figure 5.2, no clear trend was found between the value of recovery and log Kow., since the

distribution is too scattered.

However, this cannot fully illustrate the function between recovery and hydrophobicity, since other

factors might be involved. The matrix effect is one of the factors that can influence the process

efficiency, thus it is difficult to select the mechanism that mainly governed the extraction efficiency.

Figure 5.2 Relationship between initial recovery (RSDRE <30%) and Log Kow.

According to the results of the measurement from the initial condition, it can be concluded that the

method optimization is necessary due to the low recovery, which was obtained by most of the

compounds. In addition, they were influenced by the matrix, which motivates that the method

optimization should be carried out.

5.2 Method optimization

The first step to do is to design an experimental setup that is explicitly discussed in section 5.2.1.

Moreover, the principle on how to evaluate the optimization will be explained in section 5.2.2.

Optimization of the PLE method encompasses (i) modification in the solid mixture, (ii) PLE

settings, and (iii) the extraction solvent. Modifying the solid mixture was done by washing the sand

with Na2EDTA and adding Na2EDTA and NH4Ac into the extraction cell, which will be further

discussed in section 5.2.3. In section 5.2.4, there will be an in-depth discussion about the effect of

changing PLE settings such as temperature, cycles, and extraction time. The effect of changing the

extraction solvent composition will be discussed in section 5.2.5. Clean up and pre-concentration as

a post-extraction step is presented in section 5.2.6.

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&!"#

'!"#

(!!"#

)*# )$# )(# !# (# $# *# %# +#

!"#$%"&'((

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40

5.2.1 Experimental design

An overview of the whole experiment during this study is depicted in Figure 5.3. Five steps of

optimization were carried out to examine the effect of different conditions. The optimization was

done by changing one parameter per step, for example only the sand was modified in condition B,

while other parameters were kept as in condition A. Pre-spiked samples were analyzed during the

optimization process, instead of not-spiked samples. The detailed parameters applied in procedures

A-F are presented in Table 5.2.

.

41

Figure 5.3 Schematic diagram of method optimization.

Optimization I

A + Na2EDTA washed

sand

B + Acidified solvent pH 2

C + Na2EDTA in

extraction cell

D + CH3COONH4

Optimization II

80 vs 100°C 1 vs 2 cycles 10 vs 5 minutes

B

C

D

E

Optimization III (no improvement)

pH 3 pH 5 pH 4 pH 6

Optimization IV

SPE evaporation

Optimization V

MeOH: buffer pH 5 ACN: buffer pH 5 F

Extraction cell : Sample + sands

Temperature : 100°C

Number of cycle : 2

Extraction time : 5 minutes

Solvent : MeOH: H2O (1:2) (v/v)

A (Initial conditions)

No improvement F

1:1 1:2 1:1

42

Table 5.2 Parameters for each condition during method optimization.

Conditions Parameter change Extraction cell T (ºC)

Cycle (n)

x

extraction

time (min)

Solvent (v/v) pH

A Initial conditions Sand+sample 100 2 x 5 Methanol: H2O (1:2) 7.1

Optimization I

B Na2EDTA washed sand Na2EDTA washed sand + sample 100 2 x 5 Methanol: H2O (1:2) 5.7

C Solvent pH 2 Na2EDTA washed sand + sample 100 2 x 5 Methanol : 50 mM H3PO4

pH 2 (1:2) 2.7

D Na2EDTA in extraction

cell

Na2EDTA washed sand + Na2EDTA + sample 100 2 x 5 Methanol : 50 mM H3PO4

pH 2 (1:2) 3.0

Optimization II

E NH4Ac Na2EDTA washed sand + Na2EDTA + NH4Ac

+ sample

100 2 x 5 Methanol : 50 mM H3PO4

pH 2 (1:2) 3.6

F pH 5 Na2EDTA washed sand + Na2EDTA+ sample 100 2 x 5 Methanol: McIlvaine

buffer pH 5 (1:2) 5.5

43

5.2.2 Evaluation of the method optimization

Every each treatment during the optimization process was evaluated by using the response ratio (RR),

which can portray the distinction between treatments. The RR (Equation 21) was determined by the

ratio of the concentration in modified condition (Cmodified) over the concentration in the condition

where the modified parameter was not applied (Cnot-modified).

Quality of the results is firstly assessed on the basis of RSD and this is done as it was explained in

section 4.9.1. These compounds with a RSD ! 30% were further on divided into 5 categories starting

from RR < 0.5 until RR " 2 within intervals of 0.4 for each group. Moreover, another category was

established based on the compounds that are detected (>LOD) in the modified condition and not

detected (<LOD) in the comparison condition. This category is labeled as signal/n.d.. Details on how

to categorize compounds is illustrated in Figure 5.4

Figure 5.4 Evaluation procedure for method optimization.

5.2.3 Modification of the solid mixture

5.2.3.1 Effect of washing the sand with Na2EDTA

According to the Figure 5.5, 11, 9, 6 compounds have a RR in the range 1.2-2, RR " 2, and signal/n.d.,

respectively. Only 4 compounds have negative effects by using the Na2EDTA washed sand. Five

compounds (pleconaril, amoxicillin, metronidazole, risperidone and zidovudine) were not detected

either under condition A or condition B.

RR =Cmod ified

Cnot!mod ified

Not determined

(n.d.)

Cmodified Cnot-modified

Both n.r. or n.o. Both detected Cmodified! detected

Cnot-modified ! n.r. or n.o.

Signal/n.d. RR

(Equation 21)

44

From these results, it can be concluded that the treatment of washing the sand with Na2EDTA gives an

improvement for the majority of the pharmaceuticals. Due to the ability of Na2EDTA to act as a

complexing agent that binds with metals, the undesirable interaction between the metals and

pharmaceuticals could be avoided which increases the recovery. Several compounds that are usually

present in charged form are benefited by this application, such as quinolones that are situated in the

range of RR ! 2.

Figure 5.5 Effect of Na2EDTA washed sand (Ccondition B/Ccondition A) .

The X-axis in Figure 5.6 is ordered on the basis of pKa starting from the lowest (levofloxacin) to the

highest (flumequine). The graph has intersection cross at RR=1, which is signed as a reference towards

positive (RR>1) or negative effects (RR<1). pKa3 (Appendix,Table A) is used as a relevant property

that can be associated to the charge form as the respective pH condition (pH 5.7). Since quinolones

have 3 kinds of charge form (anion, cation and zwitterion), their speciation can determine the

effectiveness of complexation by Na2EDTA. Zhou et al. (2013) predicted that the cationic form of

ciprofloxacin is dominating in acidic condition, which probably caused the less improvement by

means of washing the sand with Na2EDTA. Sarafloxacin (pKa: 5.9) improved the most, which might

be due to its zwitterionic form, since its pKa3 is closer to the pH condition.

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Figure 5.6 Effect of Na2EDTA washed sand on quinolones.

5.2.3.2 Effect of Na2EDTA in the extraction cell

The addition of Na2EDTA into the extraction cell is tested as it was performed in condition D. Positive

effects were experienced for 18 compounds (Figure 5.7). The range of 1.2-2 is mostly comprised of

quinolones while in the RR ! 2 category is only occupied by tetracycline. Negative effects were

observed for 2 compounds, which are lamivudine and temazepam. These compounds are having the

same negative effects with the effect of Na2EDTA washed sand. However, there are still some

compounds that are not having the same degree of impact compared to when the Na2EDTA sand was

applied.

Figure 5.7 Effect of Na2EDTA (Ccondition D/Ccondition C) .

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46

5.2.3.3 Effect of NH4Ac in the extraction cell

The effect of adding NH4Ac in the extraction cell was based on the RR of condition E and condition D.

According to Figure 5.8, no great difference was observed for the majority of compounds by adding

NH4Ac in the extraction cell. This is showed by the RR of 26 compounds that varies between 0.8-1.2.

However, 5 compounds were found to have positive effects including acyclovir (RR!2).

NH4Ac can act as a buffer, which is not affecting the work of Na2EDTA in general yet slightly

increase the overall pH (Table 5.2).

Figure 5.8 Effect of NH4Ac (Ccondition E /Ccondition D) .

5.2.4 PLE settings

5.2.4.1 Temperature

The effect of extraction temperature was investigated by comparing the results obtained as on the

extraction temperature of 80°C with those obtained at 100°C. According to the Figure 5.9, there are no

great differences (RR: 0.8-1.2) for 27 compounds. Negative impact was experienced mostly by

quinolones (RR < 0.5 and 0.5-0.8)

Elevated temperature during the extraction allows an increased solid-liquid mass transfer rate, thus

increasing the extraction efficiency. However, higher extraction temperature can also cause thermal

degradation that could reduce the extraction efficiency.

The matrix effects are another possibility to explain lower concentrations at a higher temperature.

Considering that the higher extraction temperature does not only increases the extraction efficiency of

the compound but also the amount of matrix interferences, an increased the temperature could

proportionally increase the matrix effects (O’Connor et al., 2007).

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Figure 5.9 The effect of extraction temperature (C80/C100) .

5.2.4.2 The number of cycles

The effect of cycles is tested by comparing 2 cycles with 1 cycle. The total extraction time was set to

10 minutes (i.e. 2 cycles of each 5 min or 1 cycle of 10 min) to have a clear effect of applying two

different cycles. By having an additional cycle, new fresh solvent will be introduced so that higher

efficiency is expected.

According to Figure 5.10, a negative effect was experienced for 2 compounds, showed by the RR in

the range of RR<0.5 and 0.5-0.8. Eleven compounds were observed to have an improvement,

according to the RR values that vary in the range 1.2-2, RR ! 2 and signal/n.d.. For 21 compounds, the

RR’s are situated in the range 0.8-1.2.

Figure 5.10 The effect of cycles at a total extraction time of 10 minutes (C2cycles/C1cycle) .

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5.2.4.3 Extraction time

Increasing the extraction time has the purpose to have a longer time for solvent to retain in the

extraction time, so that increasing recovery is expected. The extraction time was tested by comparing

10 minutes and 5 minutes at 2 cycles. Nine compounds experienced a positive effect (RR: 1.2-2),

while 2 compounds have a negative effect (RR<0.5 0.5-0.8). A RR in the range of 0.8-1.2 (Figure

5.11) is obtained for the majority of the compounds (21 compounds), which means that there is no

great difference between an extraction time of 10 minutes and 5 minutes.

Figure 5.11 The effect of extraction time at 2 cycles (C10minutes/C5minutes) .

5.2.5 Extraction solvent composition

5.2.5.1 Effect of pH

The pH of the extraction solvent is tested at pH 2, 3, 4, 5 and 6. To study the impact of pH 2, the water

medium is replaced to H3PO4 pH 2 (50 mM). According to Figure 5.12, nine compounds improved

more than two-fold compared to condition B. These compounds mostly belong to quinolones,

excluding enrofloxacin, nalidixic acid, and flumequine. On the contrary, the tetracyclines and

enrofloxacin are situated in the range RR < 0.5. A study of O’Connor et al. (2007) also confirms that

the tetracyclines obtain lower recovery in acidic conditions. Benzodiazepines and sulfonamides also

decreased slightly with the RR in the range 0.5-0.8. pH 2 was tested as it is believed that in such acidic

condition, the electrostatic interactions between the compounds and the sludge will be disturbed as a

consequence of protonation of the sludge surface (Ding et al., 2011). In addition, the final extract was

more transparent showing that less matrix interferences were extracted. This could also explain the

better performance at pH 2 for some compounds.

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Figure 5.12 The effect of acidified solvent at pH 2 (Ccondition C/Ccondition B) .

However, using a solvent with pH 2 resulted into a large gap of impact between some groups, for

example, negative effects (RR <0.5) were experienced by the tetracyclines, whereas the positive effect

for the quinolones with RR ! 2. Therefore, setting the condition at a certain pH might be a solution to

compensate the loss of other group. A buffer solution was used in a mixture with methanol to create a

more stable pH condition.

A McIlvaine buffer was set to pH 3, 4, 5, 6 and mixed with methanol at a ratio of MeOH: McIlvaine

buffer (1:2). The McIlvaine buffer was selected due to its wide range of pH that makes it easier to

choose the tested pH range. The result for each pH condition was compared to the condition D (Figure

5.13).

At pH 3, a negative effect (RR < 0.5 and 0.5-0.8) was observed for 6 compounds, while 8 compounds

experienced positive effect (1.2-2, RR ! 2 and signal/n.d.). The distribution of RR in pH 4 and pH 5

are comparable with the distribution at pH 3. However, a negative effect was found for 12 compounds

at pH 6 (RR < 0.5 and 0.5-0.8). In spite of no striking differences between the effect at pH 3, 4 and 5,

condition at pH 5 was selected for further experiments since it has slightly more compounds that have

positive impact. In addition, the extracts at pH 5 was more transparent than the extracts at pH 6, which

can be assumed that it might have less interferences.

The concentrations of quinolones except enrofloxacin decrease as the pH increase, whereas the

benzodiazepines increase. This can be associated to the pKa of the groups that describing their charge

form at respective pH condition. Fluoxetine and efavirenz (-CF3 group) have respectively pKa of 10.1

and 9.1, thus they are more likely present in positive charge at which they are still attached to the

matrix.

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Figure 5.13 Effect at pH 3, 4, 5, 6 (CpH/CconditionD).

5.2.5.2 Effect of organic solvent composition

In order to investigate the effect of organic solvent, the portion of it in the mixture was increased in

order to obtain a higher recovery. The compositions that are tested are : MeOH:buffer pH 5 (1:1),

ACN:buffer pH 5 (1:2) and ACN:buffer pH 5 (1:1). Each of these new compositions was compared to

condition F, at which MeOH:buffer pH 5 (1:2) was used as an extraction solvent.

Figure 5.14 shows that there is no great difference with the MeOH:buffer pH 5 (1:1), even though 9

compounds experienced an increased (1.2–2, RR ! 2, signal/n.d.). These compounds mostly belong to

the quinolones, such as ciprofloxacin, enrofloxacin, gatifloxacin, and sarafloxacin.

Three compounds for RR < 0.5 and 0.5-0.8 was observed for the mixture of ACN:buffer pH 5 (1:2)

and 5 compounds experienced an improvement within the range 1.2-2 and RR ! 2.

Those compounds, which improved with RR ! 2, are enrofloxacin, ciprofloxacin, and levofloxacin,

which belong to the quinolones group. Under the application of ACN:buffer pH 5 (1:1), the negative

effects were found to be higher than with the other solvent composition, with 7 and 2 compounds

respectively are situated in the range RR > 0.5 and 0.5-0.8.

According to Figure 5.15, half of the pharmaceuticals have a desired range of matrix effect (85-115%)

by employing MeOH:buffer pH 5 (1:2). However, 10 compounds experienced suppression and 7

compounds show ion enhancement. The distribution of matrix effects between application of

MeOH:buffer pH 5 (1:2) and MeOH:buffer pH 5 (1:1) is not so different. Hence, increasing the

fraction of methanol in the mixture does not provide either great improvement or deterioration.

For the solvent ACN:buffer pH 5 (1:2), 10 compounds experienced suppression while enhancement

occurred for 5 compounds. The matrix suppression is more severe for ACN:buffer pH 5 (1:1), where

24 compounds were affected. The concentration of enrofloxacin and ciprofloxacin dropped drastically

when the fraction of ACN increased.

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51

This decline is probably due to the matrix effects showing stronger suppression, approximately 100%

to 20% lower than when ACN:buffer pH 5 (1:2) was applied. Apparently, increasing the fraction of

acetonitrile proved not to be as effective as employing ACN: buffer pH 5 (1:2). Based on these results,

it can be concluded that there is no striking improvement by modifying the organic solvent as well as

their portion in the solvent mixture. Therefore, condition F (Figure 5.3) was preceded to the next step

of the Optimization V.

Figure 5.14 Ratio of various composition and organic solvent extraction

(Cnew composition/Ccondition F) .

Figure 5.15 Matrix effect with various extraction solvent compositions.

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52

5.2.6 Clean- up and pre-concentration

5.2.6.1 SPE

Post extraction was aimed to concentrate the compounds and to reduce interferences, thus increasing

the process efficiency and reducing the matrix effect. However, according to Figure 5.16, most of the

compounds were observed to have lower process efficiency when SPE was applied. Eight compounds

were initially found to have a process efficiency below 20%, whereas this number grew to 32, when

SPE was applied.

No compounds was found to have matrix effects in the desired range (ME: 85-115%) when SPE was

applied (Figure 5.17). Moreover, matrix ion suppression was experienced by 23 compounds

(ME<20%), and thus contributes to the low process efficiency.

The high turbidity of the extract suggests the presence of a lot of interferences. In this case, not only

the analytes are concentrated but also the interferences, thus some loss might be occurred as a

consequence of deterioration in the sorbent performance (O’Connor et al., 2007). As a result,

suppression might be experienced for all the compounds.

Figure 5.16 Process efficiency of condition F + SPE.

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Figure 5.17 Matrix effect of condition F + SPE and condition F + evaporation.

5.2.6.2 Evaporation

Evaporation is mainly aimed to pre-concentrate the extract by blowing a nitrogen stream to allow

evaporation of the extraction solvents. Also here, the majority of the compounds show increased

matrix suppression, with matrix effects in the range of 55-85% (Figure 5.17). The reason behind this is

possibly due to the concentration of the interferences along with the analytes, thus loss in the

efficiency might be obtained.

5.3 Comparison of the initial procedure, procedure F and literature

After testing and modifying some parameters in the extraction process, the condition F is then

evaluated by comparing with the initial conditions. PE and ME are used to compare the performances

of both procedures. Figure 5.18 shows that fewer compounds for condition F are in the range PE <20%,

with a slight increase of compounds in the range 60-80% and 80-100%, which indicates improvements

due to the method optimization. Several compounds with a PE < 20% in the initial procedure, shift to

the range 20-40% in procedure F. These compounds are gatifloxacin, amitriptyline, paroxetine and

venlaflaxine (Table 5.3).

There were 6 compounds, which cannot be detected in the initial conditions that turned to be detected

in procedure F with PE vary between 10-42%. Five compounds remain <LOD, e.g. pleconaril,

amoxicillin, metronidazole, paracetamol, and zidovudine. Acyclovir and lamivudine were detected

with a PE of 30% and 24%, respectively, under the initial conditions and turned to be <LOD for

procedure F. Overall, condition F provides improvement for most of the compounds.

More compounds were observed to have matrix suppression under condition F (Figure 5.19). Only 3

compounds observed to have matrix enhancement in the range of 115-145% under condition F, while

6 compounds at the initial conditions.

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Moreover, 14 compounds were observed in the desired range (ME: 85-115%), which is an

improvement for several compounds such as fluoxetine, moxifloxacin, rimantadine, diazepam,

amitriptyline and venlaflaxine.

Dorival-García et al. (2013) reported the PE for ciprofloxacin, enrofloxacin, moxifloxacin, and

flumequine on WWTPs sludge in Granada, Spain, with values between 95-100, 97-103, 98-103, and

98-101%, respectively. If these are compared, the PE’s are much higher than the values in this study.

The use of methanol: buffer pH 3 (1:1) might be one of the factors that allow those quinolones are able

to obtain the high PE. Referring to section 5.2.5.1, quinolones are reacting positively when the acidic

extraction solvent was applied. In spite of those facts above, this study has found that fluoxetine was

obtained at a higher PE than those reported by Jeli! et al. (2009) with PE 15%.

In this study, paracetamol is still <LOD, while in the literatures, it was reported to have PE varies

between 2-109% (Barron et al., 2008; Ding et al., 2011; Nieto et al., 2007; Radjenovi! et al., 2009).

Figure 5.18 Process efficiency obtained with conditions A and F.

Figure 5.19 Matrix effects obtained with conditions A and F.

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55

Table 5.3 Process efficiency, matrix effect, and recovery for 40 pharmaceuticals

under condition F.


Sulfadoxin 25±1 71±1 35±1

Sulfamethazine Sulfonamides 41±1 74±1 55±2

Sulfamethoxazole 1.5±0.1 52.5±0.2 2.8±0.2


Analogue

42±2 80.5±0.4 52±2

Oseltamivir ethylesther 42±2 84±1 50±2

Efavirenz 105±18 135±7 78±14

Fluoxetine -CF3 41±8 89±6 46±10

Pleconaril n.d. 69±9 n.d.

Chlortetracycline

Tetracyclines

13±1 34±1 39±3

Oxytetracycline 10±7 72±3 14±10

Tetracycline 13±82 50±2 26±164

Besifloxacin

Quinolones

39±1 32±2 124±7

Ciprofloxacin -19±27 90±1 -21±30

Enrofloxacin 3.7±0.2 81±2 4.5±0.3

Flumequine 38±2 73±2 53±3

Gatifloxacin 37±4 75±4 50±6

Levofloxacin -11±27 87±1 -13±31

Moxifloxacin -30±16 91±5 33±17

Nalidixic Acid 47±3 70±2 68±4

Sarafloxacin 35±5 79±2 44±7

Amantadine Adamantane

derivatives

39±1 78±3 49±2

Rimantadine 44±2 85±1 51±2

Alprazolam 86±4 143±2 60±3

Diazepam Benzodiazepines 60±3 108±2 56±3

Temazepam 67±5 23±0.4 292±21

Acyclovir n.d. 62±5 n.d.

Amitriptyline 47±11 93±2 50±12

Amoxicillin n.d. 6.9±0.4 n.d.

Carbamazepine Not grouped 31±3 64.0±0.3 49±5

Diclofenac 87±11 101±2 86±11

Indomethacin 107±10 112±6 95±10

Lamivudine n.d. 109±5 n.d.

Metronidazole n.d. 85±3 n.d.

56


Nevirapine 40±1 78±2 52±2

Paracetamol n.d. 77±1 n.d.

Paroxetine Not grouped 41±7 84±5 49±9

Risperidone 24±2 136±5 17±2

Trimethoprim 5.3±0.2 98±1 5.4±0.2

Venlaflaxine 36±2 87±2 41±2

Zidovudine n.d. 95±4 n.d.

n.d.) not determined

5.4 Concentration of pharmaceuticals in the sludge sample

There are 17 compounds that can be detected under condition F (Table 5.4). Those detected

compounds include 10 compounds that can be quantified (RSDPE<20%). Venlaflaxine, paroxetine and

diclofenac are the three most abundant compounds with concentration that vary between 94 - 2.5 x 102

µg/kg dry matter. The compounds that could be detected at the lowest concentration are

sulfamethazine, diazepam and amantadine. Some detected compounds (fluoxetine, oxytetracycline,

tetracycline, ciprofloxacin, levofloxacin, moxifloxacin, and amitriptyline) were probably present at

high concentrations but could not be quantified, because of the uncertainty of the PE values

(RSDPE>20%). However, this resulted in high uncertainty in the PE value and thus cannot be

quantified. For example, Golet et al. (2002) found concentrations of ciprofloxacin varying between

1.5-2.6 x 103 µg/kg dry matter in sewage sludge. This supports the hypothesis that ciprofloxacin could

be present at a quite high concentration in the sludge. In addition, the high Kd (Log Kd : 4.3) value of

ciprofloxacin also illustrates its abundance in sludge (Table 2.1). The same holds for tetracycline (Log

Kd: 3.9), which is estimated to be present at high concentrations (detected, RSDPE>20%).

On the other hand, paracetamol is rarely detected over a 4 months period of measurement as reported

by Nieto et al. (2007) and it is reinforced with this study that paracetamol is cannot be detected. The

literature data of paracetamol Kd (Log Kd: -0.4) can also supports the theory that only less of it is

contained in the WWTP sludge.

The concentration observed for trimethoprim is in the same range as found in Beijing, China (Chen et

al., 2013), and Germany (Göbel et al., 2005), ranging between 10-500 µg/kg dry matter.

57

Table 5.4 Concentration (µg/kg dry matter) of pharmaceuticals in sludge sample.

Pharmaceuticals Group Concentration (%RSDa)

Sulfadoxin n.d.*

Sulfamethazine Sulfonamides 1.3 (4)

Sulfamethoxazole 50 (12)


analogue

n.d.*

Oseltamivir ethylesther n.d.*

Efavirenz 41(4)

Fluoxetine -CF3 d

Pleconaril n.d.

Chlortetracycline

Tetracyclines

n.d.*

Oxytetracycline d

Tetracycline d

Besifloxacin

Quinolones

n.d.*

Ciprofloxacin d

Enrofloxacin n.d.*

Flumequine n.d.*

Gatifloxacin n.d.*

Levofloxacin d

Moxifloxacin d

Nalidixic Acid n.d.*

Sarafloxacin n.d.*

Amantadine Adamantane derivatives

6 (13)

Rimantadine n.d.*

Alprazolam n.d.*

Diazepam Benzodiazepines 4.4 (5)

Temazepam n.d.*

Acyclovir n.d.

Amitriptyline d

Amoxicillin n.d.

Carbamazepine 88 (23)

Diclofenac Not grouped 94 (3)

Indomethacin n.d.*

Lamivudine n.d.

58

Pharmaceuticals Group Concentration (%RSDa)

Metronidazole n.d.

Nevirapine n.d.*

Paracetamol n.d.

Paroxetine 1.3 x 102 (3)

Risperidone Not grouped n.d.*

Trimethoprim 18 (15)

Venlaflaxine 2.5 x 102 (1)

Zidovudine n.d.

a) RSD in not-spiked sample (n=2)

d) detected with RSDPE>20%

n.d.) not determined for PE

n.d.*) not determined for not-spiked sample

59

6 Conclusions and Recommendations

6.1 Conclusions

In this study, the optimization of PLE conditions has been systematically investigated. and the

concentrations of pharmaceuticals belonging to different therapeutic classes in WWTP sludge has

been determined. Several parameters of the extraction procedure have been tested, and their effects

towards the extraction recovery, matrix effects and process efficiency have been studied.

The interaction of pharmaceuticals with sludge can be taken as the key to direct a strategy on the most

effective conditions that should be applied during the method optimization. It has been shown that

modifications such as washing the sand with Na2EDTA, the pH of extraction solvent, and the solvent

composition are the most important parameters. Applying these parameters on such values that have

been tested, proves to increase the process efficiency. As such, under the developed extraction method,

there are 15 compounds that have a process efficiency between 40-107%. The success of the

respective conditions in improving the extraction efficiency, can be correlated to the interaction which

was established between the pharmaceuticals and the sludge. The electrostatic interactions play an

important role, making the effectiveness of washing the sand with Na2EDTA and the pH of solvent

much more evident, since these conditions work on the basis of charge interactions. Other parameters

such as temperature, the number of cycles, and extraction time, are not essentially affecting the

quantification since their application gives only a slight change.

Clean-up and pre-concentration is a common effort to reduce matrix effects. However, SPE and

evaporation showed negative impact on the matrix effects and process efficiency. The extract

transparency became a critical issue for both of techniques, since matrix effect influenced the majority

of the compounds.

The concentrations of quantified pharmaceuticals vary from 1.3 µg/kg dry matter to 2.5 x 102 µg/kg

dry matter. Venlaflaxine has the highest concentration, while sulfamethazine has the lowest

concentration among the pharmaceuticals detected with low uncertainty (RSDPE<20%). In this study,

several antivirals such as amantadine and efavirenz were successfully quantified, which is quite novel

since this class of pharmaceuticals are rarely or never been quantitatively reported on WWTP sludege

in the literature before.

60

6.2 Recommendations

Further development of the extraction method can still be done particularly for those, having poor

recovery. A higher efficiency might be achieved by adding a higher spiked concentration to

accommodate those compounds, which are present already in a high concentration. Some constraints

regarding the post extraction that did not succeed at this study, might be solved by testing another type

of buffer (e.g acetate buffer) as a PLE extraction solvent which is expected to reduce the extract

turbidity (O’Connor et al., 2007). Increasing the extract transparency can contribute to have a less

matrix effects which often arise during this study. Besides changing the extraction solvent, extra

polishing and pre-concentration can be done by studying the effect of other sorbent materials in SPE.

According to the chemical properties of pharmaceuticals, the interactions with the SPE sorbent can be

diverse.

Method validation should be done to assure whether the procedure that has been developed is suitable

for the purpose of analytical quantification. From this several evaluation parameters such as accuracy,

precision, detection and quantification limits and linearity will be obtained to assess the method

validity. Lastly, the current evaluation method for optimization still has a rough estimation to decide

about the differences between two conditions. Improved experimental design to evaluate the extraction

optimization is therefore needed.

61

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