Pressurized liquid extraction and Orbitrap mass ...
Transcript of 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
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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
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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
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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
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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
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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
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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!
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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!
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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
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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.
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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).
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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.
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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).
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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)
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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)
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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.
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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)
44 Chitescu et al., 2011
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)
76 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 82 Nieto et al., 2007
21
Compounds T (°C) Pressure
(bar)
Cycles (n)
x
time (minute)
Solvent (v/v) Matrix Recovery
(%) 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
100 100 3 x 5 Methanol:water (1:2) WWTPs sludge 78 Radjenovic et al.,2009
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
2 Barron et al.,2008
100 100 2 x 15 Methanol:water(50mM H3PO4)
(50:50)
WWTPs sludge 109 Nieto et al., 2007
100 100 3 x 5 Methanol:water (1:2) WWTPs sludge 53 Radjenovic et al.,2009
22
Compounds T (°C) Pressure
(bar)
Cycles (n)
x
time (minute)
Solvent (v/v) Matrix Recovery
(%) 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
60 100 2 x 5 Methanol:water (50:50)
WWTPs digested
sludge
40 Barron et al.,2008
Sulfamethoxazol 50 - 100 100 3 x 25 Acetonitrile:water (7:3) pH 2
WWTPs sludge 78 Ding et al.,2010
60 100 2 x 5 Methanol:water (50:50)
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
100 100 3 x 5 Methanol:water (1:2) WWTPs sludge 52 Radjenovic et al.,2009
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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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
Compounds Group PE±SD (%) ME±SD (%) RE±SD (%)
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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(!!"#
)*# )$# )(# !# (# $# *# %# +#
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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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47
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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48
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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Table 5.3 Process efficiency, matrix effect, and recovery for 40 pharmaceuticals
under condition F.
Compounds Group PE±SD (%) ME±SD (%) RE±SD (%)
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
Oseltamivir carboxylat Oseltamivir
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
Compounds Group PE±SD (%) ME±SD (%) RE±SD (%)
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)
Oseltamivir carboxylat Oseltamivir
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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