faculty of bioscience engineering centre for...

Faculty of Bioscience Engineering

Centre for Environmental Sanitation

Academic year 2010 – 2011

MULTI-RESIDUE ANALYSIS OF HUMAN PHARMACEUTICALS

IN NAIROBI RIVER BASIN, KENYA

BY

K’OREJE KENNETH OTIENO

Promoters: Prof. dr. ir. Herman Van Langenhove

Prof. dr. ir. Kristof Demeestere

Tutor: Ing. Patrick De Wispelaere

Master‘s dissertation submitted in partial fulfillment of the requirements for the degree of

Master of Science in Environmental Sanitation

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Multi-residue analysis of human pharmaceuticals in Nairobi River basin, Kenya ©2011K’oreje Kenneth

COPYRIGHT

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

of it for personal use. Any other use is subject to the Laws of Copyright. Permission to produce any

material contained in this work should be obtained from the author.

©Gent University, August 2011

The author The promoters

K‘OREJE O. Kenneth Prof. dr. ir. H. Van Langenhove Prof. dr. ir. K. Demeestere

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DEDICATION

This work is dedicated to my late father Joseph Oreje, late brother Patrick Oreje,

late sister Mary Helida, my beloved Mum Silermina Oreje and Sister Wilfrida Oreje

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ACKNOWLEDGEMENT

To go up the ladder, others hold for one the ladder lest it falls. I take the earliest opportunity to

appreciate all those who held for me the academic ladder to reach the pinnacle. First and foremost,

my sincere and heartfelt gratitude goes to Prof. Herman Van Langenhove who not only accepted my

project proposal but also unconditionally accepted to be my promoter. His intelligent insights will

remain ingrained in my mind for years to come. Many thanks to my co-promoter Prof. Kristof

Demeestre. He was a teacher, a mentor and a counselor. Soft spoken, yet so keen and strict to detail.

His guidance shaped my scientific thinking and critical analysis, without which, my work would

have been invain. Special thanks to Patrick De Wispelaere for his tireless efforts that ensured smooth

running of the experiments. His advice was invaluable asset for the success of this work. I also

appreciate the entire EnVOC for their hospitality. In one way or the other, they made my work a

success.

Secondly, my appreciation to my colleagues at the Kenya Ministry of Water and Irrigation.

Specifically, my sincere thanks to Mrs Kellen Mwangi, Daudi Kamau and Andrew Kinyua for their

assistance during data search. Central Water Laboratory members, Noel Ndeti, Celline Obuya, John

Muasya, and Joram Kihumba deserve a special mention. Your time with me in the lab will remain

cherished. Not to forget Mwaura Murigi Njuguna of WARMA for his sacrifice during my sampling

operation. I give you thumbs up.

Thirdly, I thank the Kenya Medical Supplies Agency and its Director for accepting to release their

data to me unconditionally. Special thanks go Caroline Wambui for her unrelenting effort to ensure

that I got the data I needed even after leaving the country. Your heart of kindness will remain a

blessing to many.

Fourthly, my appreciation to the CES team, Veerle, Isabel and Sylvie for their unending support. You

made my life in Gent worth living. Prof. Mac Van de Hede must be mentioned in a special way. His

life experience stories and academic guidance have brought me this far. My gratitude to VLIR for

offering me the scholarship to study in this dynamic and classic University of Gent.

My gratitude to my marvelous classmates. Each time spent with you was a source of inspiration. My

study group members, Amanual, Susan, Malcom, Efuet, Meseret, Livin and Pascal, I appreciate you

all. I owe my success to your hard work and determination. I give praise to my friends Samson,

Nancy, Kapere, Bernadette and Sheila who always lifted up my spirit when life seemed too hard to

push. You were a great inspiration.

Finally, I thank my entire family for their sacrifice each day to ensure that I achieved my dream in

life. May God reward you handsomely.

And to God be the glory!

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TABLE OF CONTENT

COPYRIGHT ................................................................................................................................................... i

DEDICATION ................................................................................................................................................ ii

ACKNOWLEDGEMENT ........................................................................................................................... iii

TABLE OF CONTENT ................................................................................................................................. iv

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

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

ABBREVIATIONS AND NOTATIONS .................................................................................................. viii

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

CHAPTER 1 .................................................................................................................................................... 1

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

1.1 Background of the study ........................................................................................................... 1

1.2 Justification of the study ........................................................................................................... 2

CHAPTER 2 .................................................................................................................................................... 4

LITERATURE REVIEW ............................................................................................................................... 4

2.1 Classification and consumption of pharmaceuticals ................................................................. 4

2.1.1 Classification of pharmaceuticals ..................................................................................................... 4

2.1.2 Consumption of pharmaceuticals ..................................................................................................... 6

2.2 Pharmaceuticals in the environment ......................................................................................... 8

2.2.1 Sources of pharmaceutical residues in the aquatic environment................................................... 8

2.2.2 Occurrence of pharmaceuticals in the environment ....................................................................... 9

2.2.3 Fate of pharmaceuticals in the aquatic environment .................................................................... 13

2.2.4 Toxicological and Ecological impacts of pharmaceuticals .......................................................... 14

2.3 Abatement of pharmaceutical pollution .................................................................................. 15

2.3.1 Conventional treatment systems ..................................................................................................... 16

2.3.2 Advanced treatment systems ........................................................................................................... 16

2.4 Analysis of pharmaceuticals in the environment .................................................................... 18

2.4.1 Sample extraction ............................................................................................................................. 18

2.4.2 Separation .......................................................................................................................................... 20

2.4.3 Detection ........................................................................................................................................... 20

2.5 Scope of the study ................................................................................................................... 27

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CHAPTER 3 .................................................................................................................................................. 28

MATERIALS AND METHODS ................................................................................................................. 28

3.1 Chemicals ................................................................................................................................ 28

3.2 Sampling and sample preparation ........................................................................................... 28

3.2.1 Sampling area and sites ................................................................................................................... 28

3.2.2 Sampling period and method .......................................................................................................... 30

3.2.3 Sample preparation and extraction for organic trace analysis ..................................................... 30

3.3 Analysis of physical-chemical parameters.............................................................................. 31

3.4 Instrumental organic trace analysis ......................................................................................... 32

3.4.1 High Performance Liquid Chromatography (HPLC) ................................................................... 32

3.4.2 Mass Spectrometry ........................................................................................................................... 32

CHAPTER 4 .................................................................................................................................................. 35

RESULTS AND DISCUSSION.......................................................................................................... 35

4.1 Water quality of the Nairobi River basin: physical-chemical parameters .............................. 35

4.2 Prioritization of pharmaceutical compounds for trace analysis .............................................. 36

4.3 Trace analysis of human pharmaceuticals in Nairobi River basin .......................................... 42

4.3.1 Screening of priority compounds by HPLC-HRMS ............................................................... 42

4.3.2 Screening and selective target analysis of 14 focus compounds in Nairobi River basin ........ 43

4.3.3 Unequivocal identification and approximative quantification of detected focus compounds..

………………………………………………………………………………………………..48

4.3.4 Discussion ............................................................................................................................... 51

CHAPTER 5 .................................................................................................................................................. 56

CONCLUSION AND RECOMMENDATIONS ....................................................................................... 56

5.1 Conclusion .............................................................................................................................. 56

5.2 Recommendations ................................................................................................................... 56

List of Reference ........................................................................................................................................... 58

Appendix I: Molecular structures of focus compounds ............................................................................ 67

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LIST OF TABLES

Table 1: Classification of aspirin based on the ATC classification system ............................................... 4

Table 2: Classification of selected antibiotics based on molecular structure ........................................... 5

Table 3: Pharmaceutical consumption patterns in different countries in tons/year. ................................. 6

Table 4: Some PAIs treated using advanced oxidation processes ............................................................ 17

Table 5: Analytical methods for detection of pharmaceutical residues in environmental samples ...... 25

Table 6: Water quality of the Nairobi River basin ..................................................................................... 35

Table 7: Classes and quantity of pharmaceuticals supplied to the hospitals by KEMSA in 2008 ....... 37

Table 8a: List of minor group (subgroup A) .............................................................................................. 40

Table 8b: List of minor group (subgroup B) .............................................................................................. 41

Table 9: List of compounds detected during screening ............................................................................. 43

Table 10: Classification of the focus compounds for escan and MID analysis ...................................... 44

Table 11: Retention time, intensity, delta and STDEV (escan) of detected compounds in the Nairobi

River basin....................................................................................................................................... 47

Table 12: Retention times and peak areas (MID) of detected compounds in the Nairobi River basin

……………………………………………………………………………………………….48

Table 13: Retention time and response factor of the analytical standards .............................................. 49

Table 14: Peak area (response) of external standards during calibration and sample analysis ............ 50

Table 15: Concentration (ng/L) of pharmaceuticals in Nairobi River basin ....................................... 50

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LIST OF FIGURES

Figure 1: Quantity of drugs ordered and supplied by KEMSA to public health institutions in the

Nairobi region in 2008. ................................................................................................................................... 3

Figure 2a: Outpatient antibiotic use in European countries in 2008 ........................................................ 7

Figure 2b: Antiviral consumption in outpatient settings for 15 participating European countries in

2008 ……......................................................................................................................................................... 7

Figure 3: Possible sources and pathways for pharmaceuticals and personal care products (PPCP) in

the aquatic environment ................................................................................................................................ 9

Figure 4: Ranges of concentrations reported in literature for some of the frequently detected

pharmaceuticals in wastewater effluent (a) and surface water (b) .......................................................... 10

Figure 5: Scheme of sample preparation and instrumental analysis of PAIs in the environment……18

Figure 6: SPE procedure ............................................................................................................................. 19

Figure 7: Schematic diagram of a mass spectrometry system ................................................................. 21

Figure 8: Mechanism of ESI ....................................................................................................................... 21

Figure 9: Schematics of (a) a quadrupole and (b) an ion trap mass analyzers ...………………………22

Figure 10: Schematics of a triple quadrupole configuration ................................................................... 23

Figure 11: Schematics of (a) a magnetic sector analyzer and (b) a time-of-flight analyzer ................ 24

Figure 12: Map of the Nairobi River basin showing the sampling sites ................................................. 29

Figure 13: SPE cartridge sample … ............................................................................................................ 31

Figure 14: SPE extraction set-up ................................................................................................................. 31

Figure 15: Schematic diagram of the HPLC-HRMS sequence ................................................................ 32

Figure 16: Instrumental analysis set-up (AmberLab, UGent) .................................................................. 34

Figure 17: Scheme of selection criteria for focus compounds ................................................................. 42

Figure 18: Sample TIC obtained by the analysis of Group A in MID and escan mode (site 5) ......... 45

Figure 19: Sample XIC for metronidazole in escan and MID mode (Site 5) ..................................... 45

Figure 20: Schematics of compound identification criteria ................................................................ 46

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ABBREVIATIONS AND NOTATIONS

AOP Advanced Oxidation Processes

ATC Anatomical Therapeutic Chemical System

BCS Biopharmaceutical Classification System

BOD Biological oxygen demand

COD Chemical oxygen demand

DDD Defined Daily Doses

DID Defined Daily Doses per 1000 Inhabitants per day

Escan Electric scan

GDP Gross Domestic Products

HLB Hydrophilic-hydrophobic balance

HRMS High resolution mass spectrometry

KEMSA Kenya Medical Supplies Agency

LC50 Lethal concentration

LOEC Lowest observable effect concentration

MID Multiple ion detection

NGO Non Governmental Organizations

PAI Pharmaceutically active ingredients

PPCP Pharmaceutical and personal care products

SPE Solid-phase extraction

SPME Solid-phase microextraction

TDS Total dissolved solids

tR Retention time

TSS Total suspended solids

WARMA Water Resources Management Authority

WHO World Health Organization

WHOCC World Health Organization Collaborating Centre for Drug Statistics Methodology

WWTP Wastewater treatment plant

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Abstract


ABSTRACT

Human pharmaceuticals are fast becoming an environmental concern. Their occurrence in the

environment has been reported in many countries especially the Western world. They have been

reported to have eco-, geno- and human toxic effects and thus, their importance as micropollutants

cannot be ignored. However, African countries are still lagging behind in research concerning these

micropollutants. In view of this fact, this dissertation presents on one hand, an overall view of the

water quality and on the other hand, a first study on the occurrence of pharmaceutical residues in the

Nairobi River basin, Kenya.

Physical-chemical parameters of water samples were analyzed. Chemical Oxygen Demand (COD)

and Biological Oxygen Demand (BOD5) values of up to 594 mg/l and 460 mg/l were registered,

respectively. Other parameters analyzed included total suspended solids (480 mg/L), conductivity

(1080 µS/cm) and total dissolved solids (670 mg/L) among others.

Based on the pharmaceutical consumption quantity in the Nairobi region, prioritization criteria for

the selection of target compounds to be analysed has been developed. Initially, 43 priority

compounds were selected, downsized to 14 focus compounds after first screening. These focus

compounds represent six pharmaceutical classes: antibiotics, analgesics/anti-inflammatory drugs,

antiretrovirals, antimalarials, and antipsychotics.

A new multi-residue analytical method based on solid-phase extraction (SPE) and high resolution

magnetic sector mass spectrometry (HRMS) coupled to high performance liquid chromatography

(HPLC) has been developed for determination of the selected human pharmaceuticals in Nairobi

River basin, Kenya. Given the novelty of this HRMS based analytical method, a stepwise structured

methodology for compounds identification has been developed. Compounds are first identified by

analysis in electric scan (escan) mode and then confirmed in a mass selective multiple ion detection

(MID) mode. Based on the HRMS measured parameters (retention times, ion intensity and mass

accuracy), criteria for the identification of detected compounds has been developed. On this basis,

three categories of compounds were defined i.e. positively, probably and indicatively identified

compounds. The former represents compounds that are accurately identified because they met all the

conditions for accurate confirmation. The latter two classes represent compounds which didn‘t meet

all these criteria necessitating unequivocal identification via analytical standards. Five compounds

(nevirapine, paracetamol, sulfamethoxazole, sulfadoxine and trimethoprim) were positively

identified, while two (ibuprofen and zidovudine) were identified as probable compounds and another

five (amoxicillin, efavirenz, carbamazepine, lamivudine and metronidazole) were considered to be

indicatively present. Methyldopa and benzylpenicillin were not identified in all the samples

analysed.

Full confirmation and approximative quantification of the identified pharmaceuticals was achieved

by the analysis of the corresponding analytical standards. All the identified focus compounds in

escan and MID were unequivocally confirmed to be the actual focus compounds except efavirenz

and zidovudine whose retention time couldn‘t match that of the analytical standards.

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Abstract


Quantitatively, sites in the informal settlement and industrial areas showed high detection frequency

and concentrations of the focus compounds. Antibiotics were the most common class of

pharmaceuticals detected being represented by four compounds. Nevirapine was the most ubiquitous

compound being identified in 88% of the sampling sites with a mean concentration of 918 ng/L. The

other compounds which have been detected in the samples include paracetamol (50%, 8.7 µg/L),

metronidazole (63%, 286 ng/L), trimethoprim (75%, 2 µg/L), sulfadoxine (63%, 718 ng/L),

sulfamethoxazole (75%, 8.5 µg/L), lamivudine (75%, 1 µg/L), carbamazepine (75%, 251 ng/L) and

amoxicillin. The WWTP plant effluent contains pharmaceuticals thus adds burden of pharmaceutical

contamination to the Nairobi River.

This study has, for the first time, brought forward qualitative and quantitative data on the occurrence

of pharmaceutical residues in the Kenyan waters through the development of an advanced and

innovative analytical method. It has not only demonstrated that Kenyan rivers are heavily

contaminated with pharmaceutical residues as exemplified by their high concentrations, but has also

revealed the occurrence of new classes of pharmaceuticals (antiretrovirals and antimalarials) in the

environment. These pharmaceuticals are used in large amounts, yet little or no studies have been

done on their occurrence and fate in the environment. The study, therefore, presents a foundation on

which further research work can be developed.

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Chapter 1: Introduction


CHAPTER 1

INTRODUCTION

1.1 Background of the study

The ever increasing world‘s population has led to increase in demand for human pharmaceuticals in

equal measure. Similarly, new diseases continue to emerge even when health professionals put up

spirited effort to control the old diseases. As a result, new compounds are discovered and registered

everyday to help in combating the diverse kinds of diseases. In the rush to save human life, the fate

and effects of these pharmaceuticals in the environment have taken a backseat for a long time.

Coupled with lack of resources, powerful analytical techniques to study pharmaceutical compounds

in the environment in the past and limited information on their consumption, there had been less

interest in studying their environmental occurrence. However, in the recent past, awareness of

environmental problems and the contribution of these compounds (so called emerging pollutants)

have been increasing. Technological advancement in analytical instruments has not been left behind

either.

Pharmaceutical compounds are complex molecules with different physical-chemical and biological

properties and functionalities which are developed and used because of their more or less specific

biological activity (Kummerer, 2008). They are used to cure diseases, control pathogens and pests,

improve animal growth and production. The occurrence of pharmaceutically active substances

(sometimes referred to as pharmaceutically active ingredients – PAIs) in the environment has

become an important issue in the last few years (Gros et al., 2006). This increased interest emanates

from the concern that these compounds may have negative impacts on ecosystems. These concerns

have been vindicated by several studies which have reported negative effects of some

pharmaceuticals on aquatic organisms (Carlsson et al., 2006; Christen et al., 2010; Morley, 2009;

Zurita et al., 2007). Their presence in drinking water has also raised concern on the risk they pose to

human health. Moreover, pharmaceutical compounds can exert significant geno-, eco- or human

toxic effects, even at low concentrations due to their high biological activity.

Occurrence of these compounds in the aquatic systems has been reported by many researchers

(Alonso et al., 2010; Gros et al., 2006; Kummerer, 2008; Zuccato et al., 2010). Pharmaceuticals

once taken are eliminated from the body either as the parent compound or as biotransformation

products. They are then excreted in urine, feces and sweat which is disposed in domestic sewers and

finally into wastewater treatment plants (WWTPs) or surface water. These studies, however, have

been done in the developed world. In Africa, there is very little information on the occurrence of

drugs in the environment. In Kenya, for instance, to the best of this author‘s knowledge, no such

study has ever been done. This work focuses on the study of the occurrence of pharmaceutical

compounds in Nairobi River basin, Kenya.

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This work is organized into five chapters. Chapter 1 gives a brief introduction and justification of the

study. Chapter 2 provides a literature review which focuses on (i) the classification and consumption

of pharmaceuticals; (ii) their fate, occurrence, and ecological impacts, (iii) abatement technology to

remove them from the environment; and (iv) analytical technique to detect human pharmaceuticals

in the aquatic environment. The scope of the study is included in this chapter. In chapter 3, materials

and methods used in the study are outlined. Chapter 4 provides the results and discussion, their

interpretation and chapter 5 gives the recommendations and conclusions emanating from the study.

Finally, the last part contains the list of references cited in this work.

1.2 Justification of the study

As environmental awareness increases worldwide, countries are rushing to make policies to address

environmental pollution. Kenya is not left behind in this race. In order to make good and relevant

policies, scientific information is vital. This is not only important for addressing the sources of

pollution but also to prioritize dangerous compounds. Apart from conventional pollutants, emerging

micropollutants (e.g. pesticides, nano-sized particles, human and veterinary drugs, personal care

products) are becoming an important cause of environmental concern. Though there is limited

information on pharmaceutical consumption in Kenya, the Government reported that the public

sector medicine expenditure was US$ 16 million, representing quite a large portion of the

governmental budget. Combined with out of pocket expenditure on medicine by the people, it can be

concluded that the country consumes a lot of human drugs. Therefore, it is imperative that the

occurrence of these compounds in the Kenyan waters is determined for proper monitoring.

Using data from the Kenya Medical Supplies Agency (KEMSA), it was estimated that the drug

consumption was 9.2 g per inhabitant equivalents (I.E) per year in 2008 in the Nairobi region.

However, this represents only 70% of the demand and ordered amount of pharmaceuticals by the

governmental health institutions. This implies that another 30% is acquired from the private health

institution.

Due to many players (e.g. Non Governmental Organizations (NGOs), churches, private medical

practitioners, the government) in the health sector in the country, it is quite difficult to have an

accurate drug consumption rate. This notwithstanding, KEMSA data provides a good indicative

consumption rate. Figure 1 shows the estimated drug consumption in the Nairobi region. With rapid

population growth, the consumption is expected to grow rapidly. Due to poor waste management and

sewerage systems, these compounds are expected to occur in the Nairobi River basin, hence the need

for this study.

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Figure 1: Quantity of drugs ordered and supplied by KEMSA to public health institutions in the Nairobi

region in 2008.

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Chapter 2: Literature review


CHAPTER 2

LITERATURE REVIEW

2.1 Classification and consumption of pharmaceuticals

2.1.1 Classification of pharmaceuticals

Pharmaceutical drugs are classified in a number of ways depending on where and how the drugs are

being used (Thomas, 2006). The classification methods include chemical structure, pharmacological

action including site of action and target system, nature of illness and the body system in which the

drug acts (physiological classification), and route or mode of administration. The method of

classification adopted depends on the purpose of the information. Daughton et al. (2009) noted that

the Biopharmaceutical Classification System (BCS) which categorizes active pharmaceutical

ingredients (API) that are administered orally according to solubility, permeability, absorption and

solubilization of an API across the intestine has been employed by scientists. Gros et al. (2009)

classified pharmaceuticals according to their pharmacological action. Different methods have their

own shortcomings. For instance, classification according to chemical structures may group

compounds with different mode of actions together.

The most elaborate and commonly used classification system is the Anatomical Therapeutic

Chemical (ATC) classification system which is controlled by the World Health Organization

(WHO) Collaborating Centre for Drug Statistics Methodology (WHOCC). In the ATC classification

system, the active substances are divided into different groups according to the organ or system on

which they act and their therapeutic, pharmacological and chemical properties. Drugs are classified

in groups at five different levels. The first level of the code indicates the anatomical main group and

consists of one letter. Drugs are divided into fourteen main groups in this level. The second level

indicates the pharmacological/ therapeutic main group and consists of two digits. The 3rd

and 4th

levels are chemical/pharmacological/therapeutic subgroups and the 5th

level is the chemical

substance (http://www.whocc.no/atc/structure_and_principles/). For example, the complete

classification of aspirin illustrates the structure of the code as shown in Table 1.

Table 1: Classification of aspirin based on the ATC classification system

N Nervous system (1st level, anatomical main group)

N02 Analgesic (2nd

level, therapeutic subgroup)

N02B Other analgesics and antipyretics (3rd

level, pharmacological subgroup)

N02BA Salicylic acid and derivatives (4th

level, chemical subgroup)

N02BA01 Acetylsalicylic acid (5th

level, chemical substance)

http://en.wikipedia.org/wiki/World_Health_Organization

http://www.whocc.no/atc/structure_and_principles/

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The Anatomical Therapeutic Chemical (ATC) Classification System is, however, complex and can

have several codes for the same compound e.g. ibuprofen is C01EB16 (cardiac therapy),as well as

G02CC01 (gynaecological), M01AE01 (anti-inflammatory) and M02AA13 (topical products for

joint and muscular pain).

Antibiotics, due to their diversity and mode of action, have the highest number of subgroups based

on their chemical structures (e.g. penicillins, cephalosporins, quinolones, sulfonamides) as shown in

Table 2. However, it can also be based on spectrum of activity, mode of administration, and type of

activity (mechanism of action). Spectrum of activity is based on target specificity e.g. narrow-

spectrum (targets a specific type of bacteria such as gram-positive or gram-negative bacteria) and

broad-spectrum (targets a wide range of bacteria). Activity based classification identifies bactericidal

and bacteriostatic antibiotics. Bactericidal antibiotics kill bacteria by inhibiting metabolism, cell wall

synthesis and nucleic acid function or synthesis. Bacteriostatic antibiotics inhibit bacterial growth by

inhibiting protein synthesis. Antibiotics classification is important in understanding their fate in the

environment and thus inducibility of bacterial resistance which is of environmental concern

(Kummerer, 2004).

Table 2: Classification of selected antibiotics based on molecular structure (Wikipedia)

Antibiotic class Core structure Unique molecular structure

Penicillins Pencylpenicillin: R=C6H5

Phenoxyphenypenicillin: R=C6H5O

Amoxicillin: R=C6H8ON

Ampicillin: R=C6H8N

Meticillin: R=C6H9O2

Oxacillin: R=C10H8ON

Cephalosporins Cefacetril: R1=C3H5O2; R2=C2H2N

Cefradin: R1=CH3; R2=C7H12N

Cefroxadin: R1=CH3O; R2=C7H12N

Cefaclor: R1=Cl; R2=C7H10N

Cefalexin: R1=CH3; R2=C7H10N

Quinolones Ciprofloxacin: R1=H; R2=F; R3=C4H9N2;

R4=H; R5=C3H5

Rosoxacin: R1=H; R2=H; R3=C5H4; R4=H;

R5=C2H5

Sulfonamides

Sulfamethoxazole: R1=C6H6N; R2=H;

R3=C4H4NO

Sulfadoxine: R1=C6H6N; R2=H; R3=C4H4O2

R1

R2

R3

R2

R1

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2.1.2 Consumption of pharmaceuticals

The pharmaceutical industry has been flourishing over the years as the world‘s population grows and

new diseases emerge. A bigger percentage of the Gross Domestic Products (GDP) of various

countries is directed towards healthcare provision. In Kenya, for example, per capita medicine

expenditure was US$ 11 in 2003 (Ministry of Health, 2004). Global pharmaceutical sales are

projected to grow by 5-7% in 2011 hitting $880 billion compared to 4-5% in 2009 (IMS health,

2010; www: imshealth.com).

In 2005, the pharmaceutical sector value in Kenya was US$ 130 million (Kenya Pharmaceutical

Industry, 2005). These statistics give a rough idea on how much drugs are consumed globally. IMS

notes that USA remains the largest single market followed by Japan and China, respectively. In

2005, USA had 2.32 billion prescriptions (JJemba, 2008). There are no data available for the total

use of pharmaceuticals globally though sale volumes can be used for consumption estimates

(Kummerer, 2008). However, some researchers have reported estimates for different countries which

vary from region to region. These variations can be explained by differences in population and

population structure, geographical site, local legislation and laws, social believes and practices, and

economic factors.

Alder et al. (2010) noted that UK had the highest consumption of β-blockers in Europe at 3.2 g per

capita per annum (cap-1

a-1

) in 2001 while Finland had the lowest consumption (0.1 g cap-1

a-1

).

Cardiovascular drugs were among the top 200 most prescribed drugs in USA in 2005 (JJemba,

2008). Fent et al. (2006) observed that analgesics/anti-inflammatory compounds were the most

consumed drugs in Europe. Table 3 shows a summary of consumption of selected pharmaceuticals in

selected regions of the world.

Table 3: Pharmaceutical consumption patterns in different countries in tons/year (Fent et al., 2006).

Compounds

Austria

(1997)

Australia

(1998)

England

(2000)

Germany

(2001)

Switzerland

(2004)

Acetylsalicylic acid 78 20 n.a 836 44

Salicylic acid 10 n.a n.a 71 5

Paracetamol 35 295 391 621 95

Naproxen 5 22 35 n.a 2

Ibuprofen 7 14 162 344 25

Diclofenac 6 n.a 26 85 5

Carbamazepine 6 10 40 87 4

Ranitidine n.a 34 36 85 2

Metformin 26 91 206 516 51

n.a - not available

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Antibiotics consumption was recently estimated between 10,000 and 200,000 tons/year globally

(Zuccato et al., 2010). In Germany, about 302 tons of human antibiotics were discharged

(Kummerer, 2008). This value was higher than that estimated by Zuccato et al. (2010) of 7-14 tons

for annual antibiotic discharge in Italy.

The outpatient consumption of antibiotics for systemic use varied from 9.96 Defined Daily Doses

(DDD) per 1,000 Inhabitants per Day (DID) in the Russian Federation to 45.20 DID in Greece (total

care), with a median use of 19.70 DID (European Surveillance of Antimicrobial Consumption,

2008). Similarly, total outpatient systemic antiviral use in 2008 in 16 European countries varied by a

factor of 25.6 between the country with the highest (1.5 DID in the Netherlands) and the country

with the lowest (0.06 DID in Croatia) use. Figure 2a and 2b show the consumption of antibiotics and

antivirals in selected European countries.

KEY: GR-Greece, CY-Cyprus, IT-Italy, FR-France, BE-Belgium, LU-Luxembourg, LT-Lithuania, SK-Slovakia, HR-Croatia, PT-Portugal, IE-Ireland,

IL-Israel, PL-Poland, IS-Iceland, BGE-Bulgaria, S-Spain, FI-Finland, MT-Malta, CZ-Czech Republic, UK-United Kingdom, DK-Denmark, No-

Norway, HU-Hungary, SL-Slovenia, AT-Austria, SE-Sweden, DE-Germany, EE-Estonia, NL-Netherland, LV-Latvia, RU-Russian Federation

Figure 2a: Outpatient antibiotic use in European countries in 2008 (ESAC, 2008).

KEY: IT-Italy, LU-Luxembourg, SK-Slovakia, HR-Croatia, PT-Portugal, FI-Finland, CZ-Czech Republic,

DK-Denmark, NO-Norway, HU-Hungary, AT-Austria, SE-Sweden, EE-Estonia, NL-Netherland, SL-Slovenia

Figure 2b: Antiviral consumption in outpatient settings for 15 participating European countries in 2008

(ESAC, 2008).

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2.2 Pharmaceuticals in the environment

2.2.1 Sources of pharmaceutical residues in the aquatic environment

In the recent past, there has been an upsurge in human population growth; the majority has been

reported in the developing countries. With it has come the challenge for healthcare provision. New

diseases have emerged as well as the old ones creating more need for drugs. Due to this increased

production and consumption of pharmaceutical drugs, there has been increasing concern in their fate

and effect in the environment as reports show their presence in the aquatic systems.

The pharmaceuticals find their way into the aquatic systems in various ways (Figure 3). After the

drugs have been taken, they can undergo biotransformation such as conjugation in the body. Some,

however, remain unchanged. The metabolites and unchanged compounds are eliminated from the

body through urine, feces and sweat. The eliminated drugs get into the domestic wastewater system

through excretion in urine or feces. Washing away of excretions through sweat and topically applied

medication during bathing and disposal of unused or expired drugs into the sewers also contributes

to the pharmaceutical drugs load in the domestic wastewater. A survey conducted in the USA showed

that unused or expired medicines are generally disposed of through the trash, toilet and sink rather

than by return to pharmacies (Kotchen et al., 2009).

Effluent from wastewater treatment plants (WWTP) is the main source of pharmaceutical drugs in

the aquatic systems. Most biological wastewater treatments do not completely remove therapeutic

compounds which flow into surface waters and eventually into ground waters (Chang et al., 2011;

De Graaff et al., 2011). A study carried out in Italy (Castiglioni et al., 2004) found out that on

average the efficiency of the WWTPs to remove therapeutic drugs was less than 50%. These findings

buttress rising belief that much of the drugs consumed could be getting into the aquatic systems

through effluent discharges from WWTPs.

Leakage from septic tanks and landfills also contributes to contamination of both ground and surface

water. In Kibera slums, Kenya, for instance, pit latrines are constructed on the river banks which

makes it easy for the fecal matter to leak into the rivers. In some cases, the latrines not only leak but

are also emptied into those rivers once they get filled up.

Surface water run offs carry with it these compounds during rain from the solid waste dump sites and

agricultural lands where sewage sludge has been applied as manure. Pharmaceutical drug

manufacturing facilities have been reported to introduce PAIs into the aquatic system. In Taiwan, the

highest drug contaminations were found in domestic waste streams (37.5%) followed by animal

husbandry (27.9%) and drug production facilities (23.4%) (Lin and Tsai, 2009). Hospital waste

discharges are also sources of PAIs in the environment.

In many countries, sewerage systems and solid waste management is very poor. As a result, there is

direct discharge of raw sewage and industrial wastewater into the surface water. Direct dumping of

wastes into the rivers in developing countries is a common feature.

9 | P a g e



Dumped together with the solid waste are expired or unused drugs into such waters. Raw sewers

discharged into the aquatic system through sewer bursts, intentional discharge or sewage farms

exacerbate the problem.

Figure 3: Possible sources and pathways for pharmaceuticals and personal care products (PPCP) in the

aquatic environment (JJemba, 2008)

2.2.2 Occurrence of pharmaceuticals in the environment

Over time, the capita consumption of pharmaceutical compounds and range of choices has steadily

increased as more natural and synthetic compounds are discovered. The increase has also coincided

with the detection of these compounds in the environment (JJemba, 2008). This has spurred research

interest focusing on the foot print of pharmaceutical drugs in the aqueous environment. Recent

studies have reported a wide range of concentrations of about 80–100 pharmaceuticals from many

classes of drugs (anti-inflammatory drugs, beta-blockers, antiepileptic, lipid regulators, antibiotics,

etc.) and some of their metabolites in many countries in treated sewage, rivers and creeks, seawater,

groundwater and even drinking water (Fent et al., 2006). Figure 4a and 4b show concentration

ranges of selected drugs detected in waste and surface water, respectively.

PPCP Production plants

Veterinary drugs Human drugs

Aquaculture Feedlot

refuse

Manure

Sediments Soil

Disposal

Excretion

Pharmacy

Sink/Toilet

Incineratio

n

Landfill WWTP

Groundwater Surface water

Solid waste Runoff

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Figure 4: Ranges of concentrations reported in literature for some of the frequently detected pharmaceuticals

in wastewater effluent (a) and surface water (b) (Gros et al., 2006).

Antibiotics

Due to their suspected effect of inducing bacterial resistance in the environment and their diverse

application both in human and animal treatment, antibiotics have attracted intensive research interest

from many scientists who have reported their occurrence in the aquatic environment (Spongberg et

al., 2008; Terzića et al., 2008; Wille et al., 2010; Zhang et al., 2007). For instance, in surface water,

Zuccato et al. (2010) detected amoxicillin, ciprofloxacin, sulfamethoxazole and erythromycin in

concentration levels ranging from 0.8 ng/L to 38 ng/L. However, Nödler et al. (2010) reported a

higher mean concentration level of 93 ng/L for sulfamethoxazole in surface water. Moreover, Gros et

al. (2009) reported antibiotic concentration levels ranging from below detection limit to 109 ng/L in

surface water.

Lin and Tsai (2009) reported maximum concentrations of 7.4 µg/L and 1340 µg/L of

sulfamethoxazole in effluent from hospital and pharmaceutical production facilities, respectively.

Trimethoprim and lincomycin have been reported in effluent with a maximum concentration of 95

µg/L (hospital WWTP) and 44 µg/L (pharmaceutical facility WWTP), respectively (Sim et al.,

2011).

Grujić et al. (2009) detected azithromycin with concentration levels in the range of 25 – 140 ng/L in

ground water, thus giving credence to the belief that pharmaceutical compounds infiltrate the ground

water aquifers.

a b

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Analgesics and anti-inflammatory drugs

Occurrence of anti-inflammatory drugs and analgesics in wastewater and surface water has been

extensively studied. Concentration levels ranging from few ng/L to several µg/L have been reported

(Camacho-Muñoz et al., 2010; Hao et al., 2006; Kasprzyk-Hordern et al., 2007; Lajeunesse et al.,

2007; Lin and Tsai, 2009; Payan et al., 2010).

Diclofenac has been reported in WWTP effluent ranging from 217 ng/L to 5.1 µg/L (Al-Odaini et al.,

2010; Sebȍk et al., 2008; Stülten et al., 2008). Ketoprufen and ibuprofen have been reported in the

range of 2.2 to 2666 ng/L and 2.4 ng/L to 139 ng/L in effluent; less than 2.4 ng/L to 13.6 ng/L and 2

ng/L to 610 ng/L in surface water, respectively (Araujo et al., 2008; Togola et al., 2007). Moreover,

many researchers have reported paracetamol in surface water registering concentrations of up to 9.2

µg/L (Lin and Tsai., 2009). Wille et al. (2010) reported concentration levels of up to 850 ng/L for

salicylic acid, the deacylated, more active form of acetylsalicylic acid in sea water.

Similarly, maximum concentration of 400 ng/L and 6 ng/L have been reported for phenazone and

diclofenac in drinking water, respectively (Jones et al., 2005). These findings are quite expected as

analgesics and anti-inflammatory drugs are highly consumed all over the world because they are

easily accessible as they can be administered over the counter without prescription.

Antiviral and antiretroviral drugs

There is limited information available on the fate and impact of antiretroviral drugs in the

environment (Germer and Sinar, 2010). However, Prasse et al. (2010) detected acyclovir, lamivudine,

penciclovir, stavudine, zidovudine, nevirapine, oseltamivir and abacavir in domestic wastewater

influent in concentration range of 5 – 1780 ng/L. The study also reported a removal efficiency of 87

– 99% for abacavir, acyclovir, lamivudine, penciclovir, and stavudine in WWTP (activated sludge

system). Nevirapine and oseltamivir concentrations increased after treatment.

In the same study, oseltamivir, acyclovir and zidovudine were detected in river water with

concentrations up to 17, 190 and 170 ng/L, respectively. Since antiretroviral drugs are mostly used in

the treatment of HIV/AIDS related illnesses, their consumption might be higher in the developing

nations where there is high HIV/AIDS prevalence. However, there is little information on their

occurrence as many researchers, most of whom come from the developed nations, focus on

compounds which are highly used in these nations.

Beta-blockers

Beta-blockers have been detected in wastewater, surface water and drinking water ranging from less

than the level of quantification up to µg/L (Camacho-Muñoz et al., 2010; Lin and Tsai, 2009; Miège

et al., 2006; Trenholm et al., 2009). In Europe and North America, for example, β-blockers have

been reported with concentrations ranging from a few ng/L up to 2.2 µg/L in river water (Alder et

al., 2010). Fick et al. (2009) detected 240 ng/L of metoprolol in river water.

12 | P a g e



In domestic wastewater effluent, Alder et al. (2010) detected 1330, 330, 240 and 70 ng/L of atenolol,

sotalol, metoprolol, and propranolol, respectively. Similarly, Bueno et al. (2007) reported atenolol,

sotalol, and propranolol with maximum concentration levels of 4850, 155 and 100 ng/L,

respectively, in domestic wastewater effluent.

Blood lipid regulators

Due to a change in lifestyle and other factors, there has been upsurge of cases of diabetes and related

illnesses. The World Health Organization (WHO) estimates that 220 million people suffer from

diabetes worldwide (http://www.who.int/mediacentre/events/annual/world_diabetes_day/en/). These

figures show that consumption of blood lipid regulating drugs is rising and therefore their occurrence

in the environment can be quite expected. Clofibric acid, gemfibrozil and bezafibrate are the most

reported and studied compounds in aquatic systems (Camacho-Muñoz et al., 2010; Lajeunesse et al.,

2007; Sacher et al., 2008; Terzića et al., 2008).

Scheurer et al. (2009) measured concentration levels ranging from below 100 ng/L up to 1700 ng/L

and from 2 µg/L up to 21 µg/L for metformin in surface water and domestic wastewater effluent,

respectively. Kasprzyk-Hordern et al. (2008) detected clofibric acid, bezafibrate, simvastatin and

pravastatin in a concentration range of less than 0.3 to 128 ng/L in surface water.

Antipsychotic drugs

Several studies have shown the occurrence of psychoactive drugs in wastewater, surface and

drinking water. Carbamazepine has been reported in both surface and wastewater in the USA,

Canada, Europe and Asia (Gros et al., 2009; Kim et al., 2009; Santos et al., 2010; Zhao et al., 2010).

In Dõnana Park (Spain) main watersheds, Camacho-Muñoz et al. (2010) reported carbamazepine

mean concentrations of 1.1 µg/L. Similarly, Nödler et al. (2010) reported 26 ng/L of carbamazepine

in sea water sample. Fluoxetine, nordiazepam, citalopram, oxazepam and venlafaxine with

maximum concentrations of 44, 76, 120, 129 and 387 ng/L, respectively, have been reported in

surface water in Spain (Alonso et al., 2010).

Contraceptives

In the recent years, various researchers have reported the presence of contraceptives in the aquatic

environment. Al-Odaini et al. (2010) detected up to 38 ng/L of levonorgestrel in surface water. In the

same study, concentrations below the detection limit were reported for 17α-ethinylestradiol which is

quite different from findings by Brossa et al. (2005) who reported concentration levels of up to 130

ng/L of 17α-ethinylestradiol in surface water.

A similar study by Chen et al. (2007) reported maximum concentration levels of 38 ng/L of 17α-

ethinylestradiol and 39.1 ng/L of estriol in domestic WWTP effluent, respectively. These values are

closely similar to those reported by Pailler et al. (2009) for effluent.

http://www.who.int/mediacentre/events/annual/world_diabetes_day/en/

13 | P a g e



Pedrouzo et al. (2009) reported 35 ng/L of estrone 3-sulphate (conjugated form of estrone) in

effluent. This finding shows that pharmaceutical compounds do not only exist in the aqueous system

in pure forms but also in conjugated forms. Bartelt-Hunt (2011) reported estrone and other steroid

hormones in ground water with concentrations of up to 390 ng/L. Even though their consumption is

not high, their occurrence in the aquatic environment is inevitable due to their low biodegradability

as well as poor removal efficiency by conventional (waste) water treatment techniques.

2.2.3 Fate of pharmaceuticals in the aquatic environment

Several researchers have elaborated on the sources and pathways through which pharmaceuticals

enter into the aquatic environment (Daughton et al., 2009; Kummerer et al., 2004; Li et al., 2008;

Terzića et al., 2008). The compounds can get into the environment as a parent compound, metabolite

or conjugates. Once in the aquatic environment, the compounds are subjected to several processes

that may lead to their eventual elimination. These processes include adsorption, complexation,

photodegradation, biodegradation, deconjugation and hydrolysis.

Adsorption

Pharmaceutically active ingredients (PAIs) can undergo adsorption in WWTPs and the environment.

Gartiser et al. (2007) noted that in WWTPs, elimination by sorption on activated sludge is probably

of high relevance for tetracycline antibiotics. Kummerer (2008) observed that some compounds such

as quinolones or tetracyclines are eliminated by more than 50% by sorption to sewage sludge.

Adsorption depends on physical-chemical properties of the compound as well as the nature of the

sludge and the wastewater.

Complexation

The aquatic environment contains various complexation ions such as calcium. These ions may react

with pharmaceutical compounds forming complexes that are precipitated out of the water column.

Complexation can also increase the sorptivity of such compounds, thus facilitating their removal by

sorption. High amounts of hardness ions in WWTPs may partially explain the removal of

tetracyclines due to formation of complexes (Gartiser et al., 2007).

Photodegradation

Recent studies have shown that photodegradation of pharmaceuticals occurs in the aquatic

environment. It occurs through direct and indirect photolysis. In direct photolysis, a compound

absorbs sunlight which leads to its degradation. During indirect photolysis, compounds react with

species formed as a result of photolysis mainly of dissolved organic matter. Such species include

singlet oxygen; hydroxyl radicals and photoexcited organic matter (Razavi et al., 2011).

Razavi et al. (2011) reported that photodegradation of atorvastatin is a major mechanism in

determining its overall environmental fate. Exposure to sunlight facilitates the decay of

fluoroquinolone antibiotics in the euphotic zone of surface waters (Ge et al., 2010).

14 | P a g e



However, the extent to which any photochemical reaction occurs in natural waters depends on

variables such as the amount of dissolved organic matter, latitude, flow regimes, time of day and

year. Ryan et al. (2011) noted that both direct and indirect photolysis of sulfamethoxazole and

trimethoprim occur in wastewater effluents exposed to sunlight. Bartels and Von Wolf (2008)

concluded that the disappearance of oseltamivir carboxylate in the aquatic environment seems to be

a result of indirect photolysis.

Biodegradation

Biologically mediated processes can also result in the partial transformation or complete

mineralization of PAIs in the aquatic environment (Kummerer, 2008). Chang (2011) observed that

most androgens and progestogens are eliminated from waste water by biodegradation. In a review of

the fate and removal of estrogen compounds from municipal waste water, Racz and Goel (2009)

noted that estrogens are biodegraded through degradation as a carbon source for heterotrophic

bacteria, cometabolism with nitrifying biomass, and/or other cometabolism mechanisms in water.

Deconjugation

Deconjugation is a process by which pharmaceuticals in conjugated form are eclipsed once the

compound is in the aquatic system. This process normally leads to higher concentration of such

compounds in effluent compared to influent. Prasse et al. (2010) attributed the high concentration of

nevirapine and oseltamivir in effluent as compared to their concentration in the influent to their

deconjugation during the treatment.

Hydrolysis

Several compounds undergo hydrolysis in aquatic environment. Bergheim et al. (2010) reported that

penicillin G is readily hydrolysed at 20 0C, but the degradation rate is reduced at 5

0C. These

findings together with high adsorptivity explain why pecillins are rarely reported in surface water

and effluents.

2.2.4 Toxicological and Ecological impacts of pharmaceuticals

With so many new PAIs being registered every year, carrying out environmental impact studies on

each compound has been a challenge. Despite of this limitation, many researchers have recently

embarked on such studies and their findings are startling (Carlsson et al., 2006; Li et al., 2011a;

Mortensen and Arukwe, 2007).

Various reviews have been published on the toxicity and ecotoxicity of PAIs (Fent et al., 2006;

Morley, 2009; Santos et al., 2010) noting that drugs may induce unexpected effects in non

mammalian organisms which may lead to disturbance of the reproductive and hormone systems,

immune depression and neurobehavioral changes. Other effects include the development of resistant

bacteria strains and mutations.

15 | P a g e



The presence of PAIs in the environment also poses risks to humans such as the effect on human

embryonic cells, human blood cells and human breast cancer cells (Razavi et al., 2011).

Antibiotics are believed to induce the development of resistant bacterial strains. Li et al. (2009)

found a significant difference in proportions of multiresistant bacterial strains in upstream and

downstream samples from penicillin production facility. The higher number of resistant strains

downstream could only be explained by the effect of penicillin residues in the wastewater discharged

into the stream.

Quinn et al. (2008) showed that gemfibrozil and ibuprofen were the most toxic pharmaceuticals

among the compounds tested with a Lowest Observable Effect Concentration (LOEC) of 1 mg/L and

a lethal concentration (LC50) value of 22 mg/L to Hydra attenuate. The report noted that these

compounds had chronic effects such as decrease in feeding, substrate attachment and change of

morphology on the Hydra. Taggart et al. (2007) attributed the decline in Indian gyps vulture

populations to diclofenac induced renal failure after scavenging on the carcasses of cows and goats

treated with diclofenac just before their death.

Effects of PAIs in fish have also been reported quite often. Fish most often share drug targets with

humans (Fick et al., 2010) thus can easily be affected by human drugs. Rainbow trout

(Oncorhynchus mykiss) exposed to 1, 27 and 270 µg/L of verapamil showed oxidative stress and

elevated levels of the plasma ammonia concentration, implying that it impaired the detoxification

process of ammonia (Li et al., 2011a). A similar study with carbamazepine showed a 96 hour LC50

of 20 mg/L on rainbow trout (Li et al., 2011b).

Wastewater treatment plants depend on bacteriological activities to operate effectively. Any

disturbance of the microbial communities can hinder their efficiencies greatly. The presence of PAIs

in wastewater has been shown to interfere with the treatment process. Ketoprofen, naproxen,

carbamazepine and gemfibrozil inhibited nitrite production in the ammonia oxidizing bacterium N.

europaea at concentrations of 1 and 10 µM (Wang et al., 2011). It was noted that surviving cells had

reduced activity, suggesting a long term effect on the ammonium oxidizing bacteria function.

Carucci et al. (2006) showed that ranitidine and the antibiotic lincomycin inhibited ammonia

degradation up to 78% in an activated sludge wastewater lab-scale sequencing batch reactor. High

ammonia concentrations in the aquatic system endanger the survival of fish.

2.3 Abatement of pharmaceutical pollution

To control environmental pollution by pharmaceuticals, proper management and utilization of drugs

is vital. Public awareness of the effects of pharmaceuticals on the environment, proper disposal of

unused or expired drugs and good manufacturing practices go a long way in combating the entry of

PAIs into the environment. However, once they enter the aquatic environment, end-of-pipe

techniques have to be employed to get rid of them. Different techniques exist for removal of PAIs

from both wastewater and drinking water.

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Main treatment methods are physical, chemical and biological techniques. These techniques can be

categorized into two major processes: conventional treatment systems and advanced treatment

processes.

2.3.1 Conventional treatment systems

Conventional treatment systems involve two stages, i.e. primary and secondary treatment. Primary

treatment involves physical removal of larger objects from wastewater before secondary treatment.

PAIs get sorbed onto the primary sludge. This fraction is then disposed together with the sludge.

The water from the primary treatment which contains mainly dissolved solids is then directed to a

secondary treatment where PAIs can be removed mainly through sorption and biodegradation

processes. Depending on the system design, the conditions can be aerobic, anaerobic or alternation

of aerobic and anaerobic. Lagoon (or oxidation pond) systems, fixed films and suspended films are

the main systems employed in secondary treatment (JJemba, 2008).

Though these systems have shown good efficiencies in the removal of some PAIs, other compounds

are not adequately removed. De Graaff et al. (2011) reported that only 29% of centrizine was

removed in an anaerobic-aerobic-anoxic treatment system. In the same study, the aerobic stage

removed 67% of metoprolol while diclofenac showed an increased concentration in the effluent. The

increased concentration shows that diclofenac exists in the aquatic system not only as the parent

compound but also as a conjugated metabolite. The conjugate can be eclipsed, thus releasing a full

compound of diclofenac, explaining its increased concentration in the effluent.

2.3.2 Advanced treatment systems

Several advanced treatment systems have been developed. These include advanced filtration

systems, ultraviolet treatment systems, electrolysis and advanced oxidation treatment systems.

Advanced filtration

Filtration systems are based on the exclusion of contaminants based on size or charge. However,

biodegradation of compounds also takes place as biofilms develop on the filter surface. Filters can be

either granular filters (e.g. granular activated carbon) or membrane filters (e.g. microfiltration (MF),

ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO)). Membrane filters are becoming

more promising in removal of micropollutants (JJemba, 2008). However, their application is still

limited due to the high cost of establishment. The pharmaceutical removal efficiency of membrane

filters vary for different PAIs. In a membrane bioreactor (MBR) study, Tambosi et al. (2010)

reported removal efficiencies of >99%, >98%, 86-89%, and 55-64% for acetaminophen, ketobrufen,

naproxen and sulfamethoxazole, respectively.

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Oxidation and advanced oxidation

Oxidation processes have been used in disinfection of water for a long time. Chlorination and

ozonation are the major processes employed. These oxidation processes have been found to

eliminate PAIs in water by many researchers (De Witte et al., 2008; Benner et al., 2008; Flyborg et

al., 2010; Khan et al., 2010). Acero et al. (2010) demonstrated that elimination of pharmaceuticals in

natural waters and secondary effluent through chlorination process varied from compound to

compound. It was the highest for amoxicillin (50% elimination in 6 minutes) and lower for

metoprolol (50% elimination in days). It was, however, noted that a high dose of chlorine and a wide

variation of pH was needed. A similar trend was reported by Benitez et al. (2009) in ozonation of

metoprolol, naproxen, amoxicillin, and phenacetin.

The need for effective techniques to abate pharmaceutical pollution has lead many researchers to

come up with new methods. Recently, combinations of various oxidation processes enhanced with

catalysts have been advanced. These new techniques known as Advanced Oxidation Processes

(AOPs) have gained interest among scientists and environmentalists. Researchers have reported

good performance of such systems in their recent findings (Trovó et al., 2011; Zhang et al., 2010;

Kim et al., 2009; Yuan et al., 2009; Van Doorslaer et al., 20011).

For instance, Miranda-García et al. (2010) showed that immobilized TiO2 under solar irradiation

removes up to >99% of most of the pharmaceuticals. According to Xu et al. (2009), melatonin

achieved a degradation efficiency of >99% by the UV/H2O2/Fe2+

process compared to 32% by the

UV process alone. It should be noted that other parameters such as degradation products are

important apart from removal efficiency. These processes, however, have drawbacks such as high

energy requirement in photolytic methods, great consumption of chemicals and extra expenses on

the disposal of Fe(OH)3 in Fenton and photo-Fenton processes. Table 4 shows some AOPs applied

for selected PAIs.

Table 4: Some PAIs treated using advanced oxidation processes (Klavarioti et al., 2009)

Compound Advance Oxidation Process % Removal

Diclofenac Photo-Fenton in pilot plant Complete in 100 min

200 mg/L TiO2/Artificial sunlight at 750 W/m2 Complete in 60 min

Sonolysis at 617 kHz, 90 W in the presence of 100 mg/L TiO2 85% in 30 min

Carbamazepine 100 mg/L TiO2/Artificial sunlight 75% in 9 min

10 mg/L H2O2/UV(200–300 nm) 90 % at 853 mJ/cm2

Sulfamethoxazole 100 mg/L TiO2/Artificial sunlight 88 in 360 min

Clofibric acid 0.01 mM Ozone Complete in 20 min

1 M H2O2/UVC (17 W) 90% in 60 min

17β-estradiol 1.5 mg/L Ozone 99% in 1 min

Electrolysis over boron-doped diamond at 25 mA/cm2, Complete in 8 min

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2.4 Analysis of pharmaceuticals in the environment

Determination of pharmaceuticals in the environment is an integrated procedure involving sampling,

sample preparation, and instrumental analysis (Figure 5). Sampling should be carried out in such a

way that the samples are good representative of the sample area. Sampling techniques and materials

employed should protect target compounds from removal/degradation through processes such as

adsorption and photolysis. Proper storage is necessary as well as treatment before final instrumental

analysis.

Figure 5: Scheme of sample preparation and instrumental analysis of PAIs in the environment (JJemba,

2008)

2.4.1 Sample extraction

Determination of PAIs in environmental samples is challenging owing to their diverse properties and

complex matrix. Developing a single technique to identify these compounds is thus difficult.

Therefore, a combination of techniques is often applied. Before instrumental analysis, PAIs are

extracted from the environmental samples using various techniques. The most commonly employed

technique for liquid samples is sorptive extraction, mainly solid-phase extraction (SPE). Other

techniques include liquid-liquid extraction (LLE). The extraction step is vital for the pre-

concentration and/or clean-up of samples as it improves the sensitivity and selectivity towards the

analytes of interest.

Sample pretreatment (solids) e.g.

grinding, homogenization, etc.

Extraction (using centrifugal forces, Soxhlet

extraction, ultrasonic waves, microwaves, etc.)

the solvent used depends on the target compound.

Clean up using solvent exchange or column chromatography with column

packed with alumina or silica gel.

Concentrate the sample using N2, vacuum

evaporation or rotary evaporation

Derivatization (if necessary)

Analysis using appropriate instrument

Sample pretreatment (liquids)

Filtration (if necessary)

Adjust pH to desired level (based

on target compounds)

Extraction using solid-phase extraction (SPE) or

liquid-liquid extraction (LLE). The solvent used

depends on the compound of interest.

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2.4.1.1 Solid-phase extraction (SPE)

The solid-phase extraction (SPE) or liquid-solid extraction technique (Figure 6) is based on the

principle that the analytes sorb onto a sorbent material due to their hydrophobic or ion-exchange

properties. Reversed-phase materials such as alkyl-modified silica or poly(styrene-divinylbenzene)

have been employed for extraction. However, the presence of a wide range of pharmaceuticals with

diverse polarities in environmental samples poses challenges to the use of this technique. Thus, new

sorbent materials with both ion-exchange and hydrophobic properties have been developed. An

indepth review of new materials in sorptive extraction has been published (Fontanals et al., 2007).

Hydrophilic-hydrophobic balance (HLB) sorbents are being developed to aid in multi-residue

extraction. Oasis HLB is the most commonly used sorbent in this category due to its ability to

simultaneously extract acidic, neutral and basic polar analytes at a wide range of pH values. It wets

easily and can be allowed to run dry without adversely affecting the extraction efficiency (Al-Odaini

et al., 2010; Grujic et al., 2009; Nödler et al., 2010; Pedrouzo et al., 2009). Oasis HLB had better

recoveries compared to a non-polar C18 sorbent for the majority of the compounds analysed by Gros

et al. (2006). However, this wide range reduces its selectivity and thus increases the presence of

interference compounds in the extract.

Oasis MCX, mixed reversed phase-cation exchange sorbent gave good recoveries for acidic

compounds, whereas recoveries for basic and neutral compounds were poor at pH 7. Mixed-mode

cation- and anion-exchange SPE sorbents in series have been used (Lavén et al., 2009). This

technique enabled separation of acidic, basic and neutral PAIs. It‘s, however, time consuming (Gros

et al., 2006). Solid-phase extraction (SPE) can be automated and used on-line. On-line SPE reduces

solvent consumption, cost and time of extraction (Trenholm et al., 2009). It eases portability and

storage. Other forms of SPE such as solid-phase microextraction (SPME) have been used for anti-

inflammatory drugs. It is simple, solvent free, reliable, flexible, and requires less sample volume

(Araujo et al., 2008).

Figure 6: SPE procedure (Van Langenhove and Demeestre, 2010)

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2.4.1.2 Liquid-liquid extraction (LLE)

The liquid-liquid extraction technique has been used for a long time. It‘s based on the principle of

organic compounds partitioning between the aqueous sample and an immiscible organic solvent. Its

use in environmental analysis is limited due to the use of large volumes of solvents and analyte loss

due to multistage operation (JJemba, 2008). It‘s prone to contamination, poses disposal challenge for

toxic solvents and tedious. Payan et al. (2010), however, used hollow fibre based liquid-phase

microextration (HF-LPME) for acidic pharmaceuticals and reported reduced solvent consumption,

faster speed, low cost, improved accuracy, selectivity and sensitivity compared to conventional

liquid-liquid extraction.

2.4.2 Separation

Chromatography is the most commonly used separation technique in environmental sample analysis.

Gas chromatography (GC) and liquid chromatography (LC) are the main methods applied. GC has

been used by many researchers (Sebok et al., 2008; Togola et al., 2007; Miège et al., 2006). In GC,

the target compounds are vaporized and eluted in a stream of gas (mobile phase) through a column

where they partition between the liquid stationary phase and the gaseous mobile phase. Though GC

based methods have high selectivity and resolution, good accuracy and precision, wide dynamic

range and high sensitivity; it requires derivatization for most polar compounds which prolongs time

of analysis, and use of toxic derivatizing agents. It‘s prone to wrong results due to incomplete

derivatization and decomposition of thermolabile compounds during analysis (Fatta-Kassinos et al.,

2011).

These drawbacks have lead to an increase in use of LC techniques (Nödler et al., 2010; Prasse et al.,

2010; Zuccato et al., 2010). However, LC coupled to MS is susceptible to matrix effects which may

result in ion suppression leading to reduced sensitivity, linearity, accuracy and precision of the

method (Fatta-Kassinos et al., 2011). Natural organic matter, salts, ion-pairing agents, non-target

contaminants are responsible for ion suppression (Gros et al., 2009). A higher number of surrogate /

internal standards compensates more accurately for matrix effects. Moreover, selective extraction,

sample clean-up and extract dilution can be employed (Gómez et al., 2007; Gros et al., 2006).

However, dilution can lead to a loss of sensitivity (Pailler et al., 2009).

2.4.3 Detection

Due to their complex matrix and the trace concentrations of PAIs, environmental samples require

highly sensitive and selective equipment for detection. Initially, fluorescence and UV spectroscopy

were used. However, these techniques were not suitable for multi-residue analysis. Mass

spectrometry is the most currently used technique for multi-residue analysis. It has high selectivity

and sensitivity as well as confirmation possibilities (Grujic et al., 2009). It can be coupled to HPLC

or GC (Jelic et al., 2011; Luo et al., 2011; Pedrouzo et al., 2009; Wang et al., 2011; Wille et al.,

2010; Zuccato et al., 2010).

21 | P a g e



2.4.3.1 Mass spectrometry

This technique is based on the principle that the sample transformed into gas phase is ionized. The

ions are accelerated into a mass analyzer for separation based on their mass-to-charge (m/z) ratio.

The ions then strike the sensor of a detector which measures their electric charge and amplifies the

weak ionic current. The spectrum from the signals sent by the detector is then displayed on a

computer system (Figure 7) (Hoffman et al, 2002).

Figure 7: Schematic diagram of a mass spectrometry system

Several ionization techniques are used in mass spectrometry. They include electron ionization,

chemical ionization, thermospray ionization, atmospheric pressure chemical ionization, electrospray

ionization and desorption ionization technique. Electrospray ionization (ESI) is the most commonly

used ionization method, particularly for PAIs in aqueous environmental samples. It has higher

sensitivity and less fragmentation than electron impact (Nödler et al., 2010). However, it is prone to

matrix effect which may reduce sensitivity and selectivity as a result of ion suppression or

enhancement. In ESI (Figure 8), tiny droplets of the aqueous sample are formed at the end of a

capillary which is exposed to a strong electric field (3-6 kV). The droplets then pass through a heated

inert gas or capillary to remove solvent molecules. Evaporation of the solvent causes the droplets to

shrink till the repelling coulombic forces nears their cohesive forces. This causes a cascade of

raptures leading to smaller droplets (Hoffman et al., 2002). Ions of the analyte are then liberated.

Figure 8: Mechanism of ESI (Van Langenhove and Demeestre , 2010)

Analyzers

Analyzers are used to filter the ions according to their mass-to-charge ratio before they are led to the

detector. There are different types of analyzers including quadrupole (Q), time-of-flight (TOF),

magnetic sector and ion trap analyzers. The commonly used mass analyzers are quadrupole (Q) and

ion trap (Figure 9).

Inlet: direct probe or

chromatograph Ion Source (Ionizer) Analyzer Detector Computer

22 | P a g e



Quadrupole analyzer

A quadrupole analyzer employs the stability of the trajectories in oscillating electric fields to

separate ions according to their mass-to-charge (m/z) ratio (Hoffman et al, 2002). It consists of four

rods with circular (or ideally hyperbolic) sections arranged in parallel. The opposing rods which are

electrically connected are supplied with direct current (DC) and a radiofrequency (RF). By adjusting

the DC and RF voltages, the ions entering the rods are separated. Ions with specific m/z ratio pass

through the rods while others are neutralized by striking on one of the rods.

Ion trap analyzer

In principle, an ion trap analyzer is similar to a quadrupole. It‘s like a three-dimensional quadrupole

having two end-cap electrodes and annular electrodes. Ions are trapped inside the volume between

the electrodes from where they are expelled to the detector according to their m/z ratio. Both

quadrupole and ion trap have low mass resolution and mass range (2000-4000).

a) b)

Figure 9: Schematics of (a) a quadrupole and (b) an ion trap mass analyzers (Van Langenhove and

Demeestre, 2010)

In mass spectrometry, mass resolution (ability to separate two ion signals from one another (e.g. at

10% height in a mass spectrum)) is critical. Resolution (R) is defined as R=m/Δm = m1/(m1-m2)

with m= mass and Δm= mass difference; m1 and m2 are (m/z) ratios of the first and second ion,

respectively, and m1>m2.

Due to their complex matrix, environmental sample analysis requires instruments with high

selectivity and sensitivity. To achieve this, two approaches are currently employed in mass

spectrometry.

a. Tandem mass spectrometry

This is a hybrid system where different analyzers are combined to achieve the desired result. Triple

quadrupole (QqQ) is the commonly used configuration.

23 | P a g e



A triple quadrupole instrument (Figure 10) consists of two quadrupole mass spectrometers in series

with a non mass resolving quadrupole between them. The first and third quadrupoles act as mass

filters while the middle quadrupole serves as a collision cell. Combinations of Q and TOF (QqTOF),

and combinations of Q and a linear ion trap (QqLIT) have also been reported (Buchberger, 2011).

QqTOF provides more suitable information for confirmation purposes as well as identification of

unknown compounds because of their ability to provide exact mass measurements. QqLIT is

appropriate for both quantification and confirmation (Gros et al., 2009).

Figure 10: Schematics of a triple quadrupole configuration (Van Langenhove and Deemestre, 2010)

The use of tandem mass spectrometry has led to the achievement of low method detection limits

(MDL) and limits of quantification (LOQ), though it may vary as a function of sample matrix and

recovery (Al-Odaini et al., 2010). For instance, Lavén et al. (2009) achieved a MDL of 4.8 ng/L for

enapril in effluent using LC-MS/MS (QqTOF) and serial mixed mode cation- and anion-exchange

SPE, compared to 0.7 ng/L obtained by Gros et al. (2009) using LC-MS/MS (QqLIT) and Oasis

HLB cartridges. It is important to note that the QqQ system is used only in target analysis and

therefore it is not possible to detect the analytes outside the targeted ones (i.e. specified mass).

b. High resolution mass spectrometry

High resolution mass spectrometry has been employed as an innovative alternative to achieve high

selectivity and sensitivity. They are used in full-scan analysis thus has the advantage of being used

for screening. Two types are commonly used. They are the time-of-flight (TOF) and magnetic sector

analyzers (Figure 11).

Time-of-flight (TOF) analyzer

Time-of-flight analyzers are based on the principle of the relationship between the mass of ions and

their velocity at a given kinetic energy. Since ions originating from the same source with the same

energy and accelerated at the same potential arrive at the detector at different times, accurately

measured times can be used to distinguish ions of different mass. This technique has high

transmission efficiency and thus high sensitivity.

24 | P a g e



Magnetic sector analyzer

In a magnetic sector analyzer, the ions enter a magnetic field where they are separated as they are

deflected as a result of a centripetal and centrifugal force. Ions are selected according to their

momentum. Ions of equal energy but different m/z ratio describe different radial trajectories when

passing through the magnetic field.

The magnetic field strength (magnetic scan) or the voltage (electric scan) can be varied so that ions

of different m/z ratios are collected successively by a detector placed at a fixed position behind the

slit (Equation 1). These analyzers have high resolution (up to 60,000) compared to the others.

𝑚

𝑧=

𝐵2. 𝑟2. 𝑒

2𝑉 (1)

with m/z: the ion‘s mass-to-charge ratio, B: magnetic field strength, r: radius of the curvature, e:

elementary charge, V: electrical field voltage

Kaufmann et al (2010) noted that it can be used in trace level detection and quantification of

compounds in challenging matrices. It has higher selectivity and also permits elucidation of

compounds based on exact masses and isotopic patterns.

a) b)

Figure 11: Schematics of (a) a magnetic sector analyzer and (b) a time-of-flight analyzer (www.kore.co.uk)

As shown above, many analytical techniques are employed in the analysis of pharmaceuticals in

aquatic environmental samples. However, high resolution magnetic sector mass spectrometry

(HRMS) has no reported applications for PAI analysis. Table 5 gives a summary of the commonly

used analytical techniques for detection of PAIs in environmental samples.

Ions from source

Monitor

slit Monitor

Mass

filtering

Lens

Energy

focussing

Detector

slit

To detector

Source slit

Double focussing

point

Lens

25 | P a g e



Table 5: Analytical methods for detection of pharmaceutical residues in environmental samples

Number of

compounds

analysed

Pharmaceutical class Matrix Extraction

technique

Separion

method

Detection

method

MS

Ionisation

mode

MS

analyzer

Detection limit

range (ng/L) Reference

8

Analgesics, contraceptives,

antibiotics, stimulants,

antipsychotics

Tap water, surface

water DLLME HPLC UV/VIS _ _ 100 – 55800 (M) Yao et al, 2011

43

Analgesics/anti-inflammatory

drugs, blood lipid regulators,

antibiotics, beta blockers,

psychiatrics, antihistamines

Wastewater SPE (HLB) HPLC MS/MS ESI QqLIT 0.2-25 (M) Jelic et al, 2011

16 Antibiotics Wastewater, surface

water SPE HPLC MS/MS ESI QqLIT 0.02-2.21(M) García-Galán et al, 2011

32 Steroid hormones, antibiotics Ground water,

wastewater SPE (HLB) HPLC MS/MS ESI QqQ 0.001-0.023 (M) Bartelt-Hunt et al, 2011

23 Steroid hormones Wastewater and

surface water SPE (HLB) HPLC MS/MS ESI QqQ 0.02-40 (M) Chang et al, 2011

55

Beta blockers, psychiatric drugs,

hormones, antihistamines,

cardiac agents, metabolites

Surface, drinking &

ground water SPE (HLB) UPLC MS/MS ESI QqLIT 0.01-40 (M)

Huerta-Fontela et al,

2011

24

Antibiotics, stimulants,

psychiatric drugs, anti-

inflammatory drugs

Wastewater SPE (HLB and

MCX) HPLC MS/MS ESI QqQ 1-122 (M) Sim et al, 2011

47

Analgesics/anti-inflammatory

drugs, blood lipid regulators,

antidepressants, antiulcers,

psychiatric drugs, beta blockers

and antibiotics

Wastewater and

surface water

SPE (HLB)

UPLC MS/MS ESI QqQ 0.4-170 (I) Gracia-Lor et al, 2011

16

Antibiotics, hormones,

analgesics, stimulants,

antiepileptics, X-ray contrast

media

Tap and surface water SPE (HLB)

HPLC MS/MS ESI Qtrap 0.1-9.9 (M) Wang et al, 2011

26 | P a g e



Table 5 continued…

9 Antivirals

Wastewater

and surface

water SPE (hydrophobic) HPLC MS/MS ESI Qtrap 0.2-100 (M) Prasse et al, 2010

3 Analgesics Wastewater HF-LPME HPLC MS/MS ESI QqQ 20-300 (M) Payan et al, 2010

4 Beta blockers

Wastewater

and surface

water

SPE (HLB) HPLC MS/MS ESI Ion trap n.r Alder et al, 2010

15

Beta blockers, psychiatric

drugs, Blood lipid

regulators, analgesics/

anti-inflammatory drugs,

anti-ulcers.

Wastewater SPE (MCX) HPLC MS/MS ESI QTOF 2.4-285 (M) Lavén et al, 2009

73


drugs, Blood lipid



antibiotics, antihistamines

Wastewater

and surface

water

SPE (HLB) HPLC MS/MS ESI QqLIT 0.1-25 (M) Gros et al,2009

4 Analgesic/ anti-

inflammatory drugs Wastewater

SPE (HLB) GC MS/MS ESI Ion trap n.r Sebok et al, 2008

18


drugs, Blood lipid



antihistamines, stimulants

Wastewater,

surface water,

drinking water

SPE (HLB and

MCX )

GC MS EI Q 0.1-28.6 (M) Togola et al, 2008

DLLME: Dispersive liquid-liquid microextraction; EI: Electron impact; HLB: Hydrophilic-lipophilic balance; HF-LPME: Hollow fiber liquid-phase microextraction; n.r: not

reported; ESI: Electrospray ionization; M: Method detection limit; I: Instrument detection limit; UPLC: Ultra-performance liquid chromatography.

27 | P a g e



2.5 Scope of the study

Water quality and scarcity of fresh water are environmental issues that gain priority in both policy and

scientific work. Apart from physical-chemical macroparameters giving information on the overall water

quality, there is more and more concern on the occurrence and effects of so-called emerging organic

micropollutants. In this context, residues of human pharmaceuticals have been reported to occur in the

aquatic environment posing risks to ecosystems and human health.

As reports on such occurrences trickle from various countries, Kenya still lags behind in this research

field. Therefore, the overall scope of this thesis was to investigate water quality in a selected area

around Nairobi, Kenya, and to develop and apply an innovative analytical method enabling the

detection of multi-class pharmaceutical residues in the selected Kenyan waters. To the best of the

author‘s knowledge, there is no study/report before on the occurrence of pharmaceutical residues in the

aquatic system of the nation. For this work, the Nairobi River basin was selected as the study area, and

eight specific sampling sites were defined along the main river and its tributaries.

Three specific objectives have been formulated:

A first goal was to obtain an overall view of the general water quality at the selected locations in the

Nairobi River basin. This is in cognizant of the water quality monitoring efforts carried out by the

Water Resource Management Authority (WARMA) of the Kenyan government. Different physical-

chemical parameters like chemical oxygen demand (COD), biological oxygen demand (BOD5), total

suspended and dissolved solids (TSS and TDS), nitrates and nitrites, alkalinity, turbidity, calcium,

magnesium and total hardness, were therefore determined in the collected water samples.

The second goal was to prioritize the target compounds to be focused on for organic micropollutant

analysis. Information on pharmaceutical consumption is scarce in Kenya and there is no analytical

method which has been developed to monitor occurrence of pharmaceuticals in the environment in the

country. A proper methodology had to be developed making use of the available data of the Kenya

Medical Supplies Agency (KEMSA) and based on clear decision criteria to define priority compounds

for trace analysis.

Third, the main scope of this work was to develop an innovative and advanced analytical method

making use of multi-residue extraction and liquid chromatography coupled to high resolution magnetic

sector mass spectrometry. The method had to enable both screening and target analysis possibilities in

order to investigate the occurrence of pharmaceutical residues in the Nairobi River basin. Since no

report on the use of magnetic sector mass spectrometry for this kind of purposes could be found in

literature, all steps of the method including the definition of identification criteria had to be developed

from the beginning. Whereas the initial goal was mainly to provide qualitative data on the occurrence

of selected pharmaceuticals, the use of analytical standards enabled also a first semi-quantitative

calculation of detected concentration levels.

28 | P a g e

Chapter 3: Materials and Methods


CHAPTER 3

MATERIALS AND METHODS

3.1 Chemicals

Acetonitrile, methanol, formic acid, ammonium acetate and deionised water (all HPLC-MS grade) were

purchased from Biosolve, The Netherlands. Hydrochloric acid (37%), sodium hydroxide and Na2EDTA

(disodium ethylene diamine tetra acetic acid) were purchased from Kobian Kenya limited. Analytical

standards (purity 98%) of nevirapine and efavirenz were obtained from Toronto Research Chemicals

(TRC, Canada), while standards (purity >95%) of ibuprofen, paracetamol, sulfadoxine,

sulfamethoxazole, carbamazepine, metronidazole, trimethoprim, lamuvidine, amoxicillin and

zidovudine were purchased from Sigma-Aldrich, Belgium. For quality control, ciprofloxacin (MP

Biomedicals, France), enrofloxacin and levofloxacin (Sigma-Aldrich, Belgium) standards with above

98% purity were used.

3.2 Sampling and sample preparation

3.2.1 Sampling area and sites

The Nairobi River basin is the sampling area of this study and is drained by three main rivers, i.e. the

Nairobi River (ca. 50 km), the Ngong‘ River (ca. 27 km), and the Mathare River (ca. 25 km). The

Nairobi River is the main river, while the Mathare and Ngong‘ Rivers are its tributaries. The three rivers

cut through the Nairobi city and its environments, and finally discharge into the Athi River. The rivers

are exposed to pollution from domestic, industrial, agricultural and hospital and/or clinical waste

discharges.

During the study, seven sampling sites (Figure 12) along the three rivers were chosen, based on their

exposure to pollution sources. As the rivers approach the populated areas of the city, the water quality is

expected to gradually deteriorate downstream. The eighth sampling site was on the effluent discharge

into the river from the Dandora wastewater treatment plant (WWTP). The WWTP uses waste

stabilization pond technology for treatment.

29 | P a g e



Figure 12: Map of the Nairobi River basin showing the sampling sites

Sampling site 1 (Ondiri swamp) is at the source of the Nairobi River. There are few human settlements.

Small-scale farming activities are carried out here. The water is visually clear and transparent.

Sampling site 2 (Outering road, Kariobangi) is characterized by the neigbourhood of partly sewered

residential houses. At this point, the river has gone through the heavily populated estates of Eistleigh,

Gikomba market and Kirinyaga Road, all contributing to domestic waste and industrial pollution (from

informal industries). However, the existence of the vast unutilized land for the Air Force Base gives the

river some time to undergo some self-purification before this point.

Sampling site 3 (Outering road, Riverside) is located within the Mathare informal settlements which is

mainly unsewered. Both clinics and the residents in the area discharge most of their waste into the river.

Sampling site 4 is approximately 300 m downstream from the confluence of the Nairobi River and the

Mathare River. It is also situated after the Dandora waste dump site. Major sources of pollution at this

point are the dump site, domestic sewers, and waste from the surrounding residential estates and

informal settlements upstream.

At sampling site 5, the Ngong‘ river has passed through the industrial area, the Mukuru and the Kibera

slums. Sources of pollution here are most probably the industrial waste discharges and domestic wastes

from the slum dwellers. Hospital waste from hospitals upstream could end up here due to seepage and

surface water run off.

Sampling site 6 is about 100 m downstream the confluence of the Nairobi, Mathare and Ngong‘ rivers.

Main sources of pollution are the domestic and industrial waste discharges from the industries along the

basin. Quarying activities also take place in the area.

6

2 5

3 1 4 8 WWTP

PP 7 6

1:385700

30 | P a g e



Sampling site 7 represents the effluent sample from the Dandora WWTP discharge. The efficiency of

the treatment plant determines the type of pollutants that can be detected in the river.

Sampling site 8 is about 500 m downstream from the effluent discharge point. The area has virtually no

human settlement. Pollution at this point is mainly expected to originate from upstream sources and the

WWTP effluent.

3.2.2 Sampling period and method

Sampling was done within a period of three days, i.e. the 27th

, 28th

and 30th

of July, 2010. All samples

were collected using pre-cleaned 2 L plastic bottles for the determination of the aqueous physical-

chemical parameters, and 1 L white plastic bottles for the organic trace analysis. The grab sampling

technique was used. Water was fetched from approximately 30 cm below the water surface by hand in

shallow areas, while a pre-cleaned plastic bottle with a hanging weight and a handle was used in deeper

sites. The samples were transported to the laboratory in an ice-cooled box and stored in a refrigerator at

4 0C awaiting further treatment within one day.

3.2.3 Sample preparation and extraction for organic trace analysis

Filtration and acidification

The samples were filtered first through a 1.0 µm GF/B Whatmann glass fiber filter (VWR, Belgium),

then through 0.45 µm Whatmann nylon filters (VWR, Belgium) under vacuum. The filtrates were

recollected in pre-cleaned and dry glass reservoirs. The samples were then acidified to pH 2.5 by

addition of concentrated hydrochloric acid (37 wt %) under stirring. The treated samples were kept in a

freezer at -20 0C till extraction was done within 6 days.

Solid-phase extraction (SPE)

The samples were taken out of the freezer and kept in the refrigerator overnight before the extraction.

From each sample, 500 mL was taken; its pH adjusted to 7 using concentrated sodium hydroxide

(NaOH), and 10 mL of a 5% (w/v) Na2EDTA solution was added. Oasis HLB (200 mg, 6 cc) SPE

cartridges were put on the vacuum system, conditioned first using 5 mL of methanol followed by 5 mL

of HPLC grade water (at 23-25 drops/minute) without vacuum. Oasis HLB cartridges (Figure 13) were

chosen due to their ability to simultaneously extract acidic, neutral and basic polar analytes at a wide

range of pH values (see section 2.4.1).

The samples were then loaded at a flow rate of 3-10 mL/min under vacuum. After that, the cartridges

were washed using 5 mL of HPLC grade water followed by vacuum drying for 15-20 minutes. The

cartridges were wrapped into aluminum foil and kept in the freezer at -20 0C till the day before

travelling. Then, they were kept in a refrigerator at 4 0C overnight before the travel date. At the Ghent

University, the cartridges were stored again at -18 0C prior to analyte elution. Figure 14 shows the

extraction set-up.

31 | P a g e



Figure 13: SPE cartridge sample Figure 14: SPE extraction set-up

(Van Langenhove and Demeestere, 2010)

Sample elution and reconstitution

Analytes were eluted with 2 x 5 mL of methanol using IST (International Sorbent Technology)

VacMaster-10 SPE equipment. The elution was carried out dropwise without vacuum. After elution, the

cartridges were dried under vacuum for 20 minutes. The eluted samples were evaporated to dryness at

25 0C with a gentle stream of nitrogen using Turbovap (Caliper Life Sciences) equipment at a pressure

of 5-12.5 psi. Drying was closely monitored to avoid volatilization and irreversible adsorption of the

analytes on the walls of the polypropylene vial. The dry extracts were reconstituted with 2.5 mL of

acetonitrile/water (10/90), vortexed and centrifuged using an EBA 20 centrifuge at 1000 rpm for two

minutes. After that, the reconstituted extracts were transferred into HPLC vials and stored at -18 0C for

further instrumental analysis.

3.3 Analysis of physical-chemical parameters

Physical-chemical parameters of the sampled water were determined according to the methods

developed by APHA (2005). Analyzed parameters included pH, conductivity and temperature (carried

out on site), chemical oxygen demand (COD), biological oxygen demand (BOD5), total suspended

solids (TSS), total dissolved solids (TDS), nitrates, nitrites, alkalinity, turbidity, calcium, magnesium,

total hardness, fluoride and iron.

32 | P a g e



3.4 Instrumental organic trace analysis

3.4.1 High Performance Liquid Chromatography (HPLC)

A Surveyor HPLC system (Thermo Finnigan, San Jose, USA) was used for separation of the analytes.

The analytes were separated on a Phenomenex Luna C18 (2) column (3 μm, 100Å (pore size) - 150 x 2.0

mm). The tray temperature and oven control were set at 15 0C and 35

0C, respectively. A binary mobile

phase consisting of methanol (A) and 0.1% formic acid in water (B) was used. The elution program was

as follows: the column was equilibrated using 10% A and 90% B for 1 minute. The gradient was then

increased rapidly to 20% A and 80% B in 0.05 minutes and held constant for 8.95 minutes. It was then

increased to 30% A and 70% B in 14 minutes, and then to 100% A in 10 minutes. It was kept constant

for 10 minutes, before returning to the starting condition in 1 minute. It was then equilibrated for 20

minutes prior to the next run, thus giving a 65 minutes run time. Partial loop injection was applied and

the injection volume was 10 µL. The mobile phase flow rate was 170 µL/minute.

3.4.2 Mass Spectrometry

To detect the compounds, a Thermo Finnigan double focussing magnetic sector MAT95XP–TRAP high

resolution mass spectrometer (HRMS) (Thermo Finnigan, Bremen, Germany) coupled to the HPLC

instrument was used (Figure 15). Ionization was done by electrospray ionization (ESI) in the positive

mode. The spray voltage was 3 kV. Nitrogen was used as both the auxillary and sheath gas, at settings

of 20 units and 4 bars, respectively. The lens voltage was 106-182 V, and the capillary temperature and

voltage were set at 250 0C and 4-84 V, respectively.

Figure 15: Schematic diagram of the HPLC-HRMS sequence

The instrument was operated in two different modes: (i) electric scan (escan) and (ii) multiple ion

detection (MID), both at a mass resolution of 8,000 (10% valley definition, corresponding to 16,000 at

full width at half maximum (FWHM)).

In the escan mode, a full scan measurement is aimed at. Full scan (all the ions in the spectrum of

interest can be measured) is important for screening because it allows determination of compounds

which were originally not targeted.

HPLC

Waste fraction

PEG 300/200 - solution

Mass spectrometer

Vinj = 10µL

Q = 170 µL/min Q = 170

µL/min Q = 50 µL/min

Q = 130

µL/min

Q = 40

µL/min Q = 10 µL/min

33 | P a g e



In the escan methodology, the instrument is set at the maximum accelerating voltage V (ca. 5 kV) and

the strength of the magnetic field B is adjusted to the value corresponding to the initial mass (m/z) to be

measured (see Equation 1 in Section 2.4.3). In the next step, the accelerating voltage is reduced in a

continuous way, so that all masses in a given m/z interval (if V decreases and B is constant, the

measured m/z increases) are measured one after each other. However, reducing the accelerating voltage

V also leads to a lower kinetic energy of the ions as defined by Ekin = z.e.V (where Ekin: kinetic energy,

V: accelerating voltage, z.e: total charge). Since the instrument is tuned at the maximum accelerating

voltage, this results into a decreased ion intensity. To overcome too pronounced intensity loss, smaller

mass ranges are scanned in different mass windows. The mass range within one mass window is based

on the rule that the highest mass should not be higher than 1.2 times the lowest mass. To ensure a good

mass accuracy, the scan speed is set at ≥ 15 sec/decade.

In the MID mode, instead of the full scan approach, a target analysis of a selected number of predefined

masses is aimed. This makes it the preferred technique for target analysis but not appropriate for

untargeted screening. Therefore, like in escan, the strength of the magnetic field is fixed at a given

value, corresponding to a targeted m/z at the maximum accelerating voltage. Then, the accelerating

voltage is reduced, not continuously like in the escan mode but stepwise, in order to obtain a

consecutive pass of the predefined masses (with a mass width around each targeted m/z depending on

the mass resolution of the instrument) through the mass analyzer. The instrument measures that region

where the ion of interest will appear while all the other regions of the mass spectrum are excluded from

the measurement. This leads to a better (at least one order of magnitude) signal-to-noise (S/N) ratio in

the ion current chromatograms. Therefore, for quantification purposes, the MID approach is used (see

Section 4.3.3). To obtain the best S/N ratio as well as at least 10 data points per chromatographic peak

(typical peak width in the order of 1 minute), the measuring time for each ion (i.e. the time during

which each ion is recorded during one cycle time) was set as close as possible to the maximum of 500

milliseconds. This resulted in a MID cycle time (i.e. the time between two consecutive measurements

of the same ion) between 0.9-2.8 sec.

For accurate mass measurement, polyethylene glycol mixtures with average molecular weights of 200

and 300 g/mol (Acros Organics, Belgium) dissolved in a methanol (0.5% formic acid), were used as

internal reference compounds. They were continuously introduced at a flow rate of 10 µL/minute into

the HPLC eluent before it enters the MS. PEGs provide each escan and MID mass window (mass

window in which the mass calibration takes place) with a specific lock and calibration mass. In every

single escan or MID measurement cycle time, the instrument automatically carries out an electric mass

calibration taking these two reference masses as calibration points. This ensures the most accurate,

selective and sensitive analysis of the target compound. ESI optimization (tuning) was done on a

selected [PEG+H]+ or [PEG+NH4]

+ ion.

In the final analysis of the focus compounds (see Section 4.3.2), their protonated molecular ion [M+H]+

or ammonium adducts [M+NH4]+ were measured. Based on the expected retention times determined

during escan analysis of a limited number of samples (see Section 4.3.1), time windows were defined in

the final detection method of the focus compounds.

34 | P a g e



This means that the total chromatographic run time is divided in different time sections, during which a

specific mass window is measured. In this way, the number of injections (analysis per sample) needed

to obtain measurements in a given m/z range, can be reduced drastically.

Considering the variation in their masses (molar mass range between 151 and 365 g/mol), retention

times and mode of detection (i.e. by the protonated ion or ammonium adduct), the focus compounds

(see Appendix I for molecular structures) were clustered into three subgroups. Subgroup A (containing

lamivudine, metronidazole, paracetamol, methyldopa, zidovudine, sulfamethoxazole, carbamazepine,

and nevirapine) covered a m/z range of 150-284 in 6 time windows. Subgroup B (trimethoprim,

amoxicillin, sulfadoxine and benzylpenicillin) represented the m/z range of 282-372 analyzed in 4 time

windows. All compounds in subgroups A and B were detected as the protonated molecular ion.

Subgroup C (ibuprofen, efavirenz, and zidovudine) covered a m/z range of 211-346 in 3 time windows.

These compounds were detected as the ammonium adduct [M+NH4]+ ion. Peak integration was done

using Xcalibur software.

For quality control, 1 mg/L of ciprofloxacin, levofloxacin and enrofloxacin standards were used at the

beginning and at the end of each MID analytical sequence, to monitor the stability of the instrument.

Figure 16 shows the set-up used for instrumental analysis. Detailed information can be found at the

AMBERLab website (www.Amberlab.UGent.be).

Figure 16: Instrumental analysis set-up (AmberLab, UGent)

HPLC MS

http://www.amberlab.ugent.be/

35 | P a g e

Chapter 4: Results and discussion


CHAPTER 4

RESULTS AND DISCUSSION

4.1 Water quality of the Nairobi River basin: physical-chemical parameters

Good water quality is vital not only for a healthy ecosystem but also for human well-being. It is

therefore important to monitor the physical-chemical parameters of water for proper management of

river water quality. On the other hand, physical-chemical parameters may affect the occurrence of

pharmaceuticals in the aquatic systems. Their knowledge may help in explaining the observations made

during the analysis of pharmaceuticals in such systems. For instance, high turbidity inhibits light

penetration in water thus impedes photodegradation of PAIs. Similarly, high concentration of hardness

ion improves removal of PAIs by complexation (Gartiser et al., 2007) while TSS affect removal by

sorption. Moreover, temperature influences hydrolysis and other chemical and biological reactions in

the aquatic systems (Bergheim et al., 2010). pH not only affects the activity of aquatic microorganisms

but also metal speciation which in turn determines complexation and sorption processes. It also

determines the existence state of some PAIs in water i.e. ionic or non-ionic form, thereby influencing

their physical and chemical properties such as polarity.

The analytical results of the physical-chemical parameters in water samples of the Nairobi River basin

in eight sites are presented in Table 6.

Table 6: Water quality of the Nairobi River basin

Units Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8

Kenyan standards

for effluent

Temperature 0C 22.9 21.9 21.9 20.7 22.9 20.7 20.9 20.6 x

pH 7.13 7.74 7.52 7.28 7.67 7.89 7.98 7.94 6.5-8.5

COD O2 mg/L 19 60 594 348 426 271 361 194 50

BOD5 O2 mg/L 4 40 460 240 340 140 210 120 30

BOD5/ COD

0.20 0.67 0.77 0.69 0.80 0.52 0.58 0.62 x

Conductivity µS/cm 240 559 720 662 1080 642 1061 778 x

Total Dissolved Solids(TDS) mg/L 149 347 446 410 670 398 658 482 1200

Total Suspended Solids(TSS) mg/L 20 100 380 480 140 160 80 100 30

Total Alkalinity mg/L 222 294 292 298 370 258 392 276 x

Turbidity NTU 22 210 56 47 115 43 139 88 x

Sulphates (SO42-) mg/L 0 3.4 12.6 34.9 31.7 23.7 22.3 19.1 x

Nitrite (NO2-) mg/L <0.01 <0.01 <0.01 0.02 <0.01 <0.01 0.07 <0.01

100 Nitrates (NO3

-) mg/L 0.5 0.9 0.8 1.2 1.3 0.9 1.1 0.9

Chloride (Cl-) mg/L 37 61 65 59 98 61 100 74 250

Iron (Fe2+) mg/L 0.7 1.3 6.2 5.9 5.4 3.2 0.9 2.1 10

Calcium (Ca2+) mg/L 11.2 28.0 18.4 27.2 37.6 17.6 30.4 32.8 x

Magnesium(Mg2+) mg/L 2.9 8.3 9.2 13.1 23.6 10.2 0.02 10.2 x

Total Hardness mg/L CaCO3 40 104 98 122 190 86 76 124 x

Fluoride (F-) mg/L 0.4 1.2 0.8 1.0 3.9 1.6 2.8 2.2 1.5

*NTU: Nephelometric Turbidity Unit; x: not reported

36 | P a g e



The temperature ranged from 20.6 to 22.9 0C. This variation can mainly be explained by differences in

the sampling time of the day. Exposure of the sites to solar radiation and any thermal discharge into the

rivers from industries can also be a factor. The pH fell within the normal pH for natural systems (6.5 –

8.5). The pH of the samples ranged between 7.1 (river) to 8.0 (effluent). The high pH in the effluent

could be due to a reduced amount of carbon dioxide due to photosynthesis by algae and macrophytes,

thus lowering the production of carbonic acid (Babović et al., 2011). The final effluent from waste

stabilization ponds often contains significant concentrations of algae (Shilton and Wamsley, 2005). This

may also explain the increase in pH at site 8 (downstream) compared to site 6 because more algae are

discharged into the river at the effluent of the WWTP. The COD and BOD5 ranged from 19–594 and 4–

460 mg O2/L, respectively. The highest concentrations were recorded at site 3, which could be due to

the high discharge of domestic waste (organic load) by the residents of the informal settlements of

Mathare, where there are neither sewerage systems nor solid waste management systems. There is,

however, a gradual decrease of BOD and COD downstream as the river undergoes self purification and

reduced sources of contamination.

The conductivity ranged from 240 to 1080 µS/cm with the highest conductivity being recorded at site 5.

This corresponded with high concentrations of ions like chloride, iron, calcium, magnesium, fluoride

and sulphate at this site. Indeed, an increase in electric conductivity occurs due to higher anionic

(carbonate, bicarbonate, chloride and nitrite ions) and cationic (calcium and magnesium ions) contents

in water (Babović et al., 2011). Possibly, the ions could be originating from the industrial waste

discharges in the area and domestic waste to some extent. Nitrite and nitrate concentrations were

generally low (<0.1 and <1.5 mg/L, respectively) in all the sampling sites possibly because of minor

agricultural activities in the basin‘s riparian.

Total alkalinity and total hardness were recorded in the range of 222 to 392 mg/L and 40 to 190 mg

CaCO3/L, respectively. High alkalinity and total water hardness may partially abate toxic effects of

heavy metals to water life (Babović et al., 2011). Moreover, high alkalinity ensures good buffering

capacity of a river system. This could explain the general stability of the pH despite the possible effects

of waste discharges. The highest iron concentration was recorded at site 3 (6.2 mg/L). This could be

due to the rusting iron sheet materials used in the construction of the shacks in the Mathare slum.

Generally, all the parameters tested registered the lowest values at site 1 (upstream). This was quite

expected as the area is less exposed to pollution sources, human activities and settlement. From the

results, it is evident that parts of the river are actually more polluted than the WWTP effluent,

especially sites 3, 4, and 5.

It can also be noted that there is a general reduction in the concentrations of the parameters downstream

especially at site 6. This can be attributed to reducing human settlements and activities in the area as

well as river self purification.

37 | P a g e



4.2 Prioritization of pharmaceutical compounds for trace analysis

Pharmaceuticals are many and of diverse classes. This poses challenges on their analysis in the

environment. It is imperative that a good system not only considers the quantity of pharmaceuticals

consumed but also class diversity when choosing priority compounds for analysis. Apart from

consumption quantity, probable exposure concentrations in the aquatic systems and effects (risk posed)

can also be employed (Besse and Garric, 2008). However, limited data on such properties has hindered

its application, thereby forcing many researchers to use consumption quantity. Al-Oldaini et al. (2010)

selected target compounds based on top 40 highly used drugs and non prescribed drugs (also called

over the counter) in Malaysia. Gros et al. (2009) selected target compounds based on their occurrence

and ubiquitous occurrence in the aquatic environment as reported in literature. Since drug consumption

varies from country to country, literature-based methods may not be applicable in some areas. For

instance, antimalarials may occur in tropical waters but absent in the temperate regions, thus may not

be reported in literature from Western countries. Therefore, a selection of target compounds in Africa

based on literature alone may ignore important compounds.

In this research, the selection of target compounds was based on the data of the quantity of each

pharmaceutical supplied by the Kenya Medical Supplies Agency (KEMSA) to government hospitals

and clinics in the Nairobi region. The region has a population of approximately 3.2 million. A total of

96 pharmaceuticals (some supplied in combination with one another) were supplied in 2008

representing 28.8 tons/year. Based on these data, the drugs were grouped into 17 different therapeutic

classes (Table 7).

Table 7: Classes and quantity of pharmaceuticals supplied to the hospitals by KEMSA in 2008

Therapeutic

Class Name of compound

QTY supplied

(g) to hospitals

Share (%) of each

class in the total

QTY supplied

Share (%) of each

compound within its

class

Share (%) of each

compound in the total QTY

supplied

Antibiotics

Co-Trimoxazole 7,833,196.60

60.84% 27.24%

Amoxycillin 2,886,715.00

22.42% 10.04%

Metronidazol 743,807.60

5.78% 2.59%

Azithromycim 324,342.50

2.52% 1.13%

Ciprofloxacin 267,235.00

2.08% 0.93%

Benzylpenicillin 218,730.00

1.70% 0.76%

Erythromycin 202,125.00

1.57% 0.70%

Streptomycin 175,000.00

1.36% 0.61%

Doxycycline 137,300.00

1.07% 0.48%

Benzathine 33854.4

0.26% 0.12%

Ceftriaxone 18,808.00

0.15% 0.07%

Ceftriaxone 18,808.00

0.15% 0.00%

Norfloxacin 13,720.00

0.11% 0.05%

Flucloxacil 9,000.00

0.07% 0.03%

Gentamicin 5,558.00

0.04% 0.02%

Tetracycline 3,517.40

0.03% 0.01%

Ofloxacin 2,540.00

0.02% 0.01%

Ampicillin 500.00

0.00% 0.00%

Total 12,875,949.50 44.770%

38 | P a g e




Therapeutic


QTY

supplied (g)

to hospitals

Share (%) of

each class in

the total QTY

supplied

Share (%) of each

compound within its

class

Share (%) of each compound

in the total QTY supplied

Analgesic, anti-

infammatory

drugs

Paracetamol 4,250,160.00

74.60% 14.78%

Ibuprofen 987,400.00

17.33% 3.43%

Acetylsalicylic Acid (Aspirin) 427,200.00

7.50% 1.49%

Diclofenac 13,345.00

0.23% 0.05%

Hydrocortisone 9,374.15

0.16% 0.03%

Amitriptyline 5,150.00

0.09% 0.02%

Aminophylline 2,439.25

0.04% 0.01%

Prednisolone 1,860.00

0.03% 0.01%

Pethidine 278.00

0.00% 0.00%

Dexamethasone 20.00

0.00% 0.00%

Loperamide 15.00

0.00% 0.00%

Codeine 11.00

0.00% 0.00%

Loperamide 10.00

0.00% 0.00%

Total 5,697,237.60 19.809%

Antimalarials

Artemether- Lumefantrine 4,485,156.48

86.29% 15.59%

Sulfadoxine +Pyrimethamine

627,500.00

12.07% 2.18%

Quinine Sulphate 84,954.00

1.63% 0.30%

Total 5,197,610.48 18.072%

Antiretrovirals

(ARV)

Stavudine+Lamivudine+Nevirapine 1,737,153.60

39.63% 6.04%

Zidovudine+Lamivudine+Nevirapine 1,173,081.00

26.76% 4.08%

Efavirenz 670,153.50

15.29% 2.33%

Lamivudine +Zidovudine 479,790.00

10.95% 1.67%

Stavudine +Lamivudine 296,913.60

6.77% 1.03%

Lamivudine 11,422.80

0.26% 0.04%

Zidovudine 8,956.00

0.20% 0.03%

Nevirapine 2,500.00

0.06% 0.01%

Abacavir (Ziagen) 1,705.68

0.04% 0.01%

Tenofovir 1,593.00

0.04% 0.01%

Lopinavir +Ritonavir 144.00

0.00% 0.00%

Stavudine 85.50

0.00% 0.00%

Total 4,383,498.68 15.241%

Anthelmitics Albendazole 186,800.00

99.84% 0.65%

Praziquantel 300.00

0.16% 0.00%

Total 187,100.00 0.651%

Antifungals

Ketoconazole 87,294.00

56.53% 0.30%

Clotrimazole 32,530.60

21.07% 0.11%

Nystatin 27,288.00

17.67% 0.09%

Fluconazole 6,809.60

4.41% 0.02%

Fluconazole 505.00

0.33% 0.00%

Total 154,427.20 0.537%

Antipsychotics

Chlorpromazine 96,309.30

72.27% 0.33%

Phenobarbitone 12,471.00

9.36% 0.04%

Carbamazepine 10,160.00

7.62% 0.04%

Haloperidol 9,583.00

7.19% 0.03%

Phenytoin Sodium 3,250.00

2.44% 0.01%

Diazepam 930.00

0.70% 0.00%

Risperidone 393.90

0.30% 0.00%

Flupenthixol Decanoate 128.20

0.10% 0.00%

Fluphenazine 45.25

0.03% 0.00%

Total 133,270.65 0.463%

39 | P a g e



Note: In bold are the priority compounds chosen

After classification of the pharmaceuticals, the quantity of each class was determined. From these

quantities, the percentage share of each pharmaceutical class within the total quantity supplied all over

the entire list was calculated. Antibiotics represented the most consumed pharmaceutical class (44.8%)

followed by analgesics/anti-inflammatory drugs (19.8%).


Therapeutic


QTY

supplied (g)

to hospitals

Share (%) of

each class in the

total QTY

supplied

Share (%) of each

compound within its

class

Share (%) of each compound

in the total QTY supplied

Antivirals

Acyclovir 42,400.00

99.81% 0.15%

Osteltamivir 78.75

0.19% 0.00%

Total 42,478.75 0.148%

Antihistamines

Chloramphenicol 21,990.00

63.86% 0.08%

Chlorpheniramine 12,447.00

36.14% 0.04%

Ranitidine 0.00

0.00% 0.00%

Total 34,437.00 0.120%

Beta blockers

Methyldopa 11,250.00

47.25% 0.04%

Salbutamol 5,394.08

22.65% 0.02%

Atenolol 4,379.20

18.39% 0.02%

Nifedipine 2,500.00

10.50% 0.01%

Enalapril 285.00

1.20% 0.00%

Hydralazine 1.40

0.01% 0.00%

Digoxin 0.88

0.00% 0.00%

Total 23,810.56 0.083%

Vitamins

Retinol 9,735.00

71.92% 0.03%

Folic Acid 3,800.00

28.08% 0.01%

Total 13,535.00 0.047%

Antidiabetics Metformine 10,000.00

91.37% 0.03%

Glibenclamide 945.00

8.63% 0.00%

Total 10,945.00 0.038%

Contraceptives

Norgestrel/Ethylestradiol 855.62

40.25% 0.00%

Etonogestrel 811.58

38.18% 0.00%

Dimedroxyprogesterone 448.05

21.08% 0.00%

Levonogestrel (progesterone) 10.46

0.49% 0.00%

Total 2,125.71 0.007%

Antiulcers Omeprazole 1,666.40

100.00% 0.00%

Total 1,666.40 0.006%

Antiparkinsonian

drugs

Benzhexol 1,230.00

98.83% 0.00%

Atropine 14.55

1.17% 0.00%

Total 1,244.55 0.004%

Antiemitic drugs Metoclopramide 1,000.40

100.00% 0.00%

Total 1,000.40 0.003%

Anaesthesia

Thiopentone Sodium 150.00

81.00% 0.00%

Ketamine 25.00

13.50% 0.00%

Pancuronium 9.00

4.86% 0.00%

Lignocaine HCL 1.19

0.64% 0.00%

Total 185.19 0.001%

Total Supply to hospitals 28,760,522.27

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Antibiotics and analgesics/anti-inflammatory drugs are the most consumed pharmaceutical classes

because of their ease of access (non prescription) and high prevalence of bacterial infections (Fent et

al., 2006, Zuccato et al., 2010).

The percentage share of each pharmaceutical compound within the total supply was also determined.

Co-trimethoxazole (27.7%) was the most consumed drug probably due to its usage in the treatment of

diverse bacterial infections. It was followed by artemether-lumefantrine (15.6%). Promotion of the use

of artemisinin-based combination therapies (ACTs) as the first line drugs for malaria treatment by the

government could explain its high consumption. For each compound, its percentage contribution within

its class was calculated. For instance, paracetamol was the most consumed compound (74.6%) within

the analgesics/anti-inflammatory drugs class.

Based on the percentage share of each pharmaceutical class within the total supply, the classes were

divided into major and minor groups. Each class representing more than 10% of the total drugs supply

were considered as a major group each and were kept individually. Four major groups were noticed: (1)

antibiotics, (2) analgesics/anti-inflammatory drugs, (3) antiretrovirals, and (4) antimalarials. Minor

classes were defined as those representing less than 10% of the total supply, and were merged into two

subgroups. Subgroup A lumped together all classes having a share of 0.1<10%; and subgroup B merged

all classes with a contribution less than 0.1% (Table 8a and 8b).

Table 8a: List of minor group (subgroup A)

Minor group

(0.1%<10%)

QTY supplied (g)

Share (%) of each compound within its

group

Share (%) of each compound in the total QTY

supplied Name of compound

Albendazole 186,800.00 33.86% 0.650%

Chlorpromazine 96,309.30 17.46% 0.335%

Ketoconazole 87,294.00 15.82% 0.304%

Acyclovir 42,400.00 7.69% 0.147%

Clotrimazole 32,530.60 5.90% 0.113%

Nystatin 27,288.00 4.95% 0.095%

Chloramphenicol 21,990.00 3.99% 0.076%

Phenobarbitone 12,471.00 2.26% 0.043%

Chlorpheniramine 12,447.00 2.26% 0.043%

Carbamazepine 10,160.00 1.84% 0.035%

Haloperidol 9,583.00 1.74% 0.033%

Fluconazole 6,809.60 1.23% 0.024%

Phenytoin Sodium 3,250.00 0.59% 0.011%

Diazepam 930.00 0.17% 0.003%

Fluconazole 505.00 0.09% 0.002%

Risperidone 393.90 0.07% 0.001%

Praziquantel 300.00 0.05% 0.001%

Flupenthixol Decanoate 128.20 0.02% 0.000%

Osteltamivir 78.75 0.01% 0.000%

Fluphenazine 45.25 0.01% 0.000%

Mebendazole 0.00 0.00% 0.000%

Phenytoin 0.00 0.00% 0.000%

Ranitidine 0.00 0.00% 0.000%

Total 551,713.60

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Table 8b: List of minor group (subgroup B)

Minor group (<0.1%)

QTY supplied (g)

Share (%) of each compound within its

group

Share (%) of each compound in the total QTY

supplied

Methyldopa 11,250.00 20.64% 0.039%

Metformine 10,000.00 18.34% 0.035%

Retinol 9,735.00 17.86% 0.034%

Salbutamol 5,394.08 9.90% 0.019%

Atenolol 4,379.20 8.03% 0.015%

Folic Acid 3,800.00 6.97% 0.013%

Nifedipine 2,500.00 4.59% 0.009%

Omeprazole 1,426.40 2.62% 0.005%

Benzhexol 1,230.00 2.26% 0.004%

Metoclopramide 1,000.40 1.84% 0.003%

Glibenclamide 945.00 1.73% 0.003%

Norgestrel/Ethylestradiol 855.62 1.57% 0.003%

Etonogestrel 811.58 1.49% 0.003%

Dimedroxyprogesterone 448.05 0.82% 0.002%

Enalapril 285.00 0.52% 0.001%

Omeprazole 240.00 0.44% 0.001%

Thiopentone Sodium 150.00 0.28% 0.001%

Ketamine 25.00 0.05% 0.000%

Atropine 14.55 0.03% 0.000%

Levonogestrel

(progesterone) 10.46 0.02% 0.000%

Pancuronium 9.00 0.02% 0.000%

Hydralazine 1.40 0.00% 0.000%

Lignocaine HCL 1.19 0.00% 0.000%

Digoxin 0.88 0.00% 0.000%

Medroxyprogesterone 0.00 0.00% 0.000%

Total 54,512.81

Within each major group and subgroup A (antihistamines, antivirals, antipsychotics, antifungals,

anthelmitics) and B (antipsychotics, antifungal, Anthelmitics), the percentage contribution of all

individual pharmaceutical compounds was calculated. Compounds making 95% of or the first five

compounds in each class and/or group were considered as priority compounds for screening purposes

(see Section 4.3.1). Cognizant of the fact that some compounds are supplied in combined form in a

drug (e.g. Co-Trimoxazole (trimethoprim/sulfamethoxazole)); the two options were considered for

flexibility and diversity of chosen compounds. The overall procedure to select compounds is

summarized in Figure 17. Based on the data analysis, a total number of 43 (Table 7, in bold) initial

priority compounds were selected for screening.

42 | P a g e



Drug quantity supplied

Therapeutic groups

Major (>10%) Minor (<10%)

0.1% <10% (Sub-minor A) <0.1% (Sub-minor B)

95% or First 5 compounds, whichever is higher

Priority compounds

Focus compounds

Screening

Figure 17: Scheme of selection criteria for focus compounds

4.3 Trace analysis of human pharmaceuticals in Nairobi River basin

4.3.1 Screening of priority compounds by HPLC-HRMS

A first screening of Nairobi river water was employed to select a more limited number of so-called

focus compounds from the initial 43 priority compounds. The aim was to investigate these focus

compounds in more detail in further work. Samples from two sites were chosen and screened for the

presence of the priority compounds (see Section 4.2). The choice of the samples for screening was

based on the knowledge of contamination sources around the sampling sites and the level of pollution

based on COD and BOD5.

During screening, the samples were analyzed using HPLC-HRMS in high mass resolution escan (See

Section 3.4). Based on their masses, the priority compounds representing a molar mass range of 150-

772 g/mol were classified into eight subgroups each representing a narrower mass window (with the

highest mass not higher than 1.2 times the lowest mass). Since the retention times of the analytes were

unknown, time windows could not be defined and each mass window required a separate analysis. For

all compounds, the signal corresponding to the protonated molecular ion, the ammonium adduct and the

sodium adduct was extracted from the total ion chromatogram (TIC), thus generating extracted ion

chromatograms (XIC). Since the measurement of protonated ions and ammonium adducts require other

conditions, 16 injections (analysis) were required for each sample.

43 | P a g e



This explains the importance of having a more limited and reasonable number of focus compounds if

many samples are to be analyzed thus, the need for screening of few samples.

Based on the screening results, the retention times, peak intensity and mass accuracy, expressed as delta

were determined. Delta represents the ratio of the difference between the exact calculated mass

(theoretical value) and the measured mass to the calculated mass, in parts per million (ppm).

Compounds showing a signal with intensity > 40,000 µV and delta < 10 ppm were selected as focus

compounds for further trace analysis. The list of focus compounds is shown in Table 9. It can be noted

that zidovudine was detected both as the protonated ion and the ammonium adduct at different retention

time. Therefore, both ions were incorporated in the list of focus compounds.

Table 9: List of compounds detected during screening

4.3.2 Screening and selective target analysis of 14 focus compounds in Nairobi River basin

Detection and identification of the focus compounds in all collected water samples was done using the

HPLC-HRMS analytical system. Since there were no analytical standards available at the beginning, a

methodology was developed to differentiate in degree of certainty with respect to compound‘s

identification. To do this, two approaches were followed: (i) escan and (ii) MID detection approaches.

The escan is important because it provides mass accuracy (delta) and the ion intensity, while MID is

more selective and sensitive. It provides much better developed chromatographic peaks which provide

the possibility to calculate peak areas as a measure of the compound‘s concentration. Based on their

masses and detection mode (as the protonated molecular ion or as the ammonium adduct), the 14 focus

compounds were divided into 3 subgroups: A, B and C (Table 10), making use of time windows defined

based on the retention times measured during screening (Section 4.3.1). This resulted in a total of 6

analyses per sample (3 escan and 3 MID analyses) to cover the entire group of focus compounds.

Name of compound Ion Retention time Delta (ppm) Intensity (µV)

Sampling site 3 Sampling site 7 Sampling site 3 Sampling site 7

Amoxicillin +H 24.05 -3.578 0.819 77,300 54,100

Benzylpenicillin +H 32.68 -4.655 _ 50,100 _

Carbamazepine +H 35.75 9.911 4.428 40,500 72,900

Efavirenz +NH4 26.49 _ -1.531 _ 52,000

Ibuprofen +NH4 36.59 2.654 3.234 54,500 57,100

Lamivudine +H 3.14 1.869 -0.739 80,100 132,000

Methyldopa +H 17.89 3.630 _ 61,400 _

Metronidazole +H 8.42 1.395 _ 40,900 _

Nevirapine +H 33.39 1.984 2.396 158,000 312,000

Paracetamol +H 8.87 -2.696 _ 599,000 _

Sulfadoxine +H 26.51 -3.890 -5.979 72,400 243,000

Sulfamethoxazole +H 23.35 3.582 3.936 369,000 641,000

Trimethoprim +H 9.48 -3.194 -6.286 570,000 16,000

Zidovudine +H 19.54 1.865 _ 67,100 _

Zidovudine +NH4 31.69 _ -3.332 _ 50,700

44 | P a g e



Table 10: Classification of the focus compounds for escan and MID analysis

Compound Time window

Retention

time range Lock-mass Calibration mass [M+H]+ [M+NH4]+

Group A Mass range (151-267)

Lamivudine 1 0-4.0 195.123 239.149 230.05939 -

Metronidazole 2 4.0-11.0 151.096 195.123 172.07167 -

Paracetamol 2 4.0-11.0 151.096 195.123 152.07061 -

Methyldopa 3 11.0-18.5 195.123 239.149 212.09173 -

Zidovudine 4 18.50-34.5 239.149 283.175 268.10403 -

Sulfamethoxazole 4 18.50-34.5 239.149 283.175 254.05939 -

Nevirapine 4 18.50-34.5 239.149 283.175 267.12404 -

Carbamazepine 6 34.5-38.0 195.123 239.149 237.10224 -

Group B Mass range (290-365)

Trimethoprim 1 0-15 283.175 327.201 291.14517 -

Amoxicillin 2 15.0-24.2 327.201 371.228 366.11182 -

Sulfadoxine 3 24.2-27.5 283.175 327.201 311.08085 -

Benzylpenicillin 4 27.5-65.0 327.201 371.228 335.10600 -

Group C: Mass range (206-315)

Efavirenz 1 20-28.0 300.202 344.228 - 333.06122

Zidovudine 2 28.0-36.00 256.175 300.202 - 285.13058

Ibuprofen 3 36.0-40.0 212.149 256.175 - 224.16451

The identification and detection of the compounds was based on four parameters: (i) the retention times

in escan and MID, (ii) the escan ion intensity (µV), (iii) the deviation of the measured mass from the

calculated accurate mass (delta, ppm) and (iv) the standard deviation (STDEV) on the delta (ppm).

First, each compound was isolated from the total ion chromatograms (TIC) (Figure 18) obtained by

both escan and MID analysis, by generating the corresponding extracted ion chromatograms (XIC)

(Figure 19). For peaks having a signal-to-noise (S/N) ratio greater than 3, the retention time, measured

mass and peak intensities (escan) and areas (MID) of each analyte were determined from the XIC. The

delta and standard deviations (STDEV) were then determined. Intensity is an important parameter in

MS as it influences the accuracy of analyte detection. High intensities are good for accurate detections.

Limits of less than 10 ppm (delta) and less than 15 ppm (STDEV) were set for any compound to be

considered as being identified. These limits were based on the experience with the MS instrument in

earlier research and its specifications. Using HPLC-QTOF, Ibáñez et al. (2009) assumed a safe

confirmation if mass accuracies were better than 2mDa (≈7 ppm) and the ion ratio deviations of at least

two ions were between the limits (±15%) established by the European Union.

45 | P a g e



File name: C:\Documents and Settings\...\mat3560 3/16/2011 5:41:18 PM Sample ID: 4293Sample Name: Sample 8 - 1ml D:\GCMSDATA\Methods\LC\Multi2_+H_A.meth Inj. Vol (µl): 10µlMass T.: 50.0 ppm MID - A - +H EP242

RT: 0.00 - 64.90

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5348.88 1697

35.841943.18

114919.64

260249.54

220043.51

152731.60

62611.02

50822.92

51523.23

74133.40

81836.84

45720.62

29413.30

934.28

1687.65 824

37.11

110749.82

104647.08

111951.81

NL:8.21E5

TIC F: MS mat3560

NL:3.14E6

TIC F: MS mat3571-c1

mat3560 #2469-2477 RT: 47.54-47.66 AV: 9 NL: 2.97E5T: + c ESI SIM ms [ 150.60-195.62]

155 160 165 170 175 180 185 190 195

m/z

0

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250000

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sity

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R

195.12270

181.04953

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escan

File name: c:\documents and settings\...\mat3571-c1 3/22/2011 1:46:13 PM Sample ID:

Sample Name: Inj. Vol (µl):

Mass T.: 10.0 mmu

RT: 4.82 - 12.29

5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0

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579

9.92

620

10.88

586

10.09

502

8.13

185

8.39

183

8.30

NL:

8.97E3

m/z=

172.06307-

172.08027 F:

M S mat3560

NL:

2.15E4

m/z=

172.06307-

172.08027 F:

M S

mat3571-c1

mat3571-c1 #185 RT: 8.39 AV: 1 NL: 3.13E5

T: + c ESI Full ms [ 149.50-196.50]

150 155 160 165 170 175 180 185 190 195

m/z

0

50000

100000

150000

200000

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Inte

nsi

ty

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R

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195.12270

151.09648

173.07843

177.10686183.12184172.07242 174.08015152.10163 161.13501 164.98390

escan

MID

Figure 18: Sample TIC obtained by the analysis of Group A in MID and escan mode (site 5)

Figure 19: Sample XIC for metronidazole in escan and MID mode (Site 5)

To confirm identification, the peak retention times of each compound in both escan and MID mode

were compared and a maximum of ±5% shift in retention times was allowed. In practice, the shifts in

retention time for all compounds considered were less than 3%. This was similar to that found by Gros

et al. (2006) for accurate confirmation of analytes with the standards. Based on all these criteria, a

decision tree to ‗label‘ detected compounds as ‗indicatively‘, ‗probably‘ or ‗positively‘ identified was

developed (Figure 20).

escan

46 | P a g e



Figure 20: Schematics of compound identification criteria

For the positively identified focus compounds, their identification was considered accurate because

their delta fell within the instrument‘s accuracy (<5 ppm) and intensity greater than 100,000 µV, which

is considered good for positive identification. For confirmation of target compounds, the agreement

between the measured and calculated masses within a <5 ppm error level provides an unequivocal

confirmation of the contaminants in the samples (Bueno et al., 2007).

The second category was considered to be probably identified because these compounds having a delta

less than 5 ppm showed the intensities between 50,000 and 100,000 µV.

tR, Escan

Delta and STDEV

Intensity

High resolution escan TIC

Extracted Ion Chromatogram (XIC)

S/N <3 S/N >3

Intensity Escan <50,000 µV or Intensity

Escan >50,000 µV AND delta 5- 10 ppm

Measured mass

Delta >10 ppm or STDEV >15 ppm Delta < 10 ppm AND STDEV <15 ppm ppm

High resolution MID

tR, difference > 5%

Ion peak area, MID

Target compound

Reject

Reject

Reject

tR, difference < 5%

Intensity Escan 50,000 µV – 100,000

µV AND delta < 5 ppm

Intensity Escan > 100,000 µV

AND delta < 5 ppm

Probably identified (b)

Indicatively identified (c) Positively Identified (a)

47 | P a g e



The compounds could, therefore, not be accurately considered to be the focus compounds but required

comparison with analytical standards. It is believed that due to their low delta values, there are higher

chances that they could be the focus compounds.

The third category was considered to represent indicatively compounds. They had intensities less than

50,000 µV or higher but delta values higher than 5 (and < 10) ppm, which is considered not good

enough for positive identification. However, instead of outright discard, they can be confirmed by

analysis of analytical standards. They only show weak possibility of the target compounds.

The developed decision tree with abovementioned compound identification criteria was employed to

identify pharmaceuticals in the Nairobi River basin water samples. It is important to notice that this is

the first attempt to determine the occurrence of pharmaceuticals in Kenyan waters. The results of escan

and MID modes are presented in Table 11 and 12, respectively.

Positively identified compounds were paracetamol, sulfamethoxazole, nevirapine, trimethoprim and

sulfadoxine. However, in some sites, these compounds appeared to fall in either of the other two

categories. Similarly, zidovudine and ibuprofen fell in the probably identified focus compounds in some

sites while in others; they appeared to be in the indicative category. Efavirenz, lamivudine,

carbamazepine, metronidazole and amoxicillin were identified as indicative compounds. Most of these

compounds have been detected in wastewater and surface water by other researchers (Al-Oldaini et al.,

2010; Fick et al., 2009; Nödler et al., 2010; Prasse et al., 2010; Zuccato et al., 2010). However, no

studies are found that report detection of efavirenz and sulfadoxine in aquatic systems.

Table 11: Retention time, intensity, delta and STDEV (escan) of detected compounds in the Nairobi River basin

Name of compound tR Site 1 Site 2 Site 3 Site 4

(min) Intensity Delta STDEV Intensity Delta STDEV Intensity Delta STDEV

(µV) (ppm) (ppm) (µV) (ppm) (ppm) (µV) (ppm) (ppm)

Amoxicillin 23.58±0.18 _ 15,000 -4.10 10.17 - - - 12,600 -3.41 5.11

Carbamazepine 35.74±0.02 _ 13,200 20.41 19.68 21,700 25.18 12.72 27,500 17.76 2.87

Efavirenz 25.83±0.00 _ - - - - - - - - -

Ibuprofen 36.66±0.06 _ 51,900 -1.54 19.98 48,500 13.45 8.09 32,700 5.90 4.00

Lamivudine 3.17±0.05 _ - - - 40,200 3.22 5.80 26,500 0.57 3.88

Metronidazole 8.37±0.04 _ 18,000 7.09 8.31 18,600 -12.96 4.64 15,700 -6.04 14.12

Nevirapine 33.37±0.02 _ 63,700 4.83 4.58 537,000 0.56 7.18 217,000 1.68 3.50

Paracetamol 8.84±0.06 _ - - - 362,000 -0.92 4.57 233,000 -0.79 7.45

Sulfadoxine 26.11±0.09 _ - - - 44,200 -2.22 6.16 36,200 -8.94 12.47

Sulfamethoxazole 23.09±0.41 _ - - - 545,000 3.78 5.23 331,000 3.23 2.53

Trimethoprim 9.37±0.07 _ 28,800 -3.06 6.04 364,000 -4.02 3.64 242,000 -2.75 1.55

Zidovudine 19.35±0.09 _ 13,900 -7.39 4.74 47,900 -2.91 7.80 - - --

escan

MID

48 | P a g e




Key: yellow: positively identified; red: probably identified; purple: indicatively identified

Table 12: Retention times and peak areas (MID) of detected compounds in the Nairobi River basin

4.3.3 Unequivocal identification and approximative quantification of detected focus

compounds

4.3.3.1 Unequivocal identification of the detected focus compounds

For each of the 12 focus compounds that were ―indicatively‖, ―probably‖ or ―positively‖ identified in at

least one of the water samples, analytical standards were purchased. This is important for their

unequivocal identification as well as quantification. The 12 analytical standards were analysed in

triplicate at a concentration of 1 mg/L via HPLC-HRMS in MID mode. Each analytical standard was

analysed in triplicate. The results are shown in Table 13.

Name of compound Site 5 Site 6 Site 7 Site 8

Intensity Delta STDEV Intensity Delta STDEV Intensity Delta STDEV Intensity Delta STDEV

(µV) (ppm) (ppm) (µV) (ppm) (ppm) (µV) (ppm) (ppm) (µV) (ppm) (ppm)

Amoxicillin 11,900 3.85 13.42 - - - 11,900 -4.51 4.77 - - -

Carbamazepine 26,800 -0.30 13.70 20,800 5.67 9.23 14,700 -6.37 13.76 23,200 11.30 4.51

Efavirenz - - - - - - 28,300 -5.94 8.08 - - -

Ibuprofen 34,000 15.86 0.00 49,100 9.84 12.49 82,500 -1.00 5.53 50,700 6.80 5.93

Lamivudine 21,900 4.30 7.03 44,100 4.35 1.93 40,100 -3.35 11.47 40,600 -2.48 5.54

Metronidazole 13,900 -2.27 6.90 116,000 8.25 7.59 - - - 14,900 -0.41 6.25

Nevirapine 521,000 1.27 6.22 82,300 5.35 5.38 105,000 0.52 6.57 74,500 2.55 3.55

Paracetamol 347,000 0.72 5.89 46,300 0.59 6.99 - - - - - -

Sulfadoxine - - - 11,200 -5.27 13.62 748,000 -1.16 3.25 19,800 -9.93 8.95

Sulfamethoxazole 751,000 6.18 2.80 106,000 -0.08 5.28 243,000 1.73 3.18 136,000 4.83 2.83

Trimethoprim 956,000 -1.96 2.46 123,000 0.96 4.27 5,310 -8.55 8.70 47,400 -1.10 4.12

Zidovudine 24,400 6.71 8.04 22,300 1.57 8.62 - - - 12,600 7.91 12.62

Name of

compound tR (min) Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8

Amoxicillin 23.45±0.03 _ _ 568,516 375,644 185,469 _ 491,737 _

Carbamazepine 35.85±0.01 _ 83,159 _ 286,168 366,348 246,130 282,304 171,955

Efavirenz 25.98±0.00 _ _ _ _ _ _ 117,213 _

Ibuprofen 36.67±0.04 _ _ _ 218,625 _ 434,735 640,170 448,620

Lamivudine 3.21±0.03 _ _ 607,962 538,920 298,817 537,421 502,132 376,336

Metronidazole 8.43±0.01 _ 209,381 _ 162,516 136,649 689,551 _ 176,197

Nevirapine 33.49±0.01 _ 1,139,151 1,860,680 3,652,830 4,731,649 1,213,663 1,730,171 1,271,336

Paracetamol 8.90±0.02 _ _ 5,729,164 3,931,485 1,773,308 657,994 _ _

Sulfadoxine 25.99±0.03 _ _ 1,135,820 1,011,807 _ 204,902 2,543,547 448,517

Sulfamethoxazole 23.24±0.05 _ _ 7,229,743 7,414,097 16,423,693 1,778,740 4,804,985 2,055,733

Trimethoprim 9.39±0.03 _ 1,209,636 7,452,248 5,697,150 16,431,593 2,163,903 _ 728,077

Zidovidine 19.65±0.03 - 157,770 823,719 - 329,771 377,538 - 175,864

49 | P a g e



Table 13: Retention time and response factor of the analytical standards

Name of compound Mean retention time (min), n=3 Mean peak area, n=3 Concentartion (mg/L) Response factor

Group A (day 1)

Carbamazepine 35.60±0.02 9,942,970 ±1,625 1.0 9,942,970

Lamivudine 3.02±0.00 4,805,645 ±434,494 1.0 4,805,645

Metronidazole 8.19±0.03 10,018,109 ±1,128,788 1.0 10,018,109

Nevirapine 33.19±0.03 25,300,796 ±1,936,952 1.0 25,300,796

Paracetamol 8.63±0.03 3,651,134±286,789 1.0 3,651,134

Zidovudine _ - - -

Sulfamethoxazole 22.68±0.08 8,092,775 ±416,257 1.0 8,092,775

Group B (day 2)

Trimethoprim 8.91±0.02 37,249,861±784,220 1.0 37,249,861

Amoxicillin 23.25±0.01 5,085± 637 1.0 5,085

Sulfadoxine 25.65±0.09 19,656,109 ±669,211 1.0 19,656,109

Group C (day 3)

Ibuprofen 36.31±0.00 52,322 ±0.0 1.0 52,322

Efavirenz - - - -

For full confirmation, the chromatographic retention times of the focus compounds detected in the

samples were compared with those of the analytical standards. Compounds were considered

unequivocally identified if the shift in retention time was less or equals to ±5% as suggested by other

authors (Gros et al., 2006; Grujić et al., 2009; Lin and Tsai, 2009). All the detected focus compounds

were unequivocally identified except zidovudine and efavirenz. Efavirenz didn‘t show any peak at the

retention time at which it was indicatively detected in the water sample taken at site 7. Given the fact

that, it had a low intensity (28,300 µV) and high delta (5.94), this all indicates that its detection most

probably was a false positive. The reason for the absence of a peak at the expected retention for the

zidovudine standard is not clear.

Overall, these results fortify the applicability of the identification criteria (Figure 20) to classify the

identification power of the focus compounds into three categories. Unequivocal identification of the

detected focus compounds by confirmation analysis of standards is, however, preferable. It might

confirm the detection of compounds that were labeled as indicative based on Figure 20, and thus could

not be accurately confirmed without standards.

From the entire study, the unequivocally identified pharmaceuticals detected in the Nairobi River basin

water were amoxicillin, carbamazepine, ibuprofen, lamivudine, metronidazole, nevirapine,

paracetamol, sulfadoxine, sulfamethoxazole, and trimethoprim.

4.3.3.2 Approximate quantification of the detected focus compounds in water samples

Based on the one-point calibration, the response factors (RF) (Equation 2) of the analytical standards

were determined (see Table 13). These response factors could be used to quantify the detected focus

compounds in the water samples making use of the MID peak areas given in Table 12.

𝑅𝑒𝑠𝑝𝑜𝑛𝑠𝑒 𝑓𝑎𝑐𝑡𝑜𝑟 (𝑅𝐹) =𝑃𝑒𝑎𝑘 𝑎𝑟𝑒𝑎 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒 𝑜𝑓 𝑐𝑎𝑙𝑖𝑏𝑟𝑎𝑡𝑖𝑜𝑛 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑

𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑐𝑎𝑙𝑖𝑏𝑟𝑎𝑡𝑖𝑜𝑛 𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑 (𝑚𝑔

𝐿) (2)

50 | P a g e



However, it is well-known that the response of an MS detector is not very stable in time. This means

that the RF that have been determined on the calibration days may not be valid at the days on which the

samples have been analyzed. In order to deal with that, three external standards (ciprofloxacin,

enrofloxacin and levofloxacin) were analysed at 1 mg/L concentration before and after each analytical

sequence both on the day that the samples were analyzed and on the day that the analytical standards

were analysed (calibration day). Table 14 shows the mean peak areas (response) obtained during the

analysis of each group of compounds (A, B, C). Within the days of sample analysis and the calibration

days, there was good stability in the responses of the three external standards. The relative standard

deviation (RSD) was less than 10% in all cases (results not shown). However, between both days, a big

variation in response is noticed, suggesting improved sensitivity during the calibration of the analytical

standards. This could be attributed to the maintenance of the equipment before the calibration was

performed. To take care of this variation in response, a correction factor (Equation 3) was determined

and used in the conversion of MID peak areas (Table 12) into compound‘s concentrations (Table 15). It

should be noted, however, that the reported concentrations do not take into account extraction recovery

and matrix effects, and can therefore not be considered to be fully validated.

𝐶𝑜𝑟𝑟𝑒𝑐𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟 =𝑅𝑒𝑠𝑝𝑜𝑛𝑠𝑒 𝑓𝑎𝑐𝑡𝑜𝑟 𝑜𝑛 𝑡𝑕𝑒 𝑑𝑎𝑦 𝑜𝑓𝑠𝑎𝑚𝑝𝑙𝑒 𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠

𝑅𝑒𝑠𝑝𝑜𝑛𝑠𝑒 𝑓𝑎𝑐𝑡𝑜𝑟 𝑜𝑛 𝑡𝑕𝑒 𝑐𝑎𝑙𝑖𝑏𝑟𝑎𝑡𝑖𝑜𝑛 𝑑𝑎𝑦 (3)

Table 14: Peak area (response) of external standards during calibration and sample analysis

Name of compound Ion Calibration day Sample analysis day

Mean peak area, n=3 Mean peak area, n=2 Correction factor Mean correction factor

Group A +H

Ciprofloxacin 6249021 2894497 0.46 0.48

Enrofloxacin 8746708 4467261 0.51

Levofloxacin 9394385 4365498 0.46

Group B +H

Ciprofloxacin 8678982 3160166 0.36 0.38

Enrofloxacin 11279548 4582807 0.41

Levofloxacin 11577623 4227966 0.37

Group C +NH4

Ciprofloxacin 5207743 4920128 0.94 0.89

Enrofloxacin 9735418 9761194 1.00

Levofloxacin 9525798 6998934 0.73

Table 15: Concentration (ng/L) of pharmaceuticals in Nairobi River basin

Name of compound Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 Site 8 Mean

Amoxicillin* _ _ 1,476,696 975,719 481,748 _ 1,277,266 _ 1,052,857±432,864

Carbamazepine _ 87 _ 300 384 258 296 180 251±104

Ibuprofen _ _ _ 23,368 _ 46,467 68,426 47,951 46,553±18,420

Lamivudine _

1,319 1,169 648 1,166 1,089 817 1,035±251

Metronidazole _ 218 _ 169 142 718 _ 183 286±243

Nevirapine _ 469 767 1,505 1,950 500 713 524 918±578

Paracetamol _ _ 16,361 11,227 5,064 1,879 _ _ 8,633±6,450

Sulfadoxine _ _ 763 680 _ 138 1,709 301 718±612

Sulfamethoxazole _ _ 9,315 9,552 21,160 2,292 6,191 2,649 8,526±6,931

Trimethoprim _ 429 2,643 2,020 5,827 767 _ 258 1,991±2,101

*amoxicillin has extremely high concentrations compared to the others and those reported in literature. Its response factor is also very low (see Table 13).

It‘s therefore, discussed independently from the others (see Section 4.3.4.1).

51 | P a g e



Generally, the mean concentration of pharmaceuticals in the Nairobi River basin ranged from 251 ng/L

to 46 µg/L. The concentrations reduced downstream. Nevirapine is the most frequently detected

compound with a mean concentration of 918 ng/L and ranges from 469-1950 ng/L. Compounds which

recorded high mean concentration levels are ibuprofen (46 µg/L), paracetamol (8.6 µg/L),

sulfamethoxazole (8.5 µg/L) and trimethoprim (2 µg/L).

All the identified focus compounds were detected in sampling site 4, followed by sampling site 6,

which recorded all the compounds except one (amoxicillin). These sites are situated after river

confluences, thus, the convergence of rivers could explain the observation as water from upstream

brings different compounds.

In site 1, none of the target compounds were identified. This fact is buttressed by the results of the

physical-chemical properties at this site which were very low relative to the others. The absence of

domestic and industrial wastewater discharge into the river at this site probably explains this further.

Domestic waste stream has high human drugs contamination (Lin et al., 2009).

In the effluent (site 7), seven out of the ten identified focus compounds were detected at concentrations

ranging from 713 ng/L (nevirapine) to 68 µg/L (ibuprofen). The effluent discharge into the river seemed

to increase the concentrations of some compounds e.g. sulfadoxine downstream.

4.3.4 Discussion

Each day, new pharmaceutical compounds are discovered and registered. Often overlooked are the

possible effects of these compounds on the environment once their intended use has been achieved or in

cases where they are not used, though produced. Nevertheless, some concerned scientists have been

burning the midnight oil to reveal if indeed they may have effects. Antibiotics have been reported to

induce bacterial resistance (Lin and Tsai 2009), while analgesics/anti-inflammatory drugs like

diclofenac led to decrease of gyps vultures in India (Taggart et al., 2007). At the same time, many

studies are reporting the occurrence of PAIs in the environment at concentration levels of low ng/L to

µg/L (Alder et al., 2010; Chen et al., 2007; Prasse et al., 2010; Zuccato et al., 2010). Similar

observations have been made in this study.

4.3.4.1 Antibiotics

The group of antibiotics is one of the most studied pharmaceutical classes in environmental samples.

Moreover, high levels of broad-spectrum antibiotics could induce the development of antibiotic-

resistant microorganisms (Fick et al., 2009). In this study, sulfamethoxazole was detected in 5 out of 8

sites studied, with site 5 having the highest concentration (21.2 µg/L). This could be due to discharge

from pharmaceutical factories in the vicinity (Lin and Tsai, 2009). Other authors have also detected

sulfamethoxazole in river water (Zhang and Zhou, 2007; Zuccato et al., 2010). Similarly, Nödler et al.

(2010) detected sulfamethoxazole with a mean concentration of 93 ng/L.

52 | P a g e



Expectedly, sulfamethoxazole was accompanied by trimethoprim in all the sites, except in the effluent.

This is in agreement with the findings by Terzić et al. (2008). The two compounds are administered as

one drug (co-trimoxazole) in the ratio of 1:5 (trimethoprim/sulfamethoxazole). Interestingly, the ratio of

their concentrations at each site (varied from 1:3-1:10) doesn‘t reflect this relation implying that they

follow different removal processes in the environment. For instance, Abellán et al. (2009) showed that

trimethoprim is more susceptible to removal by photolysis (99%) than sulfamethoxazole (38%),

respectively. This could explain the absence of trimethoprim in the effluent in contrast to

sulfamethoxazole.

Madureira et al. (2010) detected sulfamethoxazole and trimethoprim with frequencies of 33% and 34%,

respectively in the 87 samples analysed. Sulfamethoxazole had higher concentrations (53.3 ng/L)

compared to trimethoprim (15.7 ng/L) in surface water. They noted that the correlation between the

concentrations of these compounds and the ratio in which they are available in pharmaceutical

formulations cannot be used to draw direct conclusions because multiple factors can influence the

concentrations found in aquatic environments. For example, the percentage of sulfamethoxazole and

trimethoprim excreted in unchanged form by humans is 15% and 60%, respectively. The detection of

these compounds in the Nairobi River basin is consistent with their high consumption as they constitute

the highest percentage (27%) of all the drug consumption in the region.

In the effluent, sulfamethoxazole was detected at 6.2 µg/L concentration while trimethoprim was not

detected. The absence of trimethoprim could be due to its high removal efficiency (94%). However,

Gros et al. (2009) detected both compounds in effluent samples albeit at lower concentrations (116 ng/L

trimethoprim; 448 ng/L sulfamethoxazole) compared to the findings of this study.

Metronidazole was detected in 5 out of 8 sites studied. The results are in agreement with that of

Kasprzyk-Hordern et al. (2007) who detected metronidazole in river water albeit at concentrations

below method quantification limit (MQL).

It was, however, not detected in the effluent despite the fact that it is known to be non-biodegradable

(Kümmerer et al., 2000). Similarly, Gros et al. (2009) reported that metronidazole occurred in some

effluent samples in concentrations up to 295 ng/L, while it could not be detected in others. The cause of

this variation is, however, not clear as the compound is poorly removed by conventional water

treatment techniques and therefore is expected to be detected in the effluent.

Amoxicillin

Amoxicillin has been detected in both river water and effluent samples (Zuccato et al., 2010). In this

study, amoxicillin was detected in 4 out of 8 sites studied including the effluent samples. The

concentrations ranged from 0.5-1.5 mg/L. These concentrations are up to 100 fold higher than those

reported in literature (Fatta-Kassinos et al., 2011; Fent et al., 2006), drawing attention as to why the big

variation. Literature studies revealed that, often amoxicillin concentrations are lower than or in the

same order of magnitude with that of sulfamethoxazole whenever they are detected together.

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It‘s also noted that amoxicillin is susceptible to hydrolysis due to their unstable β-lactam ring,

photolysis and biodegradation (Andreozzi et al., 2004; Hirsch et al., 1999) and hence, easily eliminated

from aquatic systems. In Nairobi region, its consumption is a third of that of sulfamethoxazole, thus,

though its detection is expected, its concentration is thought to be comparable to that of

sulfamethoxazole. Based on these facts, and the fact that its response factor was low (5,085), its

concentration could have been overestimated. Cognizant of this, it is recommended that care should be

taken when interpreting these values. For the purposes of comparison, the values were not considered.

4.3.4.2 Antimalarials

Sulfadoxine was detected in five out of the seven sites studied. Its concentration ranged from 301-

1,709 ng/L. The effluent (site 7) recorded the highest concentration (1.7µg/L). This shows that the

compound is recalcitrant and may not be removed effectively by the conventional water treatment

techniques. This can be reinforced by the fact that the compound is a sulfonamide, which is known to

be more or less non biodegradable, resistant to photolysis and has low sorptivity (Luo et al., 2011).

Sulfadoxine is normally used in combination with pyrimethamine as a second-line drug in malaria

treatment. Since Kenya is a malaria prone country, the use of sulfadoxine is relatively high. There is

limited information on its occurrence, fate and removal in the environment. This study shows that

sulfadoxine concentration in the aquatic systems is in the same order of magnitude as for the other

pharmaceuticals reported, hence it‘s important for monitoring. With resistance to sulfadoxine being

reported (Zakeri et al., 2010), assessment of its ecotoxicity and human toxicity is vital especially any

possible contribution to resistance by exposure through its residues in drinking water.

4.3.4.2 Analgesics/anti-inflammatory drugs

Paracetamol was detected in 4 out of the 8 sites studied. The concentrations ranged from 1.9-16.4 µg/L,

with mean concentration of 8.6 µg/L in river water samples. Its concentration reduced significantly

downstream, with no detection at the last downstream sampling site. This could be due to dilution by

the effluent discharged into the river, which doesn‘t contain it, as well as its susceptibility to photolysis,

hydrolysis, biodegradation processes, and sorption onto suspended solids (Ranieri et al., 2011). Other

research groups have reported the occurrence of paracetamol in surface water in concentrations ranging

from ng/L to µg/L (Gros et al., 2006; Kasprzyk-Hordern et al., 2007; Togola et al., 2008; Zhang and

Zhou, 2007). Comparatively, Nödler et al. (2010) reported mean concentration of 2 µg/L in river water

samples. Similarly, paracetamol was the most often detected compound in all river water samples

analyzed by Lin and Tsai (2009) and found in high concentrations up to 15.7 µg/L. These values are in

agreement with the results of this study. It‘s the mostly used analgesic partly because it is a non-

prescriptive and easily accessible drug. Therefore, it is expected to occur in the aquatic environment. In

Kenya, it is the third most consumed drug (14%), supporting the high concentrations.

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It was, however, not detected in the effluent samples. These findings are in congruent with the results

of Nödler et al. (2010) and Grujíc et al. (2009) who didn‘t detect paracetamol in effluent, despite its

high concentrations in influent. This can be attributed to its high removal efficiency (>99%) in WWTPs

(Gómez et al., 2007; Lavén et al., 2009).

Ibuprofen has been reported in the surface water by various authors (Kim et al., 2009; Lin and Tsai,

2009). In this study, ibuprofen reported the highest concentrations ranging from 23-48µg/L in river

water samples. These values are, however, higher than 359 ng/L reported by Pailer et al. (2009). In

France, Togola and Budzinski (2007) measured mean concentrations of up to 611 ng/L in Seine River.

Ibuprofen is the second most consumed analgesic/anti-inflammatory drug after paracetamol in Nairobi

region. It‘s not surprising then, that both compounds are detected at high concentrations in the samples

tested.

In the effluent sample, a concentration of 68 µg/L was measured. Other authors have measured

ibuprofen in effluent in the range of ng/L to µg/L. Gros et al. (2009) reported concentrations up to 12

µg/L in effluent samples. This is, however, more than fivefold lower than findings of this study. Fent et

al. (2006) reviewed the occurrence of pharmaceuticals in aquatic environment and noted that ibuprofen

occurred in effluent in concentrations up to 85 µg/L. This corroborates the results of this study. The

high concentrations of ibuprofen in effluent are quite perplexing because its elimination efficiency in a

biological (activated sludge) WWTP can be greater than 95% (Sebȍk et al., 2008). However, the

elimination efficiency studies are based on activated sludge system, as opposed to waste stabilization

ponds system which is used in Nairobi. These high concentrations should be of serious concern because

ibuprofen has been reported to cause change to reproduction patterns in Medaka fish at concentration as

low as 1 µg/L and cardio abnormalities in Zebra fish (>10 µg/L) (Corcoran et al., 2010).

4.3.4.2 Antiretrovirals

Two antiretrovirals were detected in the water samples collected during this study. Nevirapine was the

most frequently detected compound (7 out of 8 sites studied) with concentration of 500-1,950 ng/L. In

Germany, Prasse et al. (2010) reported concentrations upto 17 ng/L in river water samples. In the

effluent, we recorded a concentration of 713 ng/L. Prasse et al. (2010) detected nevirapine in domestic

wastewater effluent at concentrations upto 32 ng/L. They noted that the drug is not eliminated in an

activated sludge WWTPs.

On the other hand, lamuvidine was detected in 6 out of 8 sites studied with concentrations upto 1,369

ng/L in river water samples. This is in contrast with the findings of Prasse et al. (2010) who did not

detect lamivudine in river water samples. The effluent sample recorded concentration of 1,089 ng/L.

Prasse et al. (2010) did not detect lamuvidine in effluent and noted that it can be eliminated up to more

than 93% in an activated sludge WWTP. This is, however, not confirmed by this study.

The two drugs are administered either individually or in combination with others, e.g.

stavudine/lamivudine/nevirapine in the ratio of 3:15:25 by mass. Thus, their twin detection is logical.

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Due to the high HIV/AIDS prevalence (6.3% in 2008) in Kenya, consumption of antiretrovirals is high

constituting 15% of all the pharmaceuticals consumed in the Nairobi region. Most of those who are

infected with HIV/AIDS are believed to live in the informal settlements around the city. This could

probably explain the high concentrations of the two drugs in site 3 and 5, which are situated in slum

areas. These areas have no sewerage coverage and, thus, mostly discharge their domestic waste into the

river.

Despite the significant use of antiretrovirals worldwide, there are very limited studies on their

occurrence, fate and removal in the environment. No reports have been done on their environmental

impact. With the detection of this pharmaceutical class in the river water, concern should be raised on

the possible risks it poses to the water consumers downstream. This is particularly important for

nevirapine which has been reported to develop resistance among HIV/AIDS patients (Hauser et al.,

2011). Its introduction into the environment is constant because HIV/AIDS patients administer it for

life and therefore it can be present in the environment all the time.

4.3.4.2 Antipsychotics

Despite its low consumption in Nairobi (0.03%), carbamazepine was detected in 6 out of 8 sites

studied. The concentrations in river water samples were 87-384 ng/L. This is in agreement with Nödler

et al. (2010) who reported concentration of 265 ng/L. Similar ranges have been reported (6-130 ng/L,

Grujíc et al., 2009; 20-652 ng/L, Zhang and Zhou, 2007). In the effluent, a concentration of 296ng/L

was recorded. Lavén et al. (2009) reported concentration of 410 ng/L in effluent. Similarly, Spongberg

and Witter (2008) detected concentrations of up to 111 ng/L in effluent. Its ubiquitous occurrence in the

aqueous environment is due to its recalcitrant nature and poor elimination in WWTPs (Gros et al.,

2006). Zhang et al. (2008) noted that its removal efficiency by conventional WWTP is less than 10%.

It has been noted that its concentration is some times higher in effluent than in influent, which is

attributed to enzymatic cleavage of its glucuronide conjugate (Vieno et al., 2007). It has low

biodegradability at low concentrations and low sorptivity (Zhang et al., 2008), hence its persistence in

aqueous phase. Epilepsy treatment usually lasts for life, as opposed to occasional consumption of other

pharmaceuticals; hence there is constant input of carbamazepine into natural waters (Grujíc et al.,

2009). This can also probably explain its frequent detection in surface water.

56 | P a g e

Chapter 5: Conclusion and recommendations


CHAPTER 5

CONCLUSION AND RECOMMENDATIONS

5.1 Conclusions

With the high population growth globally and emergence of new diseases as well as resurgence of other

diseases which were once considered eradicated, the consumption of pharmaceuticals is bound to reach

new heights. Environmental effects of these drugs shall continue to be getting scientists and

environmentalists concerned. Countries that are still lagging behind in the management of

―pharmaceutical wastes‖ need to start research programmes to monitor the fate, occurrence and

removal of these compounds from the environment. In this work, an overall view of the water quality

status of the Nairobi River basin has been studied. High COD and BOD have been recorded in many

sites along the river, some being higher than the values for domestic WWTP effluent, suggesting heavy

organic pollution. The parameters measured in effluent (COD, BOD, TSS) also registered higher values

than the legal standards for effluent discharged into surface water, implying inefficient treatment.

Apart from the determination of rather classical physical-chemical water parameters, most attention has

been paid towards the analysis of pharmaceutical residues as emerging organic micropollutants in the

Nairobi River basin. First, a systematic methodology based on consumption data and screening analysis

has been developed for prioritization of pharmaceutical classes and compounds for target analysis.

Starting from a first set of 43 priority compounds, a list of 14 focus compounds has been established.

Second, an innovative analytical method allowing both screening and selective target analysis of the

selected pharmaceuticals in surface water is developed making use of advanced HPLC-HRMS

equipment. The escan mode enabled full-scan analysis that was used to screen for the focus

compounds. Retention times, mass accuracy and ion intensity were determined as vital parameters to

identify the detected compounds. The more selective and sensitive MID mode was used as a pre-

confirmation tool for the target compounds identified in the escan mode, and for quantification

purposes. On the basis of well-thought criteria, a decision tree was constructed to define detected

compounds as being ‗positively‘, ‗probably‘, or ‗indicatively‘ identified. Unequivocal identification and

quantitative evaluation was obtained by the analysis of analytical standards.

The analytical standards allowed for the full confirmation of the presence of 10 pharmaceuticals in the

Nairobi River basin water belonging to 5 classes, i.e. antibiotics (trimethoprim, sulfamethoxazole,

metronidazole, amoxicillin), analgesics/anti-inflammatories (paracetamol, ibuprofen), antiretrovirals

(nevirapine, lamuvidine), antimalarials (sulfadoxine) and antipsychotics (carbamazepine). Antibiotics

were the most ubiquitous in the basin having four compounds detected. Nevirapine was the most

commonly detected compound having been detected in 7 out of the 8 sites studied. Higher

concentrations were registered in areas located near informal settlements and industrial area. This is an

indication of domestic wastewater and pharmaceutical facility waste discharges as the main sources of

pharmaceutical residues in the basin.

57 | P a g e

Chapter 5: Conclusion and recommendations


Nine out of the twelve monitored pharmaceutical compounds were detected in the effluent sample,

signifying incomplete removal by the WWTP. Generally, the compound registered mean concentrations

ranging from 251 ng/L (nevirapine) to 46 µg/L (ibuprofen). Sulfadoxine, a newly reported compound in

the aquatic system, recorded a concentration of 301-1,709 ng/L, which is in the same magnitude as the

other compounds that have been reported before.

Through the systematic development of an innovative multi-residue analytical technique, this work

provides, for the first time, data on the occurrence of frequently consumed pharmaceuticals in Kenyan

waters. The findings provide not only invaluable knowledge on the contamination of Nairobi River

basin by pharmaceuticals, but also a concrete basis for further research on the subject as well as the

need for monitoring programmes by the environmental regulatory agencies. It is a wakeup call to the

agencies involved in environmental management to start thinking beyond the conventional water

contaminants and start developing policies and programmes that would tackle the challenges of the

emerging micropollutants such as pharmaceuticals.

5.2 Recommendations

As this work is the first of its kind in Kenya, it provides the foundation on which further research can

be laid. To further improve the knowledge in this field, it is recommended that further research should

focus on:

i) The validation and evaluation of the sample preparation steps and the matrix effects on the

detection method aiming at a more comprehensive quantification of the detected pharmaceuticals.

ii) The pharmaceuticals removal efficiency of the WWTP in order to assess its actual impact in

relation to pharmaceuticals discharge into the river.

iii) The role of total suspended solids and sediments in the removal and/or redissolution of the

pharmaceuticals from the water column. High levels of total suspended solids have been reported

in the basin.

iv) The occurrence of pharmaceutical residues analysis in the drinking water abstracted from the Athi

River where the Nairobi River discharges its waters. The environmental and human health risk

posed by any detected compound should be determined through risk assessment studies.

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Appendix


Appendix I: Molecular structures of target compounds

Sulfadoxine Amoxicillin Methyldopa

Benzylpenicillin Metronidazole Trimethoprim

Sulfamethoxazole Paracetamol Carbamazepine

Zidovudine Lamivudine Ibuprofen

Nevirapine Efavirenz