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Faculty of Bioscience Engineering
Academic year 2015 – 2016
Non-equilibrium plasma in contact with water as
advanced oxidation process for decomposition of
micropollutants
Niels Wardenier
Promotor: Prof. dr. ir. Stijn Van Hulle
Promotor: Prof. dr. Ann Dumoulin
Tutor: Dr. Anton Nikiforov
Master’s dissertation submitted in partial fulfilment of the requirements
for the degree of Master of Science in Industrial Sciences: Chemistry
I
Copyright
The author, promotors and tutor give permission to make this master dissertation available for
consultation and to copy parts of this master dissertation for personal use. In the case of any
other use, the limitations of the copyright have to be respected, in particular with regard to the
obligation to state expressly the source when quoting results from this master dissertation.
Kortrijk, June 2016
The author,
Niels Wardenier
The promotors, The tutor,
Prof. dr. ir. Stijn Van Hulle Dr. Anton Nikiforov
Prof. dr. Ann Dumoulin
II
Acknowledgements
Finally finished! After nine months of hard work at the Laboratory of Industrial Water and
Ecotechnology (LIWET) and the Research Unit of Plasma Technology (RUPT), it is almost time to
finalize my master in chemical engineering technology. However, finishing this work would not be
possible, without the help of some important people. Therefore, I would like to thank all of them
which has contributed to this work.
First, I would like to thank my both promotors, prof. dr. ir. Stijn Van Hulle and prof. dr. Ann
Dumoulin. Stijn and Ann, thank you for providing this very interesting master thesis! Your critical
views, and suggestions certainly enhanced the overall quality of this work.
Next, my thank goes go to Dr. Anton Nikiforov, doctor assistant at the RUPT. Anton, thank you for
giving me the opportunity of working in your lab, and sharing your enormous knowledge about
plasma technology.
Maybe the most important person who has contributed to this work, was RUPT teaching assistant
drs. ir. Patrick Vanraes. Patrick, my sincerest gratitude for your tireless efforts, practical support
during the experiments, and our numerous discussions about plasma technology for water
treatment, AOP systems and science in general. I wish you all the best for your upcoming PhD
thesis defence, and your further academic career!
Back in Kortrijk, I also appreciated the help of ir. Yannick Verheust by the analytical part of my
thesis. Yannick, thank you for the support with the analytical procedures, especially with GC-MS
procedure development and GC-MS troubleshooting in the early months of my research work.
Aside of my supervisors and other teaching staff, also many thanks to all other thesis students
working in the analytical lab A206 in Kortrijk, and the lab of plasma technology in Ghent. Together
they created a stimulating research environment which was definitely enlightening.
Special thank goes to my girlfriend Lindsey, for the support and continuous motivation during my
thesis. Especially, I have appreciated your support in the graphical design of my master thesis. I
know this was not evident since you also had a lot of work to do in your own master year.
Last, but not least I would like to thank my parents for all support, received during my whole
studying period.
III
Table of contents List of abbreviations ........................................................................................... VI
Summary .......................................................................................................... VII
Samenvatting ................................................................................................... VIII
Introduction ........................................................................................................ 1
Chapter 1 Micropollutants ............................................................................... 3
1.1 Historical background ............................................................................................................ 3
1.2 Classification of micropollutants ............................................................................................ 3
1.3 Sources of micropollutants .................................................................................................... 4
1.3.1 PPCPs .................................................................................................................................. 5
1.3.2 Pesticides & industrial compounds .................................................................................... 5
1.4 Environmental fate of micropollutants .................................................................................. 6
1.4.1 Municipal waste water treatment ..................................................................................... 6
1.4.2 Environmental fate in MWWTPs ........................................................................................ 8
1.5 Micropollutant removal mechanisms in MWWTPs ............................................................. 10
1.5.1 Biodegradation ................................................................................................................. 10
1.5.2 Sorption ............................................................................................................................ 11
1.5.3 Volatilization..................................................................................................................... 12
Chapter 2 Advanced treatment methods for wastewater remediation ........... 13
2.1 Coagulation and flocculation................................................................................................ 13
2.2 Activated carbon filtration ................................................................................................... 14
2.3 Membrane filtration ............................................................................................................. 14
2.4 Advanced oxidation processes ............................................................................................. 16
2.4.1 AOP kinetics...................................................................................................................... 17
2.4.2 Energy efficiency .............................................................................................................. 18
2.5 Conclusion ............................................................................................................................ 20
Chapter 3 Plasma technology ......................................................................... 22
3.1 What is plasma? ................................................................................................................... 22
3.2 non-thermal plasmas ........................................................................................................... 22
3.3 Dielectric barrier discharges................................................................................................. 24
3.3.1 Energy efficiency .............................................................................................................. 26
Chapter 4 Experimental setup ........................................................................ 32
IV
4.1 Applied chemicals ................................................................................................................. 32
4.1.1 Preparation of micropollutant stock solutions................................................................. 32
4.1.2 Preparation of diluted micropollutant solutions .............................................................. 33
4.1.3 Preparation of micropollutant solutions in dichloromethane ......................................... 33
4.2 Instrumental devices ............................................................................................................ 33
4.2.1 Voltage and current probes ............................................................................................. 33
4.2.2 UV-VIS spectrophotometer .............................................................................................. 34
4.2.3 Optical emission spectrometer ........................................................................................ 34
4.2.4 GC-MS ............................................................................................................................... 34
4.3 Reactor design ...................................................................................................................... 34
4.3.1 Only plasma (1 P) single pass reactor configuration ........................................................ 35
4.3.2 Alternative single pass reactor configurations ................................................................. 36
4.3.3 Batch reactor configuration ............................................................................................. 38
4.4 Experimental procedures ..................................................................................................... 39
4.4.1 Optimization of micropollutant degradation in the DBD reactor .................................... 39
Chapter 5 Analytical procedures .................................................................... 41
5.1 Electrical diagnostics ............................................................................................................ 41
5.1.1 Power measurements ...................................................................................................... 41
5.2 Emission spectrometry ......................................................................................................... 42
5.3 Chemical diagnostics ............................................................................................................ 43
5.3.1 Determination of H2O2 ..................................................................................................... 43
5.3.2 Gas chromatography – mass spectrometry ..................................................................... 44
5.4 Toxicity analysis .................................................................................................................... 52
5.4.1 Preparation of test samples ............................................................................................. 53
5.4.2 Toxicity testing ................................................................................................................. 53
Chapter 6 Characterization of a DBD plasma reactor ...................................... 54
6.1 Spectral analysis ................................................................................................................... 54
6.1.1 Air plasma ......................................................................................................................... 54
6.1.2 Oxygen plasma ................................................................................................................. 57
6.1.3 Argon plasma .................................................................................................................... 57
6.2 Active species production in the water phase ..................................................................... 58
6.2.1 pH ..................................................................................................................................... 58
6.2.2 Conductivity ...................................................................................................................... 61
V
6.2.3 H2O2 production ............................................................................................................... 63
6.3 Conclusion ............................................................................................................................ 65
Chapter 7 Optimization of a DBD plasma reactor ........................................... 66
7.1 Adsorption ............................................................................................................................ 66
7.2 Micropollutant removal in batch reactor configuration ...................................................... 67
7.3 Micropollutant removal in single pass reactor configuration .............................................. 70
7.4 Optimization of operational parameters ............................................................................. 73
7.4.1 Effect of the working gas .................................................................................................. 73
7.4.2 Effect of the gas flowrate ................................................................................................. 74
7.4.3 Effect of the water flowrate ............................................................................................. 75
7.4.4 Effect of duty cycle ........................................................................................................... 76
7.4.5 Effect of power ................................................................................................................. 76
7.4.6 Effect of the initial concentration .................................................................................... 77
7.5 Concluding remarks .............................................................................................................. 78
7.6 Toxicity ................................................................................................................................. 79
Chapter 8 Optimization of reactor configuration ............................................ 83
8.1 Optimization of reactor configuration ................................................................................. 84
Chapter 9 Conclusions and future perspectives .............................................. 91
9.1 Conclusions .......................................................................................................................... 91
9.2 Future perspectives .............................................................................................................. 92
References ........................................................................................................ 94
Addendum 1 ................................................................................................... 115
A. DICHLORVOS ...................................................................................................................... 115
B. DIURON .............................................................................................................................. 116
C. ALACHLOR .......................................................................................................................... 117
D. BISPHENOL A ...................................................................................................................... 118
E. 1,7-Α-ETHINYLESTRADIOL.................................................................................................. 119
F. PENTACHLOROPHENOL ..................................................................................................... 120
G. CARBAMAZEPINE ............................................................................................................... 120
VI
List of abbreviations
AOP Advanced Oxidation Process
CAS Conventional Activated Sludge
DBD Dielectric Barrier Discharge
DC Duty Cycle
DOM Dissolved Organic Matter
EEO Energy efficiency per Order
GC – MS Gas Chromatography – Mass Spectrometry
HRT Hydraulic retention time
LC – MS Liquid Chromatography – Mass Spectrometry
LOD Limit Of Detection
LOQ Limit Of Quantification
MWWTP Municipal Wastewater Treatment Plant
NOM Natural Organic Matter
OES Optical Emission Spectrometry
PAC Powdered Activated Carbon
RMSE Root Mean Square Error
SIM Single Ion Monitoring
SRT Sludge Retention Time
VII
Summary
Since the early 1970s, the detection of micropollutants is systematically reported in different
water bodies. Although environmental effects of micropollutants are yet not fully understood, the
presence of micropollutants in the environment has been linked to biological effects such as the
feminization of fish populations through exposure to endocrine disruptors such as synthetic
hormones. Because of the environmental and human effects of micropollutants are still largely
unknown, public awareness against micropollutants has risen. Since municipal wastewater
treatment plants are often considered as the main access point of micropollutants into the
aquatic environment, it illustrates the incapability of biological wastewater treatment to
effectively remove micropollutants. Consequently, new advanced treatment methods for
micropollutant removal need to be investigated. Among them, advanced oxidation processes
(AOPs) have emerged as a promising water remediation technique.
This master thesis focused upon the application of non-thermal plasma as a new and
unconventional AOP for water treatment. Specifically, a DBD plasma discharge reactor with falling
water film over Zorflex® active textile is investigated towards the removal of micropollutants,
occurring in a synthetic wastewater. Therefore, eight representative micropollutants, including
the pesticides dichlorvos, diuron, atrazine, the pharmaceuticals carbamazepine and 1,7-α-
ethinylestradiol and the plasticizer bisphenol A were selected in this research. Since one of the
weakest points of AOPs is their high energy consumption, micropollutant removal in the plasma
reactor is optimized in terms of energy efficiency of the plasma reactor. Calculation of the energy
efficiency is performed by the Electrical Energy per Order (EEO) figure-of-merit. Six operational
parameters, including the working gas type, gas flowrate, water flowrate, duty cycle, applied
power and initial micropollutant concentration are investigated. Under initial parameter settings
(working gas: air, gas flow rate: 1 SLM, water flow rate: 66.30 ml min-1, duty cycle: 0.15 power:
40 W and initial concentration: 100 µg l-1), an initial EEO value of 27.4 kWh/m³ for atrazine is
calculated. Optimization of the plasma reactor increased the energy efficiency to a final EEO value
of 4.16 kWh/m³. This includes an overall improvement in energy efficiency of 84.8 %. Moreover, it
is shown in this work that the EEO value can be further decreased by slightly adapting the reactor
design. This way, our investigated DBD reactor system performs equally, or even better than
other AOP systems, used for water treatment.
Nevertheless, research into plasma technology for water treatment is not finished yet. Therefore,
some important research question still needs to be answered. Based on the experimental results
presented in this work, it is clear that additional reactor optimization in terms of effluent toxicity
can be very interesting. This should be conducted in two ways. First, overall effluent toxicity test
should be performed. Secondly, research needs to elucidate the possible reaction pathways for
the formation of harmful by-products in the liquid.
Keywords: Micropollutants, AOP, plasma technology, reactor optimization
VIII
Samenvatting
Omtrent 1970 werden micropolluenten voor het eerst op een systematische wijze gedetecteerd
in het aquatisch milieu. Hoewel de milieueffecten van deze micropolluenten nog steeds niet
volledig gekend zijn, is het wel duidelijk dat hun aanwezigheid in het aquatisch milieu gerelateerd
is aan bepaalde biologische effecten, zoals de vervrouwelijking van het vissenbestand door de
blootstelling aan hormoonontregelaars. Doordat de toxische effecten van micropolluenten op
mens en milieu grotendeels onbekend zijn, is de algemene bezorgdheid omtrent het voorkomen
van micropolluenten in (afval)water sterk gestegen. Bovendien is gebleken dat
afvalwaterzuiveringsinstallaties vaak als de belangrijkste bron beschouwd worden voor
micropolluenten in het aquatisch milieu. Dit illustreert dat biologische afvalwaterzuivering niet
geschikt is voor een effectieve, complete verwijdering van micropolluenten. Verder onderzoek
naar nieuwe, geavanceerde methodes voor de behandeling van micropolluenten is dus
noodzakelijk. Geavanceerde oxidatieprocessen blijken hiervoor een geschikt alternatief te zijn.
In deze thesis wordt niet-thermisch plasma bestudeerd als een nieuw en niet-conventioneel
geavanceerd oxidatieproces voor de behandeling van water. Meer specifiek werd onderzoek
verricht naar het gebruik van een DBD-plasmareactor met vallende waterfilm over Zorflex® textiel
voor de verwijdering van microverontreinigingen uit een synthetisch afvalwater. Acht veelvuldig
voorkomende micropolluenten werden geselecteerd, waaronder de pesticides dichloorvos,
diuron en atrazine, de farmaceutica carbamazepine en 1,7-α-ethinylestradiol en de weekmaker
bisfenol A. Aangezien het hoge energieverbruik van geavanceerde oxidatieprocessen één van hun
zwakste punten is, werd speciale aandacht besteed aan reactoroptimalisatie in functie van de
energie-efficiëntie. Hiervoor werden zes reactorparameters onderzocht, waaronder het gastype,
gasdebiet, waterdebiet, duty cycle, elektrisch vermogen en de initiële micropolluentconcentratie.
Indien standaardinstellingen gebruikt werden (gas: lucht, gasdebiet: 1 SLM, waterdebiet: 66.30 ml
min-1, duty cycle: 0.15 vermogen: 40 W en initiële concentratie: 100 µg l-1), werd een EEO-waarde
van 27.4 kWh / m³ berekend voor de verwijdering van atrazine. Door optimalisatie van de
plasmareactor kon de energie-efficiëntie verder verbeterd worden tot een minimale EEO-waarde
van 4.16 kWh/m³. Dit komt overeen met een toename in energie-efficiëntie van 84.8 %.
Bovendien werd in dit werk aangetoond dat nog hogere energie-efficiënties mogelijk zijn door
aanpassingen in het reactorontwerp. Hierdoor is de energie-efficiëntie voor de verwijdering van
atrazine in de onderzochte DBD-plasmareactor vergelijkbaar, of zelfs lager dan de energie-
efficiëntie bekomen bij andere AOP-systemen voor waterbehandeling.
Het onderzoek naar de toepassing van plasmabehandeling voor waterzuivering is evenwel nog
niet afgerond. Een aantal belangrijke onderzoeksvragen dienen nog beantwoord te worden. Op
basis van de in dit werk beschreven resultaten is het duidelijk dat verdere reactor optimalisatie in
functie van effluent toxiciteit nuttig kan zijn. Dit kan op twee manieren gebeuren. Enerzijds kan de
algemene toxiciteit getest worden. Daarnaast is het ook nuttig om de vorming van schadelijke
bijproducten in de vloeistoffase te bestuderen.
Kernwoorden: Micropolluenten, AOP, plasma technologie, reactor optimalisatie
1
Introduction
Water is a part of daily life, yet it is not unlimited. Although Earth’s surface is covered for more
than 70 %, freshwater is very rare on Earth. It is estimated that only 1 % of Earth’s water,
occurring in lakes, rivers and underground sources is directly available for human consumption as
drinking water (Kolpin et al., 2002). Consequently, nearly 2.2 billion people in more than 62
countries (i.e. a third of world’s human population), are lacking sufficient water supply. In future,
water shortages are expected to be worse, due to predicted exponential growth of human
population. Moreover, a raise in human welfare during the second part of last century, has
resulted in an increased demand for fuels, processed food, industrial chemicals, pharmaceuticals,
pesticides, and other essential products. The high consumption rates of these products go hand in
hand with the production of different waste streams. In the specific case of water contamination,
the enormous consumption of a wide variety of chemicals has resulted into the emergence of a
new type organic contaminants, also called micropollutants, in different water bodies. Municipal
wastewater treatment plants (MWWTPs) were considered as the main access point of these
micropollutants into the aquatic environment. Indeed, such treatment plants were originally
designed for the effective removal of dissolved organic matter (DOM), pathogens and easy
biodegradable nutrients such as nitrogen (N) and phosphorus (P). Unfortunately, biological
treatment technologies only permit partial micropollutant removal. Because effects on human
and environment of these organic contaminants were largely unknown, public concern has raised
towards the occurrence of micropollutants in our natural water bodies. Therefore, research
interest in advanced removal technologies for micropollutants was stimulated. Among them, both
biological (membrane technology, trickling filters) and physicochemical processes (coagulation-
flocculation, adsorption, membrane technology, AOPs) have been studied. Physicochemical
methods are usually more effective for micropollutant removal from (waste)water, but often only
micropollutant transfer from one phase to another is achieved, instead of micropollutant
destruction. Advanced oxidation processes (AOPs), on the other hand, have shown a high
micropollutant decomposition potential. These oxidation processes aim the production of several
chemical active species (predominantly OH• radicals). However AOPs are, at least currently,
associated with high chemical and energy costs. Therefore, current research focusses on the
development of novel, energy efficient AOPs. One example of such an unconventional AOP is
based on non-thermal plasma technology. Non-thermal plasma is able to generate different
reactive species such as hydroxyl radicals (OH•), ozone (O3), hydrogen peroxide (H2O2) and many
others.
Outline and objective
Although much progress has been made towards the engineering of non-thermal plasmas, there
is still a huge knowledge gap about the main oxidation mechanisms that take place in the plasma
and at the plasma-liquid interface. Hence, further research is required to elucidate and optimize
the main mechanisms contributing to micropollutant degradation by means of plasma
technology. Therefore, this master thesis project envisages the investigation of DBD plasma
technology for water treatment purposes.
2
From a practical point of view, future application of plasma technology for water treatment
purposes requires an energy efficient removal of micropollutants. In this regard, the main purpose
of this work is to optimize a DBD plasma reactor system for the removal of micropollutants from a
synthetic wastewater, containing eight model compounds.
This work consists of nine chapters. Following this introduction, the three next chapters form the
literature review of this master thesis. The first chapter deals with the occurrence of
micropollutants in the aquatic environment. After a short introduction about micropollutant
classification, their environmental fate will be revisited. Special attention will be paid to the
mechanisms contributing to an incomplete removal of micropollutants in existing wastewater
treatment plants. Building further on these findings, the second chapter deals with some
alternative wastewater treatment technologies, and their predicted efficiency for micropollutant
removal. The final chapter of this literature review explores the fundamentals of (non-thermal)
plasma technology as a possible AOP system for water treatment. The main focus is put on the
different reactor designs, and their energy efficiency towards micropollutant removal. The second
part of this thesis comprises the experimental section, and is divided in two chapters. Chapter 4
provides a description of all applied reagents, used plasma reactor configurations and other
instrumental devices, whereas chapter 5 focusses on the applied analytical methods for
micropollutant qualification and quantification.
The third part of this work deals with the experimental results and their discussion. The
experimental results are provided in three chapters. Chapter 6 deals with the characterization of
the DBD plasma by means of optical emission spectroscopy, and an investigation of solutions
parameters such and pH and conductivity. In chapter 7, micropollutant decomposition in the
plasma reactor is investigated, and the individual effect of each operational parameter on the
energy efficiency of micropollutant degradation is discussed. Finally, additional micropollutant
removal is examined by reactor configuration optimization. These results are presented in chapter
8.
The last chapter of this work provides a general conclusion, and a short discussion about some
future perspectives about plasma technology for water remediation purposes.
3
Chapter 1 Micropollutants
1.1 HISTORICAL BACKGROUND
A good water quality is essential for human life. Unfortunately, a lot of our natural water bodies
have already been contaminated with chemical, physical or pathogenic substances since ancient
times. For example, old Greek and Sanskrit writings reported about the application of early water
treatment techniques such as sand filtration for the clarification of water. Around 1500 B.C. the
Egyptians applied the chemical ‘alum’ to enhance particle settlement of suspended solids. This
way, they invented the early principles of the coagulation-flocculation method. Later, the Romans
constructed the first sophisticated systems for wastewater management (Musalaiah et al., 2016).
In contrast, the Middle-Ages were characterized by a general lack to any scientific innovation. In
the early Middle-Ages, there was no system for wastewater management, and waste was usually
discharged into the streets. In the 19th century, raw sewage produced by households and the fast-
growing industry was discharged into rivers and channels. It was the catalyst of the rapid
development of many severe water-borne illnesses such as cholera and typhoid, which were
reported throughout the century. Because water pollution became a public health concern, it was
the driving force for the development of the first sewage networks, which transported industrial
and raw sewage to the first municipal wastewater treatment plants (MWWTPs). Most of these
MWWTPs were based on the activated sludge treatment system (Wiessman et al. ,2007).
Around 1950 a global population growth, combined with raising living standards forced industries
to the large scale production of pharmaceuticals, inorganic pesticides, plasticizers, and other
synthetic materials (Eggen et al., 2014). Since 1970 these anthropogenic substances has been
systematically detected in very low concentrations in the effluent of wastewater treatment plants
(Deblonde et al., 2011; Verlicchi et al., 2012), surface water (Moons & Van Der Bruggen, 2006),
drinking water (Jones et al., 2005; Touraud et al., 2011), and even groundwater (Lapworth et al.,
2012). Because of the general lack on information about the toxicity of these trace contaminants
to human and environmental health, public concern has risen against these so-called emerging
contaminants or micropollutants. Nevertheless, most of these compounds are officially
unregulated, or are still undergoing a regulation process.
1.2 CLASSIFICATION OF MICROPOLLUTANTS
To date, many attempts have been made in order to classify trace contaminants. Several
researchers have divided identified pollutants in three broad groups: (i) pharmaceuticals and
personal care products (PPCPs), (ii) pesticides and (iii) industrial compounds (Murray et al., 2010).
According to their chemical structure, micropollutants are further divided in different subclasses.
Table 1-1 highlights most important subgroups, and some commonly detected micropollutants
belonging to each subgroup.
4
Table 1-1: Important micropollutants, subgroups and examples of some commonly found micropollutants ( Scharf et al., 2002; Murray et al., 2010; Clara et al., 2012; Margot et al., 2015)
1.3 SOURCES OF MICROPOLLUTANTS
The occurrence of micropollutants in the environment has been traced back to three major
applicants: (i) household, (ii) industry, and (iii) agriculture (Halling-Sorensen et al., 1998; Heberer,
2002; Kümmerer, 2009; Verlicchi et al., 2010). Micropollutants originating from these applicants
usually enter the aquatic environment through different pathways. The main entrance routes for
micropollutants into the environment are depicted figure 1-1 (Jiménez, 2013).
Group Subgroup Examples
Pharmaceuticals
Analgesics Paracetamol, Tramadol
Anti-epileptics and
anticonvulsants
Carbamazepine, diazepam
Antipyretics and NSAIDs Ibuprofen, naproxen
Antibiotics Ciprofloxacin, amoxicillin
β-blockers Atenolol, propanolol
Lipid regulators Gemfibrozil, benzafibrate
Iodinated contrast media Ioprimide, iomeprol
Hormones Estrone, 1,7-α-ethinylestradiol
Personal care products
UV filters Octocrylene, butyl
methoxydbenzoylmethane
Preservatives Methylparabens,
ethylparabens
Fragrances Galaxolide, tonalide
Pesticides
Herbicides Atrazine, alachlor, diuron
Fungicides Propramocarb, vinclozolin
Insecticides Dichlorvos, aldrin
Industrial compounds
Food additives Acesulfame, Saccharin
Plasticizers Bisphenol A, diethyl phthalate
(DEP)
Anticorrosives benzotriazole
Chelating agents Ethylenediaminetetraacetic
acid (EDTA)
Flame retardants Tetrabromobisphenol A,
trimethyl phoshate
Heavy metals Mercury, Cadmium
Polycyclic aromatic
hydrocarbons
Benzo(a)pyrene, napthalene
Surfactants Alkyl sulfate , alcohol
ethoxylate
5
1.3.1 PPCPs
Pharmaceuticals constitute a diverse group of compounds administered to treat human and
animal diseases. They include analgesics, anti-epileptics and anticonvulsants, antipyretics and
non-steroidal anti-inflammatory drugs (NSAIDs), antibiotics, β-blockers, lipid regulators, iodinated
contrast media and hormones ( Ternes et al., 2002; Margot et al., 2015).
When pharmaceuticals are applied to treat human diseases, pharmaceuticals are, at least partly,
absorbed by the human body, and subsequently subjected to various metabolic reactions
(Cunningham, 2004; Vieno et al., 2007). Hence, pharmaceuticals can be excreted either as parent
compound or as a transformation product. These transformation products can be as toxic as the
parent compound, or even more toxic (Zwiener & Frimmel, 2000). Excretion of pharmaceuticals
and their transformation products usually occurs through urine or faeces. By means of the sewage
network, these substances are transported to a MWWTP where they are, in best case, partially
removed from wastewater (Ternes & Joss, 2006).
Pharmaceuticals originating from agricultural applications such as animal farming and aquaculture
can enter into the aquatic environment in different ways. Animal excretions are commonly used
as a fertilizer (Boxall, 2004). Therefore, they end up in the soil. Subsequently, the parent
compound and their metabolites can end up in surface water by run-off. Alternatively, persistent
compounds with high soil mobility can emerge in the ground water. Pharmaceuticals applied in
aquaculture applications are directly released into the surface water.
Personal care products can be found either as an active ingredient or preservative in cosmetics,
washing lotions, sunscreen agents, perfumes toiletries, hair styling products or fragrances
(Daughton & Ternes, 1999; Margot et al., 2015). Due to their usage on the external body, they
enter into the sewage network mainly through wash-off during showering of bathering (Margot et
al., 2015; Ternes et al., 2002). Generally, personal care products do not undergo metabolic
transformation processes.
1.3.2 Pesticides & industrial compounds
Pesticides include all substances that are used to protect crops against several diseases. They
have been largely produced on an industrial scale since 1930. Nowadays, more than 1600
pesticides are commercially available (Gray, 2008). As they are mainly applied by spraying
techniques, pesticides are mainly transported in air, and subsequently washed out during periods
of rainfall (Autin et al., 2012). If pesticides are directly administered to soil, they may adsorb to
soil particles, being further degraded by soil biota. More persistent pesticides are able to
percolate through soil, and can eventually reach the ground water.
In addition to PPCPS and pesticides, several other micropollutants are commonly detected into
the environment. They are often referred to as “industrial compounds” (Murray et al., 2010).
They comprise a wide variety of chemical substances, including food additives, plasticizers,
6
anticorrosives, chelating agents, flame retardants, heavy metals, polycyclic aromatic
hydrocarbons and surfactants (Margot et al., 2015). Many of these compounds are directly
discharged in the surface water.
Figure 1-1: Distribution of micropollutants in the environment (Jiminez, 2013)
1.4 ENVIRONMENTAL FATE OF MICROPOLLUTANTS
1.4.1 Municipal waste water treatment
After application, most micropollutants are released into the sewage network, being transported
to a municipal waste water treatment plant (MWWTP). These MWWTPs has been identified as a
major point source of micropollutants into the environment (Heberer, 2002). Municipal
wastewater treatment combines several physical, chemical and biological treatment processes in
order to remove pathogens, dissolved organic matter (DOM) and nutrients such as nitrogen and
phosphorus from wastewater (Michael et al., 2013). A typical MWWTP is represented in figure 1-2
(Monteiro & Boxall, 2010).
As can be seen in figure 1-2, wastewater treatment in a municipal WWTP generally comprises two
treatment stages: a primary treatment step, followed by and secondary treatment stage (Michael
et al., 2013). In some MWWTPs, the treatment train has been adopted by an additional tertiary
treatment step to enhance effluent quality (Shon et al., 2006; Monteiro & Boxall, 2010).
7
1.4.1.1 Pretreatment and primary treatment
Prior to primary treatment, the incoming water typically undergoes a pretreatment step.
Wastewater pretreatment typically consists of a series of mechanical and physico-chemical
treatment steps. First, large debris and coarse materials are removed by bar screens. This is a
necessary step, since large objects may damage pumps and other mechanical equipment in
further treatment steps. Next, floating materials such as fat and oil are removed from the
incoming water (Margot et al., 2015). In a last step, primary treatment is initiated. During primary
treatment the incoming water is held in a sedimentation tank (primary clarifier) for several hours,
allowing particle settlement through a physical sedimentation process. Overall, about 50 – 60 %
of total suspended solids (TSS), 15 – 30 % of total nitrogen (TN), and 30 % of total phosphorus (TP)
content is removed through primary settlement (Poon & Chu, 1999; Kumar et al., 2007). The solid
phase (primary sludge) is discharged, and further treated in an (an)aerobic digester.
Subsequently, the liquid phase, also referred to as the primary clarified water, is send to a second
treatment stage.
1.4.1.2 Secondary treatment
Secondary treatment usually combines a biological process in an aeration reactor with a
sedimentation process in a secondary settler. Although several biological processes have been
evaluated, the conventional activated sludge process (CAS) is regarded as the most utilized
approach for municipal and industrial wastewater treatment (Verstraete & Vlaeminck, 2011).
In the CAS process, primary effluent is introduced into the aeration tank, and vigorously mixed
with activated sludge to form a mixed liquor. Next, soluble and colloidal organic matter is
converted by a diversified group of microorganisms to carbon dioxide and water, in the presence
of oxygen. Additionally, nitrogen and phosphorus are removed in the CAS process (Stare et al.,
2007; Van Veldhuizen et al., 1999). Moreover, a fraction of the converted organic matter is used
for the production of new biomass cells. After a certain contact time, the treated wastewater is
introduced into the secondary clarifier. The secondary classifier allows the separation of the
activated sludge into a solid phase (sludge) and a liquid phase (clarified water) through a
sedimentation process. Clarified liquid from the secondary clarifier is discharged to the
environment as secondary effluent. Accumulated sludge is partly recycled to the aeration tank,
and the excess sludge is managed to an aerobic or anaerobic digestor (Verstraete & Vlaeminck,
2011).
In addition to the CAS process, also other secondary treatment processes have found their
application in municipal wastewater treatment. Biofilters (trickling filters), membrane bioreactors
and rotating biological contactors all have found their application in wastewater technology. In
some cases, a more complete micropollutant removal has been observed (De Wever et al., 2007;
de Cazes et al., 2014). Better removal efficiencies are mainly attributed through a higher hydraulic
retention time (HRT) (Gros et al., 2010), and a longer sludge retention time (SRT) (Clara et
al.,2005; Petrie et al., 2014). A complete discussion of these alternative biological water
treatment processes, however, is beyond the scope of this work.
8
1.4.1.3 Tertiary treatment
Before secondary effluent is discharged into the aquatic environment, it can undergo an
additional, tertiary treatment step to enhance effluent quality. For example, chlorination is
typically used for wastewater disinfection due to its low costs. Nevertheless, the chlorination of
secondary effluent induces the formation of harmful, often carcinogenic disinfection products
such as trihalomethanes (Nieuwenhuijsen et al., 2000). Therefore, some alternatives, such as
disinfection of secondary effluent by ozonation or ultraviolet radiation are increasingly finding
their application in wastewater treatment (Lindenauer & Darby, 1994; Hassen et al., 2000;
Heberer, 2002).
Figure 1-2: Schematic overview of a conventional wastewater treatment plant (Monteiro & Boxall, 2010)
1.4.2 Environmental fate in MWWTPs
The presence of micropollutants in the environment has shown that MWWTP are incapable to
fully eliminate micropollutants. Indeed, conventional MWWTPs were originally designed for the
effective removal of dissolved organic matter (DOM), pathogens and easy removable nutrients
such as nitrogen and phosphorus in the gram per liter range (Ternes et al., 2002; Forrez et al.,
2011; Luo et al., 2014).
Daughton and Ternes (1999) and Heberer (2002) were some of the first authors to publish some
data about the removal efficiency of micropollutants in MWWTPs. Additionally, Verlicchi et al.
(2012) reviewed the environmental fate of several hundreds of pharmaceuticals in MWWTPs,
mainly in Europe and North America. Very recently, Margot et al. (2015) did the same for a wide
variety of commonly detected micropollutants. Table 1-2 summarizes the average removal
efficiency in MWWTPs for some selected micropollutants.
9
Table 1-2: Environmental fate of some most commonly found micropollutants , adapted from Margot et al. (2015)
Micropollutant Subdivision WWTP removal (%) Reference
Pesticides
Atrazine Herbicide 23 [1] [2]
Diuron Herbicide 33 [1] [2] [3]
Hexachlorobenzene Fungicide 90 [4]
Endosulfan Insecticide 84 [4]
Heptachlor Insecticide 91 [4]
Pharmaceuticals
Carbamazepine Anticonvulsant 16 [1] [3] [5]
1,7-α-ethinylestradiol Hormone 60 [1] [6]
Estrone Hormone 76 [7]
Naproxen NSAID 40 [1] [3]
Ibuprofen NSAID 80 [3] [5] [8]
Diclofenac NSAID 20 [1] [3] [5]
Paracetamol Analgesic 100 [1] [5] [8]
Ciprofloxacin Antibiotic 69 [1] [3] [5]
Benzafibrate Anti-
cholesterol
41 [1] [3]
Iopromide Contrast
medium
41 [1] [3]
Atenolol B-blocker 41 [1] [3] [5]
Propanolol B-Blocker 28 [1] [3] [5]
Personal care products
Methylparaben Preservative 95 [9] [10]
Galoxolide Fragrance 85 [9] [10]
Octocrylene UV-filter 96 [10] [11]
Industrial compounds
Bisphenol A Plasticizer 80 [1] [6]
Anthracene PAH 90 [4]
References: [1] (Margot et al., 2013) [2] (Singer et al., 2010) [3] (Matthijs et al., 1999) [4] (Katsoyiannis & Samara, 2004) [5] (Verlicchi et al., 2012) [6] (Gardner et al., 2013) [7] (Lubliner et al., 2010) [8] (Oulton et al., 2010) [9] (Clara et al., 2011) [10] (Kupper et al., 2006) [11] (Bester et al., 2008).
As can be seen in table 1-2, strong variations in removal efficiencies between micropollutants are
observed. These differences can be mainly ascribed to the differences in physico-chemical
properties between micropollutants ( Suarez et al., 2008; Omil et al., 2010). Further, it is
10
noteworthy that some significant differences in removal efficiencies between individual WWTPs
are observed. This phenomenon is mainly attributed to different operational conditions, such as
biomass concentration, sludge retention time (SRT), hydraulic retention time (HRT), configuration
and type of WWTP, between individual treatment plants (Verlicchi et al., 2012).
1.5 MICROPOLLUTANT REMOVAL MECHANISMS IN MWWTPs
Studies concerning the environmental fate of micropollutants have pointed out that some
micropollutants are partially degraded in MWWTPs, even though MWWTPs were originally not
designed for this purpose (Carballa et al., 2004; Salgado et al., 2012). The removal of
micropollutants in MWWTPs is ascribed to MWWTP operational parameters (sludge age,
hydraulic retention time, as well as physicochemical properties (Henry’s law coefficient (kH),
octanol-water partitioning coefficient (log KOW), acidity (pKa) and solid-liquid partitioning
coefficient (Kd)) of the micropollutants.
Generally, the environmental fate of micropollutants in current MWWTPs can be attributed to
three main mechanisms, including: (i) biodegradation, (ii) sorption to solids, and (iii) volatilization
(Suarez et al., 2008; Omil et al., 2010; Margot et al., 2015). Additional micropollutant removal can
be achieved by the adaptation of tertiary treatment methods on secondary effluent (Luo et al.,
2014). Some of these tertiary treatment methods will be discussed in the second chapter.
1.5.1 Biodegradation
Biodegradation refers to the transformation process in which a parent compound is converted to
one or more intermediate products by interaction with microorganisms. It is generally recognized
as the most important degradation process during wastewater treatment (Margot et al., 2015).
The biodegradation of micropollutants has been studied in a few works. For example, the
degradation kinetics through biodegradation in a batch reactor was studied by Joss et al. (2006).
In their work, the biodegradation rates of 40 pharmaceuticals in a CAS batch reactor was
evaluated. From their kinetic modelling results, the authors have calculated the reaction rate
constant through biodegradation for every individual compound. It was found that micropollutant
removal in the batch reactor followed a pseudo first order degradation kinetics (eq. 1-1):
wbiolT SSCk
dt
dC eq. 1-1
With CT the total micropollutant concentration (µg l-1), Cw the micropollutant concentration in the
liquid (µg l-1), SS the amount of suspended solids (g l-1), t the reaction time (d) and kbio the reaction
rate constant for biodegradation (l g-1 SS d-1).
According to the reaction rates, micropollutants were classified as (i) hardly degradable if kbiol >
0,1 g-1 SS d-1 (ii) , moderately biodegradable if 0.1 g-1 SS d-1 < kbiol < 10 g-1 SS d-1, and (iii) highly
biodegradable if kbiol > 10 g-1 SS d-1. In addition to the work of Joss et al. (2006), Tadkaew et al.
11
(2011) evaluated the relationship between the parent compound and their tendency to
biodegradation. It was found that short linear molecules were more easily degraded than large
aromatic compounds, and that the occurrence of electron withdrawing groups (halogens,
sulphate groups) significantly reduces the biodegradation potential of micropollutants.
1.5.2 Sorption
Another important contributor to the elimination of micropollutants in MWWTPs is sorption onto
solids such as sludge or particulate matter (Rogers, 1996 ; Suarez et al., 2008). This is certainly the
case when the organic contaminant is hydrophobic or positively charged (Joss et al., 2006). The
sorption of micropollutants onto activated sludge comprises two main processes: (i) absorption
and (ii) adsorption.
Absorption refers to the hydrophobic interactions of aliphatic and aromatic groups of a
compound with the lipophilic cell membrane of the sludge and the lipid fractions of the sludge.
Rogers (1996) proposed the octanol-water partitioning coefficient (log KOW) as a general rule of
thumb to predict the absorption potential of micropollutants onto sludge: log KOW < 2.5 indicates
a low sorption potential, 2.5 < log KOW < 4.0 indicates a medium sorption potential and log KOW >
4.0 indicates a high sorption potential.
Adsorption is mainly caused by electrostatic and ionic interactions between micropollutants and
negatively charged surfaces of biomass cells (Ternes et al., 2004). The solid-liquid partitioning
coefficient (Kd) has been proposed as a relative accurate indicator for the sorption potential of
micropollutants, since it combines the effects of both acidity (pKa) and lipophilicity (log KOW) on
the sorption potential (Luo et al., 2014).
The solid-liquid distribution coefficient (Kd) is defined as the ratio between the micropollutant
concentration in the solid and the liquid phase at equilibrium conditions (eq. 1-2) (Omil et al.,
2010).
SS.C
CK
lelubso
sorbedd eq. 1-2
Where Kd represents the solid-liquid distribution coefficient, in l kg-1, Csoluble the pollutant
concentration in the liquid phase in µg l-1 , Csorbed the sorbed pollutant concentration, in µg l-1 and
SS the suspended solids concentration, in kg l-1 (Suarez et al., 2008). Substances having Kd values
higher than 500 l kg-1 (log Kd < 2.70) are easily sorbed onto sludge, whereas micropollutants with
Kd values below 300 l kg-1 ( log Kd < 2.48 ) are showing low sorption potential (Omil et al., 2010).
Below a Kd value of 1 l kg-1, sorption onto sludge can be neglected (Carballa et al., 2007).
12
1.5.3 Volatilization
Volatilization involves the process whereby a pollutant is transferred from the liquid phase to the
gas phase. In CAS processes, the volatilization potential is affected by the volatility of the
compound (Henry’s law coefficient, kH) and the operational conditions of the MWWTP (agitation,
aeration, pH and T of the wastewater) (Pomiès et al., 2013). The volatilization potential pollutant
can be roughly estimated from the Henry’s law coefficient (kH), indicating how easily
micropollutants will be removed through aeration. Most micropollutants are characterized by a
Henry’s law constant ranging from 10-2 to 10-3 mol/(m-3 Pa) (Stenstrom et al., 1989).
The volatilized fraction yvolatilized of a compound during aeration is given in eq. 1-3 (Joss et al.,
2006):
airH q.
T.R.1000
k
dvolatilize e1y
eq. 1-3
Where kH is the Henry’s law coefficient, in mol/(m-3 Pa) , R the universal gas constant (8,314 J/mol
K), T the temperature in Kelvin , and qair the applied aeration rate, expressed in m³air m-3wastewater.
In most MWWTPs involving CAS secondary treatment, wastewater is typically aerated with an air
flowrate ranging from 5 to 15 m³air m-3wastewater (Pomiès et al., 2013). Under these circumstances,
micropollutant removal through volatilization has found to be completely negligible for most
pharmaceuticals and hormones, nearly negligible for fragrance compounds such as tonalide and
galoxide and very significant for celestolide, which is also a musk fragrance (Suarez et al., 2008).
13
Chapter 2 Advanced treatment methods for
wastewater remediation
In the previous chapter, it was shown that a relative new class of emerging micropollutants was
systematically detected in different water bodies. A general lack on scientific knowledge about
the potential adverse effects of organic contaminants on both human and environment health has
raised public concern towards the occurrence of micropollutants in the environment. Moreover, it
was found that current conventional MWWTPs were quite ineffective to remove micropollutants.
This has forced the water industry to explore new advanced treatment methods such as the
coagulation – flocculation method, activated carbon filtration, membrane filtration and advanced
oxidation processes to tackle the detrimental effects of micropollutant releasement in the aquatic
environment. However, none of these specific treatment technologies assures a complete
micropollutant removal for all micropollutants. The first four sections provide a rather theoretical
investigation of above mentioned water remediation techniques, whereas the last section of this
chapter summarizes the major advantages and drawbacks of each investigated technique for
micropollutant elimination.
2.1 COAGULATION AND FLOCCULATION
The coagulation-flocculation process has been practiced as a water treatment process since
ancient times (Musalaiah et al., 2016). The technique was originally applied for the removal of
turbidity from wastewater, caused by suspended solids and colloidal particles (Thébault et al.,
1981). Because some micropollutants, especially the non-polar ones, tend to adsorb onto
suspended solids (see chapter 1), the coagulation-flocculation process is studied as a possible
water polishing method for the abatement of micropollutants from wastewater and drinking
water (Ternes et al., 2002; Thuy et al., 2008).
Wastewater often contains colloidal and suspended particles, which are characterized by a
negative surface charge (Thuy et al., 2008). Consequently, aggregation of these charged particles
is hindered by electrostatic repulsion forces. Therefore, a neutralization of the net charge will
enhance suspended solids to settle down. This particle settlement is achieved by the coagulation-
flocculation method. The application of the coagulation and flocculation technique usually
involves a coagulation step, followed by a flocculation step. During coagulation, a coagulant is
added to the wastewater, to destabilize charged particles. Hence, it causes part of the suspended
solids to stick together in small aggregates, called microflocs. Commonly used coagulants include
positive charged iron- and aluminium salts, such as ferric chloride (FeCl3), iron sulphate (Fe2(SO4)2)
or aluminium sulphate (Al2(SO4)3 ) (Westerhoff et al., 2005). In the flocculation the aggregation of
small microflocs into large flocs is further enhanced by thoroughly the treated solution. In most
cases, this aggregation process is encouraged through the addition of flocculants. Among them,
polymeric substances are commonly employed as flocculants. Finally, large flocs are separated
from the treated water by adapting a physical separation technique, such as rapid sand filtration.
14
2.2 ACTIVATED CARBON FILTRATION
Adsorption is defined as the process in which a chemical substance (adsorbate) adheres on the
surface of a solid substance (adsorbent) (Grassi et al., 2012). Adsorption processes are widely
used to remove micropollutants from different water bodies. Typical adsorbents used for water
treatment purposes are: (i) activated carbon, (ii) zeolites, (iii) molecular sieves and (iv) polymeric
adsorbents (Qiu et al., 2009). Among the different materials that have been used as an adsorbent,
the vast majority of the studies concerning the removal of micropollutants from (waste)water
have used activated carbon as adsorbent. Activated carbon is characterized by a very porous
structure with a large surface area, ranging from 600 to 2000 m² g-1 (Grassi et al., 2012). Two
types of activated carbon are available for water remediation purposes: powdered activated
carbon (PAC) and granular activated carbon (GAC) (Ternes et al., 2002).
Adsorption on solid surface is mainly a physical process, and is called “physisorption”. Therefore,
the adsorption process is driven by physical phenomena, such as electrostatic interactions and
Van der Waals forces between adsorbent and adsorbate (Grasi et al., 2012). However, the exact
amount of adsorbed adsorbate on a specific adsorbent under equilibrium conditions is function of
the physical nature of the adsorbate parameters, such as micropollutant concentration and
temperature. The adsorption kinetics can be expressed by a number mathematical models,
“called isotherms” which are experimentally determined. Among them, the Freundlich model (eq.
2-1) and the Langmuir model (eq. 2-2) are mostly used (Foo & Hameed, 2010).
n/1
eFe c.Kq eq. 2-1
With qe the amount of adsorbate adsorbed on the adsorbent at equilibrium conditions (mg g-1).
K and n are experimentally determined constants, and ce is the initial micropollutant
concentration (mg l-1)
e
e0e
c.b1
c.b.Qq
eq. 2-2
In this equation Q0 represents the maximum monolayer coverage capacity (mg g-1), b is the
Langmuir isotherm constant (dm³ mg-1), and ce the micropollutant concentration (mg l-1) under
equilibrium concentration.
2.3 MEMBRANE FILTRATION
Another new advanced water polishing technique is based on membrane technology. Membrane
filtration processes cover a multitude of physical separation processes in which a gaseous or liquid
stream is split into two different side streams. In (waste)water treatment applications, the
incoming water is separated over a semi-permeable membrane in two different side streams: a
clean water stream, called the retentate, and a concentrate stream which contains the
compounds stopped by the membrane (figure 2-1) (Verliefde, 2008).
15
Figure 2-1: Schematic example of a membrane filtration unit
According to their driving forces, different membrane processes could be distinguished: (i)
concentration driven membrane processes, (ii) pressure driven processes, and (iii) electrical
potential driven processes (Strathmann, 2001). In most water treatment applications, the driving
force is predominantly ascribed to a pressure difference applied over the membrane (Van Der
Bruggen et al., 2003).
Membrane processes aredivided in four important processes: (i) microfiltration, (ii) ultrafiltration,
(iii) nanofiltration and (iv) reverse osmosis (RO). Nanofiltration and RO processes are particularly
important for the elimination of organic contaminants (Xu et al., 2010). Microfiltration and
ultrafiltration are incapable for micropollutant removal due to a too high pore size (Ozaki, 2004).
Table 2-1: General characteristics of membranes used for membrane filtration (Ozaki, 2004)
Properties Membrane filtration
Ultrafiltration Nanofiltration Reverse osmosis
Pore size > 100 nm 2 – 5 nm 3 – 10 nm 10 nm – 1 µm
MWCO < 350 < 150 300 > 300
Applied pressure 1 – 10 MPa 0,3 – 1,5 MPa 0,01 – 0,3 MPa 0,005 – 0,2 MPa
The total removal of organic contaminants is often expressed as the percentage of rejection, and
can be calculated with eq. 2-3 (Kimura et al., 2003):
100.c
c1%R
i,f
i,p
eq. 2-3
With R the total removal percentage of compound i across the membrane, cp,i the micropollutant
concentration in the permeate stream (µg l-1), and cf,i the micropollutant concentration in the
feed stream (µg l-1).
The rejection of micropollutants on nanofiltration and reverse osmosis membranes is dominated
by three key pollutant-membrane interactions, being electrostatic interactions, adsorptive
interactions and steric hindrance (sieving effect). All these parameters are affected by the
operational conditions (feed composition, pH, temperature) and the physicochemical properties
16
of both the micropollutant and the membrane (molecular weight, pore size, hydrophobicity)
(Bellona et al., 2004).
2.4 ADVANCED OXIDATION PROCESSES
Most advanced water treatment methods mentioned earlier in this chapter are associated with
some inherent disadvantages, such as the production of a toxic waste stream, which should be
further treated in an additional treatment step. Moreover, most of these advanced treatment
methods are incapable to reduce persistent substances to sufficiently low concentration levels
(Mantzavinos & Psillakis, 2004). Therefore, alternative water remediation techniques based on
advanced oxidation processes (AOPs) have been proposed to meet our new, stringent
environmental quality standards. Advanced oxidation processes (AOPs) have been suggested as
an effective and sustainable treatment method, since these techniques can avoid the production
of harmful by-products and waste streams.
Advanced Oxidation Processes (AOPs) aim the in situ generation of hydroxyl radicals (OH•) (Glaze
& Kang, 1989; Andreozzi et al., 1999; Hernandez et al., 2002) In addition to the generation of
hydroxyl radicals (OH•), also other oxidative chemical species can be produced, including ozone
(O3), atomic oxygen (O), hydrogen peroxide (H2O2) (Loures et al., 2013). However, hydroxyl
radicals have been considered as the main important chemical species in AOPs since they are
characterized by a significant higher redox potential 2.80 V in comparison with other oxidative
species (table 2-2).
Table 2-2: Standard redox potentials of some commonly used oxidants (Loures et al., 2013)
Oxidizing species Oxidation potential (V)
Fluorine (F) 3.03
Hydroxyl radical (OH•) 2.80
Atomic oxygen (O) 2.42
Ozone (O3) 2.07
Hydrogen peroxide (H2O2) 1.78
Hydroperoxyl radical (HO2•) 1.70
Permanganate (MnO4-) 1.68
Hypobromous acid (HOBr) 1.59
Chlorine dioxide (ClO2) 1.57
Hypochlorous acid (HOCl) 1.49
Chlorine (Cl2) 1.36
The exact mechanism of the OH radical production depends on the kind of advanced oxidation
process that is applied. Currently, these OH• generation mechanisms have been well studied in
literature. For the most practical applied methods, more information can be found in several
reviews (Munter, 2001; Esplugas et al., 2002; Klavarioti et al., 2009; Poyatos et al., 2010; Ghatak,
2014). In all cases, the generation of highly reactive OH• radicals requires the input of energy.
Energy can be delivered by a chemical, photochemical, sonochemical or electrochemical process
(Klavarioti et al., 2009). Accordingly, all different AOPs described in literature can be divided into
17
chemical, photochemical, sonochemical and electrochemical AOPs. Some examples are
highlighted in table 2-3.
Table 2-3: Classification of AOPs according to the energy source Oturan & Aaron, 2014)
Chemical AOPs Fotochemical AOPs
Sonochemical AOPs
Electrochemical AOPs
O3/OH- O3/UV O3/Ultrasound Electro-fenton
O3/H2O2 (peroxone)
H2O2/UV H2O2/Ultrasound Anodic oxidation
Fe2+ + H2O2 (Fenton)
Fe2+ + H2O2 + UV Photo-fenton
TiO2/UV
Among the several types of AOP investigated over the last 30 years, most practical applied AOPs
are using combinations of hydrogen peroxide (H2O2), ozone (O3) and/or UV radiation (Tezcanli-
Güyer & Ince, 2004). A few pilot-scale or full scale applications of such chemical or photochemical
AOPs are mentioned in literature (Heberer, 2002; Kruithof et al., 2007). Other sonochemical and
electrochemical AOPs has only been evaluated on laboratory scale (Goel et al., 2004; Sirés &
Brillas, 2012).
2.4.1 AOP kinetics
In all AOP processes, it was found that hydroxyl radicals (OH•) were the most important reactive
species. These highly reactive oxidants react in a very fast, unselective way with a lot of different
pollutants, including phenols (Chen & Ray, 1998; Di Paola et al., 2003; Chiou et al., 2008), textile
dyes (Dutta et al., 2001; Houas et al., 2001; Islam et al., 2013), pesticides (Acero et al., 2000;
Benitez et al., 2002; Tizaoui et al., 2011), pharmaceuticals (Deng et al., 2013; Rodríguez et al.,
2014) and various other micropollutants (Cater et al., 2000; Wang et al., 2009; Tan et al., 2013).
AOPs have the potential to fully mineralize micropollutants to carbon dioxide (CO2) and water
(H2O), although this is rarely applied, since it would require large energy or chemical inputs.
Although the overall AOP degradation chemistry is different for each micropollutant, three major
reaction mechanisms have been established, being (i) the abstraction of a hydrogen atom (eq. 2-
4), (ii) the electrophilic addition of a OH• radical to a multiple bond (eq. 2-5), and (iii) electron
transfer reactions (eq. 2-6) (Legrini et al., 1993):
OHROHRH 2 eq. 2-4
2222 CRCOHROHCRCR eq. 2-5
OHRXOHRX eq. 2-6
18
Hence, the reaction between micropollutants and hydroxyl radicals produces secondary radicals,
which can affect further micropollutant decomposition (Neyens & Baeyens, 2003). The overall
degradation progress usually follows a first order kinetics in both micropollutant and hydroxyl
radical concentration, and can be described with eq. 2-7:
MOHM,OHM c.c.k
dt
dc eq. 2-7
With cM the micropollutant concentration (µg l-1), kOH the second order reaction rate constant (M-1
s-1) for the reaction of OH radicals (OH•) with micropollutant molecules M, and cOH• the hydroxyl
radical concentration (µg l-1).
When the concentration of hydroxyl radicals is much lower than the micropollutant concentration
(cM >> cOH•), the reaction kinetics can be simplified from a second-order reaction kinetics to a
pseudo first order reaction kinetics (eq. 2-8 ):
M0M c.k
dt
dc eq. 2-8
In this equation, the first-order reaction rate constant (k0) for the pseudo-first order reaction is
calculated with eq. 2-9:
OHM,OH0 c.kk eq. 2-9
The second order reaction rate constants have been extensively studied with various methods
over the last years. Very fast reactions were generally observed, with second order kinetic
constants towards different kinds of micropollutants (kOH•,M) varying between 2,2. 107 and 1,8.
1010 M-1 s-1 (Yao & Haag, 1991; Von Gunten, 2003; Jin et al., 2012; Sudhakaran & Amy, 2013).
2.4.2 Energy efficiency
A number of AOPs haven been developed during the last 30 years (Klavarioti et al., 2009). Most of
them have shown to be efficient in the removal of micropollutants from (waste)water. The overall
effectiveness of an AOP is affected by a number of parameters, such as the type of organic
compound, the initial concentration, the specific process conditions and, in the case of
wastewater treatment, the occurrence of radical scavengers such as sulphate (SO42-) and
bicarbonate (HCO3-) (Stasinakis, 2008). Although some full-scale applications were already
commercially available, a comprehensive comparison of different AOP performances is generally
lacking. To this end, Bolton et al. (1996) introduced the electrical energy per order (EEO) figure-of-
merit to compare the electrical energy performance of different AOPs. In the case of low
micropollutant concentrations, the EEO represents the electrical energy input, expressed in kWh,
required to reduce the initial micropollutant concentration with 1 order of magnitude (90%), in 1
19
m³ contaminated water (Bolton & Tumas, 1996; Cater et al., 2000). In batch reactor systems, the
EEO can be calculated according to eq. 2-10 (Bolton & Tumas, 1996):
f
i
c
clog.60.V
1000.t.PEEO eq. 2-10
With EEO the electrical energy in kWh, P the applied power in W, t the operation time (min) ci, the
initial micropollutant concentration (µg l-1) and cf the final micropollutant concentration after a
certain treatment time t.
Note that eq. 2-10 can only be used for batch reactor systems. For continuous flow reactor, eq. 2-
10 is adapted to eq. 2-11:
f
i
C
Clog.F
PEEO eq. 2-11
With F the total water flow rate of the solution under treatment, in m³ h-1. The other symbols are
defined as in eq. 2-10.
To the authors knowledge, only a limited number of comparative studies are conducted to
compare the EEO values for different AOPs. Recently, Lester et al. (2011) evaluated the
application of different UV and O3 based advanced oxidation processes for the decomposition of
an antibiotic (ciprofloxacin), and an antineoplastic drug (cyclophosphamide). The energy
efficiency (EEO) for removal of both compounds with different AOPs is presented in table 2-4.
Table 2-4: energy efficiency for the removal of ciprofloxacin and cyclophosphamide with different UV and O3 based AOP
AOP EEO
Ciprofloxacin cyclophosphamide
UV 16.3 70.7
UV/O3 5.2 59.1
UV/H2O2/O3 5.1 37.8
O3 3.2 1.7
H2O2/O3 2.2 7.2
According to the results of Lester et al. it is concluded that O3 based processes were more
efficient than UV based processes. Furthermore, the combination of different treatment methods
resulted in a higher energy efficiency. The peroxone process (O3 + H2O2) was considered as the
most energy-efficient process.
20
Nevertheless, chemical and photochemical AOPs have not been compared with other
sonochemical and electrochemical AOPS. However, Oturan and Aaron pointed out in their review
that chemical AOPs, such as the Fenton process (Fe2+ + H2O2) or the peroxone process (O3 + H2O2)
usually perform worse in comparison with sonochemical and electrochemical methods (Oturan &
Aaron, 2014). However, the authors did not validate this claim with research data. Hence, it
remains still very interesting to elaborate the exact influence of different chemical,
photochemical, sonochemical and electrochemical AOPs on the overall process efficiency.
2.5 CONCLUSION
In this chapter, several advanced treatment methods for the elimination of micropollutants from
wastewater have been described. However, future application of a certain water remediation
method will be based on the capacity of micropollutant decomposition, and its associated energy
efficiency.
To this end, Westernhoff et al. (2005) have compared the feasibility of several water treatment
methods (coagulation-flocculation, adsorption on activated carbon, O3/H2O2 and nanofiltration)
towards micropollutant removal from surface water. These results are presented in table 7-5.
% removal
Compound Coagulation-Flocculation
Adsorption (PAC)
O3/H2O2 Nanofiltration FeCl3 Al2(SO4)3
PPCPs
1,7-α-ethinylestradiol 0 16 67 98 77
Carbamazepine 0 7 55 98 61
Diazepam 0 5 53 85 75
Diclofenac 0 0 64 96 74
Estriol 0 4 54 98 63
Galaxolide 15 18 63 89 -
Gemfibrozil 2 20 0 98 15
Iopromide 0 12 33 60 37
Naproxen 0 0 87 93 89
Pentoxyfylline 0 2 65 98 66
Triclosan 0 13 93 82 97
Pesticides
Anthracene 0 0 77 91 -
Atrazine 0 0 54 52 66
Heptachlor 15 18 63 89 -
According to the results obtained by Westernhoff et al. (2005), micropollutant removal by means
of the coagulation-flocculation method is considered to be almost negligible for most
micropollutants, especially when FeCl3 is used as a coagulant. In some cases, higher removal
percentages, up to 20 %, are reached by applying Al2(SO4)3 as a coagulant. More recent research
reports seem to confirm these observations. Vieno et al. (2007) studied the removal of 11
21
frequently detected pharmaceuticals from surface water. The contribution of coagulation-
flocculation with ferric chloride was found to be very low (5-35 %) for all investigated compounds.
Next to coagulation-flocculation, adsorption on activated carbon is commonly employed for
micropollutant removal from drinking water. According to table 2-5 an average removal efficiency
of 65 % is observed for the majority of micropollutants. However, other compounds, i.e. the more
polar ones (atrazine, iopromide) show less tendency to adsorb on activated carbon. Indeed, the
degree of adsorption on activated carbon is affected by several physicochemical properties of
both adsorbate (log KOW pKa, molecular size target molecule) and adsorbent (texture, pore size
and surface area). Because activated carbon surface is rather hydrophobic, non-polar
micropollutants (log KOW) are more easily adsorbed (Kovalova et al., 2013).
Aside of drinking water treatment, activated carbon have also been applied for wastewater
treatment. Since organic matter is ubiquitously present in wastewater, usually high PAC dosages
are required (Margot et al., 2013). Furthermore, competition between micropollutant for
adsorption, and pore-blocking by natural organic matter (NOM) significantly reduce the overall
adsorption capacity (Koh et al., 2008; Luo et al., 2014).
Just as activated carbon filtration, processes based on membrane technology (nanofiltration,
reverse osmosis) also exhibit good micropollutant removal efficiencies. In comparison with
activated adsorption slightly higher removal efficiencies are reached for most pollutants.
However, some operational and maintenance costs, such as membrane fouling and the high
energy consumption especially for reversed osmosis processes, are limiting future applications
(Petala et al., 2006).
Although high micropollutant removal efficiencies are reached by adsorption on activated carbon
and membrane technology, it should be emphasized that nor activated carbon filtration, nor
membrane based technologies succeed in effective degradation of micropollutants. Instead, both
treatment realize micropollutant transfer from one phase to another, resulting in a toxic waste
stream production, which should be disposed. Moreover, polar micropollutants are still not
adequately removed to sufficiently low concentration levels. To overcome these problems, the
application of ozonation and other AOPs need to be considered. AOPs have the potential to
effectively mineralize micropollutants. Indeed for the peroxone process (O3 + H2O2), high removal
efficiencies (> 90 %) are reported for the majority of the compounds mentioned in table 2-5.
However, AOPs are also associated with high energy costs, and further research needs to
concentrate on the development of new, energy efficient AOPs.
22
Chapter 3 Plasma technology
The existence of plasma was discovered by William Crookes, which identified ‘plasma’ as the
fourth state of matter in 1879. The term ‘plasma’ was first introduced by Irving Langmuir in 1928,
who described the phenomenon ‘plasma’ as an electrically quasi neutral gas, consisting of positive
ions, free electrons and neutral gas atoms (Langmuir, 1928).
3.1 WHAT IS PLASMA?
From a fundamental point of view, matter consists of four fundamental states of matter: the
solid, liquid, gas and plasma state. Phase transformations between those states of matter are
possible through the addition of thermal or electrical energy. For example, when energy is added
to a substance in the solid state, it will be converted to a liquid. If sufficient heat is added, the
liquid particles will vaporize to the gas phase. Another addition of energy will finally convert the
gas to the plasma state. The phase transition from the gas phase to the plasma based is based on
the partial ionization of the gas molecules (Yamamoto & Okubo, 2007) (figure 3-1).
Figure 3-1: the transition states of matter on application of heat (Reddy, 2014)
3.2 NON-THERMAL PLASMAS
In the early research stage, artificial plasmas were easily produced in gas discharge tubes. It
consists of two parallel metal electrodes, surrounded by a volume of gas. Originally, few neutral
gas molecules in the gas tube are ionized by external influences (cosmic radiation,
photoionization, natural radioactivity), resulting in the occurrence of electron-ion pairs (Conrads
& Schmidt, 2000; Fridman et al., 2005). If a DC powered voltage is supplied to the electrodes a
homogeneous electric field (E) is formed between both electrodes (Mededovic, 2007). Under the
influence of the electric field free electrons are accelerated towards the anode, producing a small
background current, up to 10-6 A (Raizer , 1987). As the voltage is increased, primary electrons are
further accelerated in the direction of the anode, gaining kinetic energy by the applied electric
field. On their way to the anode, primary electrons can collide and react with neutral gas atoms,
producing secondary electron-ion pair (Fridman et al., 2005). This process is often referred to as
an “electron avalanche” or “Townsend discharge” (figure 3-2). At this point, a strong increase in
current is observed (figure 3-3).
23
Figure 3-2: Illustration of a Townsend discharge (left) and a streamer discharge (right) Chu & Lu (2002)
Figure 3-3: I-V characteristics of a typical low-pressure gas discharge
Among different low temperature (non-thermal) plasmas dielectric barrier discharges (DBD),
corona discharges, microwave discharges, inductively coupled plasmas, gliding discharges and arc
discharges (Bogaerts et al., 2002). Some plasma discharges, together with some specific
characteristics are highlighted in table 3-1 (Nehra et al., 2008). Since this thesis deals with DBD
plasma, only DBD discharges will be briefly discussed.
24
Table 3-1: Overview of different plasma discharges and their characteristics (adapted from Nehra et al., 2008)
Parameters Corona discharge dielectric barrier discharge
Atmospheric plasma pressure
jet
Atmospheric glow micro
hollow cathode discharge
Applied Power DC AC or DF RF (13,5 MHz) DC
Pressure 1 bar 1 bar 1 bar 1 bar
Electron +/- 5 eV 1 – 10 eV 1-2 eV -
Electron density 109 – 1013 1012 – 1015 1011 – 1012 -
Breakdown voltage
10 – 50 kV 5-25 kV 0,05-0,2 kV -
Tmax 273 K 300 K 400 K 2000 K
3.3 DIELECTRIC BARRIER DISCHARGES
In 1857, Ernst Wermer von Siemens reported for the first time about dielectric barrier discharges,
also called “silent discharges”, for the generation of ozone from air (Kogelschatz, 2003). Up till the
mid 1990s dielectric barrier discharges have found their main application in the construction of
industrial ozonators (Eliasson et al., 1987; Kogelschatz, 2003). Over the last two decades,
dielectric barrier discharges attracted more and more attention in mostly applications, such as
water treatment, air pollution control and green chemistry in general environmental (Aerts et al.,
2013; Pringle et al., 2004; Thevenet et al.,2014). Next to the application of dielectric barrier
discharges for environmental purposes, DBD plasmas are extensively studied in medicinal and
industrial applications.
The scheme of a typical planar DBD device is given in figure 3-4 (Wagner et al., 2003). Such a
reactor consists of two parallel (planar) electrodes separated by a dielectric barrier. Specifically,
the dielectrical barrier can be constructed by covering at least one of the electrodes with a
dielectric material (silica, quartz, mica, ceramic materials, etc). In most cases, quartz glass is used
as a dielectric.
Figure 3-4: Configuration of planar dielectric barrier discharge reactor (Wagner et al., 2003)
25
The physics of an atmospheric pressure DBD discharges shows many similarities with those of
classical gas discharges, mentioned before (Kogelschatz, 2003). Due to the coverage of on one of
the electrodes, DBDs are initiated by the application of an AC powered voltage on the high
voltage electrode (anode). This results in the formation of a negative charge on the anode. When
the applied electrical field reaches the breakdown voltage, a Townsend discharge is initiated
followed by the propagation of a streamer discharge. In a typical DBD, the electron avalanche to
streamer transition is finalized on a timescale of ± 10 ns (Chirokov et al., 2005). During the
electron avalanche, electrons are accelerated through high energy levels. Subsequently, collisions,
of highly energetic electrons with gas molecules leads to the formation of different, reactive
species, such as hydroxyl radicals (OH•), ozone (O3), atomic oxygen (O) and hydrogen peroxide
(H2O2) in the gas phase, together with physical phenomena such as intense UV radiation (Ghezzar
et al., 2013).
The exact generation mechanisms of all these active species will not be discussed in detail here. In
the experimental section (chapter 6) the most important reactions, contributing to micropollutant
decomposition, are discussed. In order to have a complete overview of the production of highly
reactive species in both the gas and the liquid phase, the reader is referred to some excellent
review papers. For example, Eliasson et al. (1987) and Kogelschatz (2003) have discussed the gas
phase kinetics and ionization reactions taking place in the plasma, together with the gas phase
production of ozone. Bruggeman & Schram (2010) have reviewed the production of OH• radicals.
According to their review at least 30 possible reactions pathways are identified. In contrast, Locke
& Shih (2011) provided an overall reaction mechanism for hydrogen peroxide production in the
liquid phase. Finally, Brisset et al. (2008) Lukes et al. (2012) and Lukes et al. (2014) have
established the formation of different nitrogen containing species in the bulk liquid for plasma
discharges in air.
To date, plasma technology have been performed for the decomposition of different organic
compounds in water. In the early research stage, plasma-assisted decomposition of
micropollutants was limited to some very simple compounds, including phenols (Bubnov et al.,
2007; Marotta et al., 2011), synthetic dyes (Magureanu et al., 2008; Xue et al., 2008) and other
easy degradable organic compounds such acetic acid (Ognier et al., 2009). Later, research interest
more and more focused upon the application of plasma for the decomposition of
pharmaceuticals (Magureanu et al., 2010; Krause et al., 2011; Rong et al., 2014), antibiotics (Rong
& Sun, 2014) and pesticides (Vanraes et al., 2015a; Vanraes et al., 2015b, Feng et al., 2016).
Different liquid-liquid and liquid-gas phase reactors have been designed, and evaluated for
micropollutant decomposition. For instance, Krause et al., Dobrin et al. , Rong & Sun and Banashik
et al. have been working with corona discharge reactors (Banaschik et al., 2015; Dobrin et al.,
2013; Krause et al., 2009; Rong & Sun, 2014), whereas Jovic et al., Aonyas et al., Rong & Sun,
Dojcinovic et al., Vanraes et al., and Feng et al. investigated DBD reactors with falling water films
(Aonyas et al., 2016; Dojčinović et al., 2011; Feng et al., 2016; Jovic et al., 2014; Rong & Sun, 2015;
Vanraes et al., 2015b). Plasma discharges directly in the liquid phase were also studied (Lukes et
al., 2005). In an attempt to classify all existing reactors, Locke et al. (2006) and Bruggeman & Leys
26
(2009) identified two to three different groups of plasma reactors, depending on the plasma-
water phase distribution. Recently, this approach was extended to six reactor groups by Vanraes
et al. (2016) (table 3-2).
Table 3-2: Classification of different plasma reactors used for water treatment
Discharge Reactor type
Directly in the water bulk Electrohydraulic discharge reactor
Directly in the water bulk with externally applied bubbles Bubble discharge reactor
In the gas phase, over a water film Gas phase discharge reactor
In the gas phase with water drops Spray discharge reactor
Combination of previous type Hybrid reactor
Not in contact with the solution under treatment Remote discharge reactor
This work will predominantly deal with the optimization of micropollutant decomposition in a gas
phase reactor design (chapter 6). Due to small adaptations to this reactor type, a remote
discharge reactor, and hybrid reactor will also be investigated for the elimination of
micropollutants (chapter 8).
3.3.1 Energy efficiency
The optimal reactor design should aim for a high energy efficiency in removing the majority of
organic compounds. Unfortunately, only limited comparative studies concerning the energy
efficiency of different reactor designs has been conducted (Malik, 2010; Jiang et al., 2014). This
can be explained by the limited accessibility to several plasma reactors amongst different plasma
groups working on plasma treatment for water remediation. Malik was the first to compare the
energy efficiencies of 27 commonly used plasma reactors (Malik, 2010). In his review article, Malik
theoretically compared the energy efficiencies of 27 frequently used plasma reactors for the
removal of synthetic dyes (Malik, 2010). Accordingly, the energy yield G50 (eq. 3-1) was chosen for
the comparison of different reactor designs. The energy yield G50 gives an indication about the
energy required, in order to degrade 50 % of the initial micropollutant concentration:
60A
50 10.6,3.2ln.P
V.C.k.
2
1G eq. 3-1
With G50 the energy yield (g/kWh), V the treated volume (l), C0 the initial concentration (µg/l) and
P the applied power (W).
According to the comparative research of Malik, it was found that pulsed corona discharges are
preferred above dielectric barrier discharges. Further, plasma discharges in the gas phase yielded
higher energy efficiencies than plasma discharges directly in the water bulk. Moreover, the usage
of oxygen gas as discharge medium resulted in the highest energy efficiency, whereas discharges
in air yielded a significant lower energy efficiency. It was also concluded that the most energy
27
efficient reactors are the ones which spray the treated solution as fine droplets in the plasma
zone. The contribution of some reactor parameters (applied power, discharge type, discharge
medium and the method of solution treatment) to the energy efficient elimination of
micropollutants is illustrated in table 3-3 (Malik, 2010).
Table 3-3: Energy efficiency of different plasma reactor designs (Malik, 2010)
Energy efficiency High Moderate Low
Applied power Pulsed DC Pulsed AC Continuous AC or DC
Discharge type Pulsed corona
discharge
Pulsed dielectric
barrier discharge
DC discharges
Discharge medium Oxygen Air Liquid
Treated solution as Fine droplets Thin water film Bulk liquid
However, it should be emphasized that Malik’s review only included a limited number of plasma
reactors (27). In literature, many more reactor designs are described for micropollutant
decomposition. Moreover a comparison of energy efficiency in different plasma reactors by
means of the G50 energy yield parameter should be avoided, since it is strongly influenced by the
initial micropollutant concentration. Hence, only plasma reactors using a comparable initial
concentration should be taken in consideration when evaluating reactor performance. A more
suitable calculation method for comparison of energy efficiencies among different plasma
reactors was introduced earlier by Bolton et al. (1996), and is based on the energy efficiency per
order of magnitude (EEO). A relationship between the EEO and G50 is given in eq. 3-2 (Vanraes et
al., 2015b):
50
03
G
C.10.10ln.
5,0ln
5,0EEO eq. 3-2
With C0 the initial micropollutant concentration in g l-1 and G50 the energy yield in g kWh-1.
The EEO concept was already described in previous chapter (section 2.4.2, equations 2-10 – 2-11),
and is commonly used for a comparison of energy efficiency among AOPs (Cater et al., 2000;
Safarzadeh-Amiri, 2001; Lester et al., 2011).
Note that in this research, a pulsed DBD reactor over moving water was used, instead of a pulsed
DC corona discharge reactor which is, according to Malik, expected to be more energy efficient.
However the choice of a AC pulsed DBD reactor with falling water film above a pulsed DC corona
discharge was based on several reasons. First, DBD plasma discharges are usually more stable,
and usually operate at relative low powers in comparison of corona discharge reactors. Due to
lower applied power, heat losses through Joule heating effects are negligible, just as the thermal
decomposition of long living oxidants (O3 , H2O2) due to high temperatures.
28
Further, Malik entirely focused upon the electrical energy applied to the plasma reactor.
However, the energy consumption by water pumping needs also to be considered. Usually, the
energy cost for water spraying is higher than just pumping water, in order to create a thin water
film. Due to these large energy demands for water spraying systems, especially in large scale
applications, a reactor design with falling water film is preferred.
Nevertheless, the main problem of micropollutant elimination by plasma technology as a stand-
alone process, is its high energy cost. Malik (2010) calculated that the average energy efficiency
for removal of textile dyes in different plasma reactors was in the range 0.029 – 12.7 g kWh-1 (EEO
= 2.61 - 687 kWh/m³), which is still quite high for implementation in existing MWWTPs. To
overcome these problems, the combination of plasma technology with the addition of catalysts
has been suggested as an interesting way to improve the energy efficiency of micropollutant
elimination by plasma. This way, several plasma-catalytic systems has been proposed. Two
commonly used catalysts are titanium dioxide (TiO2) and activated carbon. The application of
these catalysts in plasma systems is discussed below. For a complete overview of other applied
catalysts in plasma-catalytic systems, the reader is referred to Jiang et al. (2014).
Heterogeneous photocatalysis, using TiO2 is considered as a promising stand-alone AOP processes
for mineralization of micropollutants in wastewater treatment (Islam et al., 2013). This AOP relies
on the production of OH• radicals by irradiation of a semiconductor catalyst surface (Fujishima et
al., 2000; Herrmann, 1999). To this end, many semiconductors, including ZnO, CdS and TiO2 have
been investigated as possible photocatalysts (Augugliaro et al., 2012). Among them, titanium
dioxide (TiO2) is a popular choice, since it is a low cost material, characterized by a low toxicity and
high chemical inertness (Jiang et al., 2014). To date, however, no commercial applications of TiO2
based heterogeneous photocatalytical AOPs in pilot scale reactors are known. Major drawback
which impedes the practical application of heterogeneous photocatalysis in pilot-scale reactors, is
the poor absorption capacity of TiO2 for solar light (Pichat, 2015). Therefore, the catalyst surface is
usually irradiated with UV lamps, which increases the overall cost of this AOP system.
In this context, the application of TiO2 in plasma-catalytic systems is particularly attractive.
Indeed, several plasma reactors (glow discharge reactors, DBD reactors) are known to emit a
considerable amount of UV radiation (Ghezzar et al., 2013). Interaction of UV light with a
wavelength (λ) smaller than 380 nm may result in the formation of conduction band electrons
(ecb-) and valence band holes (hvb+). If the holes subtract electrons from water molecules,
adsorbed on the catalyst surface, OH radicals are formed. In the same time, free electrons can
react with O2 molecules, that will be reduced to a superoxide radical anion (O2•-). The overall
reaction mechanism is presented by the reactions r.3-1 – r.3-3 (Augugliaro et al., 2012; Ghodbane
et al., 2014).
vbcb22 heTiOhTiO r. 3-1
2cb2 OeO r. 3-2
29
HOHhOH vb2 r. 3-3
According to the reactions r.3-1 – r.3-3, a certain amount of hydroxyl radicals is produced through
the interaction between UV radiation produced by the plasma, and the TiO2 surface. Note that
also a significant hydroxyl radical production originates from the plasma discharge. Accordingly,
the combination of plasma technology with heterogeneous photocatalysis on TiO2 may led to an
enhanced hydroxyl radical production in comparison with plasma treatment alone (Jiang et al.,
2014).
The synergetic effect of plasma treatment and heterogeneous photocatalysis on TiO2 have been
confirmed in a number of studies. Ghezzar et al. (2013) investigated the degradation of a 54 mg l-1
synthetic dye solution (yellow tartrazine) in a coaxial DBD plasma reactor with and without the
dosage of TiO2. If TiO2 was added to the liquid phase (concentration: 4 g l-1), 96 % degradation of
yellow tartrazine was reported, versus only 11 % degradation in the absence of TiO2. This
corresponds to an EEO value of 106 kWh/m³ with and 897 kWh/m³ without TiO2. In addition,
similar results have also been reported for the degradation of phenol in a pulsed corona discharge
system, where the plasma discharge was applied directly in the liquid (Lukes et al., 2005). The
energy efficiency of phenol removal (EEO) with and without TiO2 addition was calculated as EEO =
1312 kWh/m³ and EEO = 1546 kWh/m³, respectively (Lukes et al., 2005).
Next to the application of TiO2 as a catalyst, activated carbon is also commonly investigated. The
usage of powdered activated carbon (PAC) was initially introduced by Grymonpré et al.
(Grymonpré et al., 1999; Grymonpré et al., 2003). The authors studied the degradation of phenol
in a pulsed corona plasma system, operating in oxygen. Higher removal percentages were
observed in the presence of activated carbon (89 % phenol degradation vs. 40 % phenol
degradation in the absence PAC. Next to the higher removal percentages, a higher energy
efficiency was reached (EEO = 132 kWh/m² with PAC dosage, versus EEO = 291 kWh/m³ without
PAC dosage).
Higher micropollutant removal efficiencies in combined plasma-PAC catalytic systems can be
explained by the adsorption of micropollutants on PAC. Aside of micropollutant adsorption, ozone
(O3) and hydrogen peroxide (H2O2), occurring in the liquid phase due to plasma treatment, tend to
adsorb on the activated carbon surface. On the catalytic surface O3 and H2O2 will decompose
through reactions with surface functional groups (especially hydroxyl groups) present at the
activated carbon catalytic surface. Reactions r.3-4 – r.3-6 illustrate the decomposition of ozone,
whereas reactions r.3-7 – r.3-8 show the decomposition of H2O2 on PAC. According to these
reactions, it can be seen that PAC is continuously regenerated in the reactor system (Sanchez-Polo
et al., 2005; Hao et al., 2009; Kurniawan & Lo, 2009).
223 OHOACHACHO r. 3-4
OHACOOHACO 23 r. 3-5
ACOOACOO 23 r. 3-6
30
OHOHACOHAC 22 r. 3-7
222 HOHACOHAC r. 3-8
Further, reactions r.3-5 and r.3-7 show that ozone and hydrogen peroxide decomposition on
activated carbon is accompanied with the production of OH radicals close to the catalytic surface,
resulting in a more efficient reaction between adsorbed micropollutants and hydroxyl radicals
(r.3-9 – r.3-10) (Jiang et al., 2014):
MACMAC r. 3-9
ermediatesintACOHMAC r. 3-10
Different research groups intensively studied the relationship between activated carbon dosage
and enhanced micropollutant elimination of phenol (Qu et al., 2013) and other compounds such
as methyl orange (Zhang et al., 2007) and pentachlorophenol (Qu et al., 2009; Lu et al., 2012). In
all studies, the application of PAC resulted in significant higher degradation percentages and
higher energy efficiencies.
Recently, activated carbon fibres (ACF) have also found their application in plasma catalytic
reactor designs. For example, Xin et al. (2016) investigated the effect of DBD plasma treatment
and activated carbon fibres on the degradation of triclosan. A synergetic effect between DBD
plasma treatment and adsorption on ACF fibres system was found. By an input power of 80 W, a
10 mg l-1 triclosan solution was degraded for 93 %, within 18 minutes of operation in the DBD
plasma-ACF fibre system. Under the same conditions, 85 % degradation of triclosan was reported
by application of only DBD treatment.
Based on the findings of Malik (2010) concerning the energy efficient for different reactor designs,
and the benefits of micropollutant elimination by combining plasma technology with the addition
of catalysts, the construction of a pulsed DBD reactor with moving water film over an activated
carbon textile (“Zorflex®”) has been accomplished at the RUPT in the years 2013-2014. The
applicability of this innovative reactor design has already been successfully tested, in single
compound tests i.e. for the decomposition of the pesticides alachlor, atrazine, diuron,
isoproturon, pentachlorobenzene and α-hexachlorocyclohexane (Vanraes et al., 2015b, Vanraes
2016). Although the results were very promising for single compound elimination of
micropollutants, still a lot of research questions needs to be answered, in order to more deeply
understand the oxidation mechanisms taking place on the Zorflex® textile, and in the liquid phase.
Moreover, the energy efficiency of micropollutant elimination in a certain plasma-catalytic system
depends on several other process parameters. These parameters can be loosely divided in (i)
operational parameters and (ii) solution parameters (Jiang et al., 2014). Operational parameters,
as for example working gas type, gas flowrate, voltage, water flowrate and applied power are
easy to control. However, optimization of these experimental conditions is necessary since their
exact contribution to the degradation of micropollutants is still partially unknown, and expected
to be dependent on the reactor configuration.
31
It has to be emphasized that it is the aim of this study, to achieve a high energy efficient removal
for a wide variety of micropollutants into the developed pulsed DBD plasma reactor. In this
regard, a synthetic wastewater containing 5 pesticides (atrazine, alachlor, dichlorvos, diuron,
pentachlorophenol), 2 pharmaceuticals (carbamazepine, 1,7-α-ethinylestradiol) and 1 plasticizer
(bisphenol A) will be prepared. A selection of these micropollutants was based on the following
criteria. First, all micropollutants investigated in this research were already systematically
detected in secondary effluent, originating from existing MWWTPs. Secondly, most of these
micropollutants are suspected to cause potential adverse effect on human and environment.
Thirdly, all of these compounds are easily measurable by adapting chromatographical analysis on
GC-MS.
32
Chapter 4 Experimental setup
Current chapter gives a detailed description about the chemicals used throughout this work. First,
all applied chemicals and their preparation procedures are discussed. Then, instrumental devices,
and a complete overview of the reactor setup is described. The final section provides a brief
explanation of applied experimental procedures.
4.1 APPLIED CHEMICALS
Table 4-1 gives an overview of all applied reagents throughout this work. All chemicals were used without any further purification.
Table 4-1: Summary of applied chemicals in this work
Name Chemical formula Purity Manufacturer
Atrazine C8H14ClN5 > 98 % Sigma Aldrich®
Alachlor C14H20ClNO2 99.8 % Sigma Aldrich®
Diuron C9H10Cl2N2O 99.6 % Sigma Aldrich®
Bisphenol A C15H16O2 > 99 % Sigma Aldrich®
Dichlorvos C4H7Cl2O4P 98.8 % Sigma Aldrich®
Pentachlorophenol C6HCl5O 99 % Sigma Aldrich®
Carbamazepine C15H15N2O 99 % Sigma Aldrich®
1,7-α ethinylestradiol C20H24O2 99.8% Sigma Aldrich®
Dichloromethane CH2Cl2 > 99.8 % Carl roth®
Hydrogen peroxide H2O2 30 % Carl roth®
4.1.1 Preparation of micropollutant stock solutions
Due to the low water solubility of most micropollutants, saturated stock solutions (see table 4-2)
were prepared in deionized water with a maximal conductivity varying between 0.50 and 2.00
µS/cm. For saturated solutions of DIU, ATR, PCF, ALA, BISA, CARB and ETH, an appropriate amount
of powdered micropollutant was weighed on an analytical balance (Sartorius, model Quintix 224-
1S), quantitatively transferred into a volumetric flask, and eventually filled up with deionized
water to a final volume of 500 ml. A DVOS stock solution was prepared by dissolving 7.6 µl DVOS
in 500 ml deionized water. All solutions were continuously mixed at 300 rpm on a magnetic stirrer
for at least 3 hours. Next, saturated solution were filtered with Rotilabo cellulose filtration paper
(type 601).
33
Table 4-2: Saturated concentrations of prepared stock solutions
Micropollutant Saturated concentration (mg l-
1)
References
Dichlorvos (DVOS) 25 -
Diuron (DIU) 42 (Giacomazzi & Cochet, 2004)
Atrazine (ATR) 30 (Edwards et al., 1992)
Pentachlorophenol (PCF) 14 (Arcand et al., 1995)
Alachlor (ALA) 242 (Edwards et al., 1992)
Bisphenol A (BISA) 300 (Shareef et al., 2006)
Carbamazepine (CARB) 17.7 (Zhang et al., 2008)
1,7-α-Ethinylestradiol (ETH) 11.3 (Shareef et al., 2006)
4.1.2 Preparation of diluted micropollutant solutions
Solutions containing 1 mg l-1 of each micropollutant were prepared by diluting an appropriate
volume of the saturated solutions. Before the start of a plasma experiment, the stock solution
was further diluted to the desired concentration (100 µg l-1 or 200 µg l-1).
4.1.3 Preparation of micropollutant solutions in dichloromethane
Next to micropollutant stock solutions in water, micropollutant stock solutions in
dichloromethane (CH2Cl2) were prepared. A 1 g l-1 stock solution containing the micropollutants
dichlorvos (DVOS), diuron (DIU), atrazine (ATR), alachlor (ALA), pentachlorophenol (PCF),
bisphenol A (BISA), carbamazepine (CARB), and 1,7-α-ethinylestradiol (ETH) was prepared by
accurate weighing 10 mg of each micropollutant in a 20 ml vial. 10 ml dichloromethane (CH2Cl2)
was added, and the solution was gently mixed by hand shaking. Next, a 100 mg l-1 solution was
obtained by ten times diluting the stock solution with dichloromethane. Finally, 1 ml solution was
transferred into a GC-MS vial for GC-MS method optimization.
4.2 INSTRUMENTAL DEVICES
4.2.1 Voltage and current probes
A dual channel Tektronic TDS 1002 with a maximum frequency of 60 MHz was used for
simultaneous, real time monitoring of voltage and current waveforms. Voltage was measured
with a Tektronic P6015 HV probe, connected to channel 1 of the oscilloscope. Current was
measured with a Pearson 2877 current probe, connected to channel 2 of the oscilloscope. Data-
acquisition was performed by transferring the oscilloscope data over a R232 port to a Dell latitude
D829 laptop.
34
4.2.2 UV-VIS spectrophotometer
UV-VIS absorption spectra are recorded using a UV mini 1240 spectrometer from Shimadzu. With
this UV-VIS spectrophotometer, spectral data acquisition is possible over a wavelength range
between 190 nm and 1100 nm. Therefore, two different types of lamps are used: a 20 W halogen
lamp is used in the visible light spectral region, whereas a deuterium lamp is to collect absorption
data in the UV-region.
4.2.3 Optical emission spectrometer
The main oxidizing species emitted by the DBD plasma discharge were determined using an
OceanOptics® S1000 spectrometer. The spectrometer was able to acquire spectral data over the
range of 250 nm to 900 nm.
4.2.4 GC-MS
Gas chromatography-mass spectrometry (GC-MS) was used for both qualification and
quantification of micropollutants in water samples. All chromatographical analyses were
conducted using an Agilent 6890 GC Series gas chromatograph coupled to a Hewlett Packard 5973
mass selective detector. Chromatographical separations were performed on a capillary (5%-
phenyl)-methyl polysiloxane HP-5MS column (0.25 mm x 30 m x 0.25 µm). More information
about the method development, instrumental settings and separation conditions is provided in
chapter 5.
4.3 REACTOR DESIGN
In chapter three, different reactor designs have been theoretically evaluated with respect to their
energy efficiency. It was concluded that reactor systems based on dielectric barrier discharges
over a moving water film were both sustainable and energy efficient. Based on this conclusion,
the construction of a DBD reactor with moving water film has been accomplished at the RUPT in
the years 2013-2014. Subsequently, this innovative reactor design was tested for the removal of
atrazine. In this early research stage, micropollutant decomposition was solely studied in batch
reactor configuration, where the solution under treatment was continuously recirculated in the
reactor.
In contrast, most experiments performed in this work are performed in a continuous flow (single
pass) reactor configuration. This configuration is chosen, since it is more representative for real-
world application than batch treatment. In the operational parameter optimization experiments
only the plasma chamber was applied. This reactor configuration is referred to as the “only
plasma configuration” (1 P)
35
4.3.1 Only plasma (1 P) single pass reactor configuration
In the only-plasma-configuration, experiments were conducted in a coaxial DBD plasma reactor
with moving water film over Zorflex® active carbon textile. A complete overview of the
experimental setup is given in figure 4-1. Briefly, the plasma chamber consists of a quartz glass
vessel (thickness 1.55 mm) with cylindrical geometry. Around the quartz tube, an outer mesh grid
was wrapped, which served as the high voltage electrode. Inside, a grounded, stainless steel tube
with an outer diameter of 28 mm was placed.
Micropollutant solution was pumped upwards in the inside of the steel tube, using a peristaltic,
two channel Carl Roth® pump. Reaching the top of the tube, micropollutant solution flew through
a small hole into the overflow channel, and gradually moved down along the Zorflex® active
carbon cloth. During operation, working gas (dry air, argon or oxygen) was continuously pumped
from the gas reservoir to the reactor, using a PR 4000 mass flow controller (MKS instruments).
Plasma was generated between inner and outer electrode by a custom made AC high voltage
power supply, triggered by a pulse generator (model TGP110, Thurbly, Thandar instruments).
Figure 4-1: Schematics of DBD reactor, operating in single pass mode (1P)
The use of Zorflex® active carbon as a reactor membrane, covering the grounded electrode,
allowed an increase of energy efficiency, in comparison with other DBD reactors with moving
water film, described in literature. Zorflex® activated carbon cloth was supplied by Chemviron
Carbon (Chemviron Carbon, 2016). A microscopic and macroscopic image of this textile is given in
figure 4-2. Originally, Zorflex® textile was designed for military purposes, such as the production
of chemical warfare suits. Nowadays Zorflex® is applied in a variety of applications such as wound
36
dressing, air cleaning, and water treatment. In this research, a FM50 K type Zorflex® was used.
This model had a thickness of 0.5 mm, and was further characterized by a surface density of 130
g/m³, a carbon tetrachloride activity varying between 55 and 70 m/m %, and a total adsorption
surface area of more than 2000 m²/g. It is therefore considered as highly efficient for air and
water pollutant adsorption purposes (Chemviron Carbon, 2016).
Figure 4-2 Microscopic (left) and macroscopic (right) picture of Zorflex® active carbon cloth (Chemviron)
4.3.2 Alternative single pass reactor configurations
Plasma discharges are known to produce plasma gas. Hence, additional micropollutant removal
can be achieved by additionally plasma gas bubbling through the micropollutant solution. In
practice, plasma gas bubbling is performed in another reactor vessel which can be placed before,
or after the plasma reactor. Detailed schemes are provided in figures 4-3 – 4-4. In the 1P2O
reactor (figure 4-3), the micropollutant solution was first treated with plasma in the plasma
chamber, and subsequently transferred to the ozonation chamber. There, plasma gas produced in
the plasma chamber was bubbled through the micropollutant solution. In the 1O2P configuration
(figure 4-4), the micropollutant solution was first ozonated with plasma gas produced in the
plasma reactor and subsequently treated with plasma in the plasma chamber.
37
Figure 4-3: Schematics of 1P2O reactor configuration, operating in single pass mode
Figure 4-4: Schematics of 1O2P reactor configuration, operating in single pass mode
38
To compare micropollutant removal through plasma treatment with micropollutant removal by
ozonation with exhausted plasma gas, the 1 O reactor configuration is applied (figure 4-5). In the
1 O reactor configuration, micropollutant solution was introduced in the ozonation chamber.
Simultaneously, working gas (air or argon) was introduced in the plasma reactor, and plasma gas
(mainly containing ozone), produced through the plasma discharge was bubbled through the
micropollutant solution. Note that in the 1 O configuration deionized water was introduced into
the plasma reactor, instead of micropollutant solution. Furthermore, this reactor configuration
operated also in single pass configuration.
Figure 4-5: Schematics of only ozonation (1O) reactor configuration, operating in single pass mode
4.3.3 Batch reactor configuration
In a limited number of experiments of this work, batch processes were used. In the batch
configuration (figure 4-6), 500 ml of micropollutant solution was pumped into the ozonation
chamber. Next, the solution under treatment was transferred from the ozonation chamber to the
plasma chamber. As in the single pass process, water rose in the steel tube, and subsequently fell
down along the Zorflex® textile. After plasma treatment in the plasma chamber, the
micropollutant solution was transferred to ozonation chamber, and subsequently bubbled
through with plasma gas, produced in the plasma chamber during plasma discharge.
39
Figure 4-6: Schematics of batch reactor configuration for only plasma treatment
4.4 EXPERIMENTAL PROCEDURES
4.4.1 Optimization of micropollutant degradation in the DBD reactor
In a typical optimization experiment, 2.5 l micropollutant mixture (100 µg l-1) was prepared by
dilution from a 1 mg l-1 stock solution. Influent was continuously fed in the plasma chamber with a
2-channel Carl Roth® peristaltic pump, operating at a constant flow rate of 66.30 ml min-1.
Effluent was continuously withdrawn from the reactor. At different time intervals (0, 2.5, 5, 10,
15, 20, 25 and 30 min), approximately 60 ml reactor effluent samples were taken. Immediately
after sample collection, 19.00 ml sample was accurately weighed in 20 ml vials, using an analytical
balance. Micropollutant extractions were performed by means of an optimized liquid-liquid
extraction method (see chapter 5, section 5.3.2.2). After extraction, micropollutant
concentrations in the initial sample, the sample after adsorption (0 min) and the plasma treated
samples, were analyzed using an optimized GC-MS technique.
From the experimental results, the removal efficiency of each micropollutant was calculated as
(eq. 4-1):
100.C
C1%R
0
t
eq. 4-1
With R the removal efficiency (%), C0 the initial micropollutant concentration (µg l-1), and Ct the
final micropollutant concentration (µg l-1) after a certain treatment time t.
40
To optimize our reactor system, a classic one-parameter-at-a-time approach was adapted. The
operational parameters, investigated in this work, included: (i) gas type, (ii) gas flowrate, (iii)
water flowrate, (iv) duty cycle, (v) applied power, and (vi) initial micropollutant concentration.
Reactor efficiency towards the removal of each micropollutant was calculated by the EEO figure-
of-merit, which was already introduced in section 2.4.2 (eq. 2-11)
41
Chapter 5 Analytical procedures
This chapter deals with the analytical procedures applied in this work. The first section provides
more information about the electrical diagnostic methods. The electrical diagnostic methods
mainly include a monitoring of the current and voltage waveforms, together with the calculation
of the applied power to the DBD reactor. The second section gives a detailed description of the
chemical diagnostics. The last section provides more information about the used toxicological
tests.
5.1 ELECTRICAL DIAGNOSTICS
5.1.1 Power measurements
As previously described, voltage was measured with a Tektronix P601 5HV probe and current was
measured with a Pearson model 2877 current probe. Both voltage and current waveforms were
simultaneously monitored using a Tektronix S1200 oscilloscope. Figure 5-1 shows a typical voltage
and current waveform, as produced by the AC driven DBD plasma discharge. As could be seen of
this figure, both signals appear as sine waves. The distorted current sine with sparks on top of it
indicated the plasma discharge.
Figure 5-1: Typical voltage and current waveforms produced by pulsed DBD discharges (Davister, 2015)
The power of the plasma discharge was determined by multiplying the power per period (P0) by
the duty cycle (DC). The power per period was calculated by integrating the product of voltage
and current and dividing it by the total pulse duration (eq. 5-1).
2
1
T
T120 dt.V.I
TT
1P eq. 5-1
42
With P0 the power dissipated in one period (W), I is the momentary current (A), V is the
momentary applied voltage (V) during plasma generation, and T2 – T1 is the length of time interval
over which is integrated (s).
It is noteworthy to mention that the voltage applied to the reactor was periodically interrupted to
avoid excessive gas and liquid temperatures, since this would induce heat losses in the reactor,
combined with ozone decomposition in the liquid and gas phase. Interruption of pulsed voltage
was realized by the application of a duty cycle, which expresses the percentage of time that
voltage was turned on (Ton) or off (Toff) (eq. 5-2):
offon
on
TT
TDC
eq. 5-2
With Ton the time that voltage was applied to the reactor (ms), and Toff the interruption time
between two voltage pulses (ms). In all experiments, Ton + Toff was 30 ms. Hence, the total power
delivered by the plasma discharge was obtained by multiplying the power per period P0 with the
applied duty cycle (eq. 5-3):
DC.PP 0 eq. 5-3
Where P represents the total power (in Watt), P0 the power per period (in Watt) and DC the
applied duty cycle.
5.2 EMISSION SPECTROMETRY
In last decades, lot of research efforts have been made towards the detection of both long and
short living species, produced by plasma discharges. In this work, optical emission spectrometry
(OED) has been employed as a diagnostical method to derive more information about the
presence of active species in air, oxygen and argon plasmas.
In DBD plasmas, ions, radicals and diatomic molecules are excited through the addition of
electrical energy. This excitation process is characterized by the promotion of electrons to higher
energy levels. Since the excited state is a highly unstable condition, excited species will rapidly
return to the ground state. This phenomenon is associated with the emission of light with a
wavelength specific to the target species. The obtained spectrum is characterized by the presence
of fine spectral lines, attributed to the presence of atomic radicals and ions, and spectral bands
indicating the presence of diatomic molecules (N2, O2) and radicals (OH•).
In the OES experiments, plasma light emitted by air, argon and oxygen DBD plasmas was collected
for different power settings by a quartz optic fibre placed at a 90 degree angle relative to the
plasma discharge. Plasma radiation was introduced and dispersed in a monochromator. All
emission spectra were acquired over the spectral range 200-900 nm with SpectraSuite® software
43
from Ocean Optics. Analyses focused upon the identification of plasma species by their
wavelength in the emission spectrum.
5.3 CHEMICAL DIAGNOSTICS
5.3.1 Determination of H2O2
Hydrogen peroxide (H2O2) concentration in liquid samples was determined by using the UV-VIS
spectrophotometrical method proposed by Nogueira et al. (2005). The spectrophotometric
method was based on the specific reaction between ammoniummetavanadate (NH4VO3) and H2O2
in acidic media (r. 5-1):
OH3VOOHH4VO 23222
3 r. 5-1
In the presence of H2O2, a red-orange coloured peroxovanadium complex was formed. The
peroxovanadium complex was characterized by a broad absorption band in the range 300-630 nm
with an absorption maximum of 450 nm.
To correlate the absorbance value of the peroxovanadium complex at 450 nm with a H2O2
concentration in the liquid sample, a calibration curve was needed. Six calibration standards were
made by dilution of a hydrogen peroxide standard solution with known concentration (30 m/m%).
The absorbance of each calibration standard was measured, and plotted against known
concentrations for diluted solutions (figure 5-2).
Figure 5-2: Calibration curve used for determination of hydrogen peroxide in liquid samples
To measure the concentration of H2O2 in liquid samples, 0.8 ml of vanadate solution was added to
2.0 ml sample. The absorption of the sample was measured over the range 350 nm - 550 nm with
the UV-VIS spectrophotometer. Concentration of H2O2 in the liquid samples was calculated by
applying Lambert’s-Beer law (eq. 5-4):
y = 0,0002x - 2E-05R² = 0,9986
0
0,02
0,04
0,06
0,08
0,1
0,12
0,14
0 100 200 300 400 500
Ab
sorb
ance
Concentration (µmol/l)
44
l.
Ac
22OH
eq. 5-4
With cH2O2 the experimentally determined H2O2 concentration in the calibration standard (µg l-1), A
the experimentally determined absorbance of the peroxovanadium complex at 450 nm, ε the
molar extinction coefficient of H2O2 (0,000229 l mol-1 cm-1) and l the optical path length of the
cuvet (1 cm).
5.3.2 Gas chromatography – mass spectrometry
Chromatographical techniques combined with mass spectrometry, such as gas chromatography-
mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are most
commonly employed methods for both qualitative and quantitative determinations of
micropollutants in water.
Gas chromatography allows the separation of chemical substances due to their different
physicochemical interactions with a stationary phase on the inner wall of a GC column.
Consequently, different molecules elute from the GC column on different times (also referred to
as the retention time). After separation in the GC column, the chemical substances are
transferred into the mass spectrometer (MS) by means of a GC-MS interface. In a typical MS
separation procedure, the entering molecules undergo an ionization and fragmentation process.
Subsequently, these charged fragmentation products are separated according to their mass-to-
charge ratio and eventually detected by a mass selective detector.
It should be noted that mass spectrometers can operate in two different modes: SIM and SCAN
mode. In SCAN mode a full mass scan is acquired by continuously scanning a wide mass-to-charge
(m/z) ranges. On the other hand, SIM mode only scans for a few selected ions associated with the
analyte.
Most preferable mode depends on the aim of the analysis. Performing a GC-MS analysis in full
SCAN mode is particularly useful for determination of unknown substances in a sample. It
provides more information than SIM mode since a full mass spectrum is monitored. However,
SCAN mode is associated with a significant lower instrumental sensitivity, up to two orders of
magnitude. On the other hand, selected ion monitoring (SIM) maximizes the instrumental
detection limit of trace contaminants, since only a few mass fragments of interest are monitored.
5.3.2.1 Method optimization
First, a preliminary test run was carried out in SCAN mode to determine all micropollutants, by
using a 100 mg l-1 standard solution in dichloromethane. In accordance with procedures described
in literature, a wide temperature range was combined with a slow heating rate.
45
Briefly, our initial column temperature was set on 100 °C. This temperature was hold for 1 minute,
and then raised to 270 °C with a temperature rate of 10 °C/min. The injection temperature was
set on 250 °C. All other constant GC-MS parameters are presented in table 5-1.
Table 5-1: Overview of constant GC-MS parameters
Column MS parameters
Model HP-5MS Solvent delay 2.00
Model number Agilent 19091s-433 MS quad (°C) 150 (max 200)
Nominal length (m) 30 MS source (°C) 230 (max 250)
Nom. diameter (µm) 250
Nom. film thickness
(µm)
0.25 Back inlet
Injection parameters Pressure (kPa) 73.2
Injection Splitless Purge flow
(ml/min)
2.00
Injection volume (µl) 1.0 Total flow
(ml/min)
13.9
Syringe size (µl) 10.0 Gas type Helium
Identification of target compounds is performed by comparison of the obtained mass spectra
from the built-in software, with the mass spectra found in the NIST database. Figure 5-3 provides
an overview of the mass spectra of all target compounds.
46
Figure 5-3: Mass spectra of target compounds measureable with GC-MS (NIST, 2016)
To shorten the GC-MS procedure, an optimization of the chromatographical conditions was
carried out, by adapting a classic one-parameter-at-a-time approach. First, an optimization of the
injection temperature was carried out, then the effect of the applied oven program on the
47
separation process was evaluated. Characteristics of the optimized GC-MS method are highlighted
in table 5-2. A corresponding chromatogram is given in figure 5-4.
Table 5-2: optimization of GC-MS procedure
Oven program Optimized method
Injection temperature (°C) 270
Initial column temperature (°C) 110
Hold (min) 1
Rate (°C/min) 25
Maximum 190
Hold 0
Rate (°C/min) 25
Maximum (°C) 300
Hold 0
Rate (°C/min) 30
Maximum (°C) 310
Hold 2
Total time (min) 10.39
Figure 5-4: example of a GC-MS chromatogram obtained after method optimization
In addition to the optimized GC-MS method in SCAN mode, a GC-MS method operating in single
ion monitoring (SIM) mode was developed, in order to reach lower detection limits. For each
component, 1 target ion and 2 qualifiers were selected (see table 5-3). The selection of a target
ion was necessary since it is the major parameter for identification and quantification of the
48
target compound. The target ion is represented by the most intense mass-to-charge (m/z) ratio
found in the mass spectrum. Additionally, two qualifying ions were selected to confirm a correct
peak identification. These qualifying ions are respectively represented by the ions with the second
and third largest intensity in the mass spectrum.
Table 5-3: Developed GC-MS method in SIM mode (NIST,2016)
Target compound Retention time (tR) Target ion
(m/z)
Quantification
ions (m/z)
DVOS 3.71 109 185, 79
DIU 4.10 72 232, 234
ATR 6.31 200 215, 173
PCF 6.44 266 165, 202
ALA 7.03 45 160, 188
BISA 8.05 213 228, 119
CARB 8.79 193 165, 95
ETH 10.09 213 296, 160
5.3.2.2 Extraction procedure
Determination of micropollutants in wastewater and other environmental samples is often a
challenging task, mainly due to their low concentrations (ng l-1 – µg l-1 range) in these samples,
and the complexity of the water matrix. Solid-phase extraction (SPE) is generally recognized as an
optimal extraction procedure for micropollutants from environmental samples. Nevertheless,
other extraction techniques, for example based on liquid-liquid extraction, are also frequently
carried out for the extraction of micropollutants from environmental samples. In the specific case
of pesticides, liquid-liquid extraction with dichloromethane (CH2Cl2) is often recommended
(Kotowska et al., 2014).
Based on the recommendations of the EPA and the current knowledge at the RUPT about liquid-
liquid extractions for micropollutants, an extraction method with dichloromethane (CH2Cl2) was
tested and further optimized for the isolation of micropollutants from water. The developed
extraction procedure is illustrated in figure 5-5. In this method, 1 ml extraction solvent
(dichloromethane) was added to 19.00 ml water sample, and adequately shaken by hand. After
extraction, the organic phase was removed and transferred into a GC-MS vial. GC-MS analysis was
then performed with the optimized GC-MS method, described in section 5.3.2.1.
49
Figure 5-5: Schematic illustration of the liquid-liquid extraction procedure
To gain more insight about the required extraction time, an experiment was designed to
determine the influence of different extraction times (0, 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10 min) on the
extraction efficiency. All extractions were carried out with a mixture containing 100 µg l-1 of each
micropollutant.
The results are shown in figure 5-6. DVOS, DIU, ATR, PCF and ALA are rapidly extracted within
5 minutes of operation, whereas BISA, CARB, and ETH needed an extraction time of at least
8 minutes. Nevertheless, all micropollutants were fully extracted after an extraction time of
10 minutes. Consequently, a 10 minutes extraction time was chosen as the optimal extraction
time in further experimentations.
50
Figure 5-6: Influence of the extraction time on the extraction efficiency
5.3.2.3 Method validation
Prior to the application of an analytical method for the analysis of water samples, the analytical
procedure should be investigated, in order to confirm its suitability. In this work, the validation of
the GC-MS procedure was restricted to an investigation of the parameters: (i) linearity, (ii)
0
500000
1000000
1500000
2000000
2500000
0 5 10
Re
spo
ns
Extraction time dichlorvos (min)
0
100000
200000
300000
400000
500000
0 5 10
Re
spo
ns
Extraction time diuron (min)
0
200000
400000
600000
800000
1000000
1200000
0 5 10
Re
spo
ns
Extraction time atrazine (min)
0
50000
100000
150000
200000
0 5 10
Re
spo
ns
Extraction time pentachlorophenol (min)
0
100000
200000
300000
400000
0 5 10
Re
spo
ns
Extraction time alachlor (min)
0
500000
1000000
1500000
2000000
2500000
3000000
0 2 4 6 8 10
Re
spo
ns
Extraction time bisphenol A (min)
0
200000
400000
600000
800000
1000000
0 2 4 6 8 10
Re
spo
ns
Extraction time carbamazepine (min)
0
10000
20000
30000
40000
50000
60000
70000
80000
0 5 10
Re
spo
ns
Extraction time ethnylestradiol (min)
51
accuracy, (iii) precision (iv) limit of detection (LOD), and (v) limit of quantification (LOQ). A
summary of the validation results can be found in table 5-4.
Linearity – range
External calibration curves were prepared in water by extracting 8 calibration standards with the
optimized extraction method. All calibration curves were found to be linear in the whole
concentration range (0 - 1000 µg l-1), with correlation coefficients (r²) ranging from 0.9934 to
0.9999.
Accuracy – extraction efficiency
In order to test the accuracy of the analytical method, 1 ml of a test solution, containing a
micropollutant mixture in dichloromethane (2 mg l-1) was added to 19 ml deionized water. Next,
liquid-liquid extraction was performed with the optimized extraction procedure, described in
section 5.3.2.2. After GC-MS analysis, the accuracy of the optimized chromatographical procedure
was determined by calculating the recovery percentage as the ratio between the individual
micropollutant concentrations in dichloromethane before and after extraction (eq. 5-5):
100.c
c(%)erycovRe
B
A eq. 5-5
With cA, the micropollutant concentration in dichloromethane before extraction (µg l-1), and cA
the micropollutant concentration after extraction (µg l-1), as measured with optimized GC-MS
procedure.
Precision
Precision measured as intraday precision, was evaluated by assessing three replicate water
samples at three different concentration levels (100, 50 and 5 µg l-1). Next, precision was reported
as relative standard deviation (RSD), eq. 5-6:
100.x
s%RSD eq. 5-6
Where s is the standard deviation, and x the average concentration. Both statistic parameters
were calculated for each concentration level. The reported RSD value (see table 5-4) was
determined as the average RSD value calculated from three concentration levels.
Detection limit
A popular approach for determining the LOD and LOQ for water samples was proposed by the US
EPA (Corley, 2002). Following their recommendations, a 5 point calibration curve was constructed
within the concentration range (0 – 20 µg l-1). According to the proposed root mean square error
52
(RMSE) method, the limit of detection (LOD) and limit of quantification (LOQ) were estimated as
(eq. 5-7 – eq. 5-8):
a
RMSE3LOD eq. 5-7
a
RMSE10LOD eq. 5-8
With a the slope of the calibration curve, and RMSE the root mean square standard error on the
calibration curve. The RMSE was calculated according to eq. 5-9:
2n
cc
RMSE
2^
i
eq. 5-9
Where RMSE represents the root mean square standard error on the calibration curve, ci the
concentration of the calibration standards (µg l-1), ĉ the concentration predicted from the
calibration curve, and n the number of calibration standards (5).
Table 5-4: Method validation of the optimized GC-MS method
Component Recovery
(%)
aPrecision
(% RSD) bLOD (µg l-1) cLOQ (µg l-1)
Linearity
Range
(µg l-1)
r²
DVOS 92,9 1,70 0,356 1,19 0 – 1000 0,9979
DIU 96,4 1,45 0,499 1,66 0 – 1000 0,9982
ATR 92,0 1,21 0,595 1,98 0 – 1000 0,9987
PCF 98,2 1,94 4,50 15,0 5 – 1000 0,9995
ALA 95,0 1,11 0,287 0,955 0 – 1000 0,9994
BISA 90,8 1,22 0,527 1,76 5 – 1000 0,9999
CARB 90,6 3,11 3,45 11,5 0 – 1000 0,9994
ETH 94,2 2,52 0,58 1,94 0 - 1000 0,9934 a Relative standard deviation b Limit of detection c Limit of quantification
5.4 TOXICITY ANALYSIS
The future implementation of advanced oxidation processes is partly based on the potential
toxicity of the effluent into the receiving environment. Hence, toxicity testing is interesting to gain
more insight about the effluent toxicity after plasma treatment. These toxicity tests were
conducted in close collaboration with the Environmental Toxicology Unit (GhenToxLab) of Ghent
University.
53
5.4.1 Preparation of test samples
An initial toxicity experiment was set up with a micropollutant mixture containing 100 µg l-1 of the
micropollutants DIU, ATR, ALA, BISA, CARB and ETH. DVOS and PCF were excluded from the
toxicity test since these compounds were respectively too unstable in water, and too volatile. The
experiment was performed in the DBD plasma reactor operating in single pass configuration for
three different working gases (air, argon and oxygen). Other operational settings (table 5.5) were
hold constant for all toxicity experiments. When steady state conditions were reached, 1 l effluent
was continuously withdrawn from the plasma reactor. Part of the sample (500 ml) was heated for
1 hour on a heating device at 80 degrees, in order to remove long living oxidants such as ozone
(O3) and hydrogen peroxide (H2O2). The other 500 ml did not undergo an additional heating step.
Similarly, 1 l of the influent solution and 1 l of effluent without plasma operation were analysed
for comparison.
Table 5-5: Operational settings for the toxicity test
Operational parameters Settings
Gas flow 1 SLM
Water flow 66.30 ml/min
Concentration 100 µg l-1
Duty cycle 0.15
Power 40 W
* 1 SLM = 1 standard liter per minute. This unit is defined as the gas flow rate in liter per minute, under standard
conditions of temperature (273,15 K) and pressure (101325 Pa)
5.4.2 Toxicity testing
A chronic toxicity test, based on an algae test with Pseudokirchneriella subcapitata (Selenastrum
Capricornutum) was initiated immediately after finalizing the plasma experiment. The toxicity of
the samples was evaluated for the dilutions 0x, 10x and 100x. As the samples were already
prepared in deionized water, dilutions were made in prepared medium. In order to create optimal
growth conditions for the algae, nutrients were added to the undiluted samples. Further, the pH
of all undiluted samples was adapted to 8.0 ± 0.2 Therefore, these parameters were also
measured at the start and at the end of the test.
Algae tests were performed in erlenmeyer flasks containing 50 ml of test medium. Each test
consisted of a control and three dilutions of each sample, and for each three replicates. Each
replicate was inoculated with 1 X 104 cells/ml (= cell density N0 at the start (t0) of testing).
Afterwards, all erlenmeyer flasks were incubated at 24 °C on a light table (24 h light, 120 µmol
photons/m2/s) and were manually shaken two times per day. Cell densities (N1, N2 and N3) were
measured using a particle counter (Coulter Counter Z1, Beckman) after 24 (t1), 48 (t2) and 72 (t3)
hours. The pH of the test medium was measured again at the end of the test.
54
Chapter 6 Characterization of a DBD plasma
reactor
Before the DBD plasma discharge reactor was tested for its performance towards the degradation
of organic compounds, a preceding characterization of the DBD plasma was performed. Plasma
characterization is important, as it provides information about the quantitative and qualitative
production of active species in the plasma zone and the liquid. This information can be used for
reactor comparison and to gain deeper insight in micropollutant decomposition processes. In the
first section, spectral analysis by means of optical emission spectroscopy (OES) is discussed to
unravel the gas phase reactions, occurring during plasma discharges in air, argon and oxygen. The
three following sections provide more information about the chemical reactions occurring in the
liquid phase.
6.1 SPECTRAL ANALYSIS
6.1.1 Air plasma
Knowledge about the production of active species produced by plasma is essential to understand
the formation of oxidative species in the plasma. In an air plasma, accelerated electrons are
continuously colliding with molecular nitrogen (N2) and oxygen (O2) molecules. As a result,
nitrogen and oxygen molecules are ionized (r. 6-1 – r. 6-2) (Eliasson et al., 1987 ; Ahn et al., 2003).
e2NeN 22 r. 6-1
e2OeO 22 r. 6-2
In this work, optical emission spectroscopy (OES) was employed to get more insight into the
production of active species in the plasma zone, under different working gas conditions. Figure 6-
1 represents a normalized and time averaged emission spectrum of an air plasma.
55
Figure 6-1: Normalized emission spectra of a DBD plasma discharge in air
As seen in figure 6-1, the emission spectrum of a plasma discharge in air shows only bands.
Spectral lines were not observed in the spectrum. Further, the optical emission spectrum is
dominated by molecular nitrogen (N2) bands. Specifically, different vibrational belonging to the N2
second positive system C³∑u(v’) -> B³∏g(v’’) were successfully identified by comparing the plasma
spectrum with spectral data given in literature (Sharma & Saikia, 2008). ∆v represents the change
in quantum number.
Although the emission spectrum of plasma discharges in air is dominated by different vibrational
excitation transitions of molecular nitrogen, also other important nitrogen containing species,
such as nitric oxide (NO) can be formed. The formation of NO is described by the Zeldovich
mechanism (r. 6-3 and r. 6-4) (Anetor et al., 2014):
NNOON2 r. 6-3
ONOON 2 r. 6-4
According to literature, the presence of NO molecules in the plasma is indicated by the
appearance of several NO specific bands in the UV region (280-300 nm) of the emission spectrum
(Drakes et al., 1997). Together, these spectral bands are referred to as the NO γ-system. NO bands
were not visible in our air spectrum. The absence of NO bands could be explained by the very low
NO concentrations which are usually detected in DBD plasma reactor systems with falling water
film. In such reactors, low NO concentrations are attributed to the rapid oxidation of NO by
atomic oxygen and OH radicals, according to the following gas phase reactions (Orlandini &
Riedel, 2000):
56
MNOMONO 2 r. 6-5
MHNOMOHNO 2 r. 6-6
MHNOMOHNO 32 r. 6-7
Although, the emission spectrum of plasma discharges in air is dominated by the presence of
nitrogen containing species, plasma discharges in air also involve the formation of oxygen
containing species such as hydroxyl radicals (OH•), ozone (O3) and atomic oxygen (O). The
presence of excited OH radicals has been confirmed by the presence of the OH (A-X) spectral band
around 308 nm. Due to spectral overlapping with the N2 vibrational band, the OH (A-X) spectral
band was hardly visible. Nevertheless, the presence of OH radical species in air plasmas has been
confirmed by other researchers with alternative spectroscopic techniques such as laser induced
fluorescence spectroscopy (LIF) (Kanazawa et al., 2011; Ono et al., 2011).
According to a review of Bruggeman & Schram (2010) many different plasma-chemical reactions
are responsible for the production of OH• radicals. Most of these reactions require the presence
of H2O molecules. The dissociation of water by electron impact is considered to be the most
important source of hydroxyl radicals (r. 6-8):
eHOHeOH2 r. 6-8
Next to the electron impact dissociation, vibrational and rotational excitation of water molecules
also contribute to the production of OH• radicals (Joshi et al., 1995; Vanraes et al., 2015a)
eOHeOH *
22 r. 6-9
OHHOHOHOH 22*
2 r. 6-10
OHHHOH 2**
2 r. 6-11
Another important molecule formed in the gas phase is ozone (O3). By gas discharges in oxygen or
air, ozone formation is mainly initiated by the three body reaction (r.6-12) (Kogelschatz, 2003 ;
Brisset et al., 2011):
MOMOO 32 r.6-12
M represents a third collision partner, which can be O2 or N2. Ozone cannot be determined by OES
since it is a molecule which occurs in the ground state. Therefore, it will not emit light and is
consequently not detectable in the OES spectrum. Contrarily, ozone typically shows absorption of
UV light. Therefore, measuring the very characteristic absorption band of ozone, appearing in the
UV region near 253 nm is a possible way for studying ozone production in the reactor.
Alternatively, absorption lines in the IR region can also be measured. Due to instrumental
limitations, ozone production in the gas phase was not measured in this work, but the reader is
referred to Davister (2015) for ozone measurements in the same reactor under similar conditions.
57
6.1.2 Oxygen plasma
A typical normalized emission spectrum of a DBD plasma discharge in oxygen is presented in
figure 6-2. Although pure oxygen was used as working gas, the optical emission spectrum of a
plasma discharge in oxygen plasma is dominated by the presence of the N2 second positive
system, originating from air impurities in the plasma chamber. In contrast to the air plasma,
excited atomic oxygen species, also noted as O I, are visible in the emission spectrum at a
wavelength of 777,5 nm (Milosavljevic et al., 2014). It corresponds to the atomic oxygen
transition O (3p5P → 3s5S) (Krstulović et al., 2006).
Note that the atomic oxygen bands are less intense than the N2 spectral bands, belonging to the
N2 second positive system. The excitation of N2 molecules to N2* (r.6-13) requires relative low
energetic electrons. In contrast, the excitation of atomic oxygen proceeds in two different steps.
First, oxygen molecules are dissociated through an electron impact reaction (r.6-14). Next, atomic
electrons are excited (r.6-15) ( Eliasson et al., 1987; Ahn et al., 2003; Kogelschatz, 2003).
*22 NeN r. 6-13
OOeO2 r. 6-14
*OeO r. 6-15
Figure 6-2: Normalized emission spectra of a DBD plasma discharge in oxygen
6.1.3 Argon plasma
Figure 6-3 shows a normalized emission spectrum of a DBD plasma discharge in argon. As
mentioned in literature, the plasma gas contains a large number of argon emission lines in the
infrared region of the emission spectrum (700 – 850 nm). These bands constitute neutral argon
(Ar I) transitions (Wagatsuma & Hirokawa, 1995) Single ionized argon bands (Ar II) were not
58
observed in the OES spectrum. Together with the argon specific bands, the N2 second positive
system, discussed in section 6.1.1, is visible. The presence of nitrogen specific bands in the argon
plasma shows that the argon gas in the plasma chamber was contaminated with a small amount
of air, and some dissolved N2, present in the liquid phase.
Figure 6-3: Normalized emission spectra of a DBD plasma discharge in argon
6.2 ACTIVE SPECIES PRODUCTION IN THE WATER PHASE
In the previous section, the formation of different reactive species in the gas phase was discussed,
based on the optical emission spectra of plasma discharges in air, argon and oxygen. However,
micropollutant decomposition is expected to occur mostly at the plasma-liquid interface, and in
the bulk liquid. In the bulk liquid, micropollutant decomposition in the result of the production of
a wide variety of aqueous radicals and ions. Formation of these oxidative species is caused by a
series of complex interactions. A complete discussion of all interaction mechanisms between all
chemical species with micropollutants is beyond the scope of this work. However, the reactor is
characterized for the production efficiency of hydrogen peroxide in the plasma chamber.
Moreover an investigation of the solution parameters conductivity and pH is performed, since
they are a good representative to give a general view about the chemical processes taking place in
the plasma chamber and ozonation chamber.
6.2.1 pH
Figure 6-4 shows the variations in pH for the plasma and ozonation chamber, during the
treatment of deionized water. Experiments were performed in single pass reactor configuration,
where the treated solution was respectively exposed to only plasma treatment in the plasma
chamber (1P process) and only ozonation (1O process).
59
The experimental results presented in figure 6-4 show that DBD plasma treatment of deionized
water in the plasma chamber leads to a strong increase in acidity. Initial pH value of deionized
water was measured as 7.78. The initial pH remained unchanged after adsorption on Zorflex®.
Then, the initial pH rapidly decreased to a final pH value within 5 minutes of plasma treatment.
The strongest decrease in pH was observed for plasma discharges in air (final pH = 3.17). In the
case of oxygen, the final pH was more than 1 unit higher (4.48). The lowest pH drop was observed
for plasma discharges in argon (final pH = 5.78).
Although a rapid decrease of the initial pH (7.83) was also observed in the ozonation chamber, the
pH drop was, both for air and oxygen (final pH 4.41 and 4.69 respectively,) lower than in the
plasma chamber. The pH value did not decreased significantly for ozonation with argon (final pH
6.54).
Figure 6-4: pH variation in the plasma chamber (left) and ozone chamber (right)
The acidification of solutions treated with plasma has been extensively studied in literature.
Brisset et al. reported as one of the first authors about the acidic effects, induced by air plasmas
(Brisset et al., 1990a ; Brisset et al., 1990b). Additionally, strong pH drops in micropollutant
solutions and deionized water have been confirmed by many other researchers. For example,
Shainsky et al. (2012) reported about a fast pH drop to a final pH of 2.01 in deionized water after
DBD plasma treatment in an air environment. Similar results were obtained through
experimentations in other reactor types (Brisset et al., 2008; Ikawa et al., 2010; Oehmigen et al.,
2010). In literature, it was suggested that acidification of plasma treated water by air plasmas is
the result of nitrous acid (HNO2) and nitric acid (HNO3) formation in the bulk liquid. Formation of
these species can be understood by the diffusion of nitrogen containing gases produced during
the plasma discharge, into the liquid. Especially the production of gaseous nitrogen oxides (NO
and NO2) correlates very well with the appearance nitrite and nitrate in the liquid. In section 6.1,
it was shown that the formation of nitrogen oxide (NO) in the gas phase is described by the
Zeldovich mechanism (r.6-3 and r.6-4). NO formed in an air plasma is rapidly converted to
nitrogen dioxide (NO2) by reactions r. 6-16 and r.6-17 (Kogelschatz, 2003):
0,00
2,00
4,00
6,00
8,00
10,00
0 10 20 30
pH
time (min)
Plasma chamber
Air Oxygen Argon
0,00
2,00
4,00
6,00
8,00
10,00
0 10 20 30
pH
time (min)
Ozonation chamber
Air Oxygen Argon
60
22 NO2ONO2 r. 6-16
223 ONOONO r. 6-17
In a next step, nitrogen oxides dissolve into the liquid, leading to the formation of nitrite and
nitrate in the plasma chamber (r.6-18 - r.6-19). According to these reactions the formation of
nitrite and nitrate goes hand in hand with the formation of hydrogen cations (H+) in the water
phase, which describes the experimental observed pH drop by plasma discharges in air (Brisset et
al., 2008 ; Lukes et al., 2014).
H2NONOOHNO2 3222 r. 6-18
H2NO2OHNONO 222 r. 6-19
To ensure that nitrite (NO2-) and nitrate (NO3
-) were produced in the bulk liquid, concentrations of
both species were measured in the liquid samples at the start, and at the end of each experiment.
The results are presented in table 6-1.
Table 6-1: Nitrite (NO2-) and nitrate (NO3
-) production in the water phase
Sample NO2- (mg l-1) NO3
- (mg l-1)
Initial < 0.07 < 2.2
0 min (adsorption) 0.09 < 2.2
Plasma air 2.0 33
Plasma argon 0.51 12
Plasma oxygen < 0.07 3.8
The results given in table 6-1 indicate a significant higher NO2- and NO3
- production, when air was
used as a working gas. Therefore, the results are in good agreement with the observed pH drop in
the plasma chamber. Highest amounts of nitrite and nitrate were produced in the air plasma.
Contrarily, significant lower amounts of nitrite and nitrate were detected when plasma discharge
occurs in an argon and oxygen environment. Very interesting is the observation that the
determined nitrite concentration in air plasmas (2.0 mg l-1) was much lower than the nitrate
concentration (33 mg l-1). One of the reasons of a significant lower nitrite concentration, is the
disproportionation of nitrite into nitrate and nitric oxide (r.6-20). This reaction is only feasible in
strong acidic medium (pH < 3.5 ) (Lukes et al., 2014):
OHNONO2H3NO3 332 r. 6-20
Next to the conversion of nitrite in acidic media, nitrite can also be oxidized through other
reactions involving reactive oxygen species (ROS). Some of these reactions will be explained later
in this work. Surprisingly, significant pH drops have also been reported in solutions treated in
nitrogen-free plasmas. For instance, Shainsky et al. (2012) found a final pH value of 2.07 after
oxygen DBD plasma treatment of deionized water. This suggests that other reactive species,
61
formed in the liquid phase, contribute as well to the acidification of deionized water in the plasma
chamber. Probably, the self-decomposition of ozone, produced in the plasma discharge,
contributes to significant lower pH values. Although ozone decomposition in water is mainly
initiated under alkaline conditions, it has also been observed, at a slower rate, under acidic
conditions (Ershov & Morozov, 2009). Possibly, the formation of charged species in the gas phase,
and the subsequent dissolution in the liquid also contributes to the observed pH drops. Further
research is definitely needed to validate this claim.
In contrast to plasma discharges in air and oxygen, usually no acidification of plasma treated
water is reported by DBD discharges in argon (Shainsky et al., 2012). However, a decrease in pH
was observed when argon was used as discharge medium (pH = 5.78). Moreover, relative high
amounts of nitrite and nitrate were measured in the liquid phase (see table 6-1). It is therefore an
indicator of working gas contamination with nitrogen molecules. Hence, the contribution of nitrite
and nitrate formation by plasma discharges in argon and oxygen should not be underestimated in
the interpretation of pH data.
When deionized water is subjected to only ozonation by plasma gas bubbling, but without direct
contact with plasma, a pH drop to a final pH value of 4.41 and 4.69 is observed for plasma
bubbling with air and oxygen, respectively. These pH values are in the same order of magnitude of
the final pH value obtained after plasma contact in oxygen (4.48). An explanation of these
phenomena is difficult, but just as in the plasma chamber, ozone decomposition and dissolution
of species produced in the gas phase can attribute acidification of ozonated water.
6.2.2 Conductivity
In addition to the pH measurements, also changes in solution conductivity were measured. Figure
6-5 shows the temporal evolution of conductivity in the plasma chamber and ozonation chamber.
Because deionized water was used, the initial conductivity was very low (0.24 µS/cm). The initial
conductivity remained unchanged after adsorption on Zorflex®. Then, initial conductivity rapidly
increased to a final, constant value within 5 minutes of plasma treatment. Highest increase in
conductivity was observed for air (final conductivity 420 µS/cm). For oxygen, the final conductivity
was 99.9 µS/cm. Conductivity only slightly raised for plasma discharges in argon (final conductivity
16.9 µS/cm). In the ozonation chamber, measured conductivity was about a factor ten lower than
in the plasma chamber. Highest conductivity was measured for oxygen (24.9 µS/cm). The average
conductivity for air was 16.9 µS/cm. In case of argon, the increase in conductivity was negligible
(1.15 µS/cm). It suggests that only argon gas, without ozone, was introduced into the ozonation
chamber.
62
Figure 6-5: Conductivity in the plasma chamber (left) and the ozone chamber (right)
Together with the pH drop explained in the pH measurements (section 6.2), the increase in
conductivity delivers additional experimental evidence about the production of charged species in
the treated solution. For discharges in air, the main contribution of the solution conductivity is
delivered by the decrease in pH (Magureanu et al., 2013). Moreover, conductivity and pH are
closely correlated with each other, as predicted by (Burlica & Locke, 2008):
OH6.198H82.349.10000 eq. 6-1
With H+ an OH- respectively the concentration of hydrogen cations and hydroxyl anions,
expressed in mol l-1. Λ0 is the initial solution conductivity (µS/cm), and Λ the steady state
conductivity, also in µS/cm.
By using this model, a steady state conductivity of µS/cm² is predicted. The model can be
extended by adding an additional term for the contribution of nitrate to the steady state
conductivity (Burlica & Locke, 2008). Note that nitrite was only produced to a minor extend (see
table 6.1), so the contribution of nitrite to overall conductivity was not included in the model (eq.
6-2):
30 NO4,71OH6.198H82.349.1000 eq. 6-2
According to eq. 6-2, a steady state conductivity of 274.8 µS/cm is predicted. The concentration
of H+ and OH- was calculated from the pH measurements discussed in previous section, whereas
the NO3- concentration was calculated from the data presented in table 6-1. This conductivity is
significantly lower than the experimentally measured steady state conductivity of deionized water
treated with air plasma (420 µS/cm). Hence, it confirms that the measured steady state
conductivity cannot be fully explained by the presence of only H+, OH- and NO3- ions. Several other
ions clearly contribute to the solution conductivity. Probably, these ions originate from the self-
decomposition of ozone, or other ions, present in the liquid sample. Small, but significant
increases in conductivity where also measured during the ozonation process with oxygen or air, in
0
100
200
300
400
500
0 10 20 30
Co
nd
uct
ivit
y (µ
S/cm
)
time (min)
Plasma chamber
Air Oxygen Argon
0
5
10
15
20
25
30
0 10 20 30
Co
nd
uct
ivit
y (µ
S/cm
)
time (min)
Ozonation chamber
Air Oxygen Argon
63
the ozonation chamber. For ozonation with argon, the almost negligible steady state conductivity
confirmed that only a very few ozone was produced in ozonation processes with argon.
6.2.3 H2O2 production
The production of H2O2 in the liquid phase is illustrated in figure 6-6. Since the initial solution and
the sample taken after adsorption were not treated with plasma, no H2O2 was detected in these
samples. A similar H2O2 production was observed for discharges in oxygen and argon (147.0 µmol
l-1 and 126.7 µmol l-1 respectively). The usage of air resulted in a lower H2O2 production (56.5 µmol
l-1). A small amount of hydrogen peroxide production was also observed in experiments by which
only the ozonation chamber was used. Again, the amount of detected hydrogen peroxide was the
highest for ozonation in oxygen (24.9 µmol l-1). Ozonation in air produced 18.3 µmol l-1 H2O2. No
H2O2 production was measured for argon.
Figure 6-6: H2O2 variations in the plasma chamber (left) and the ozone chamber (right)
It is very interesting to note that plasma discharges in argon and oxygen yielded a similar H2O2
production. Similar results are also reported by Porter et al. (2007), who have investigated the
H2O2 production after plasma discharges in argon, oxygen, air, nitrogen carbon dioxide and
helium. The authors reported a similar H2O2 production for plasma discharges in argon, oxygen
and carbon dioxide. No H2O2 production in air and nitrogen plasmas was found.
In contrast to the results presented by Porter et al. (2007), a low H2O2 production (56.5 µg l-1) for
plasma discharges in air is measured in this research. Nevertheless, this H2O2 production is
approximately three times lower than the H2O2 production in oxygen and argon. Hence it could be
concluded, that the presence of nitrogen species in the discharge medium results in a lower H2O2
production. In this regard, two potential scavenging reactions in the aqueous phase are
responsible for the low H2O2 production. First nitrite is easily oxidized to nitrate by H2O2 (r.6-21)
(Anbar & Taube, 1954).
OHNOOHNO 23222 r. 6-21
0
20
40
60
80
100
120
140
160
0 10 20 30
H2O
2(µ
mo
l/l)
time (min)
Plasma chamber
Air Oxygen Argon
0
5
10
15
20
25
30
0 10 20 30
H2O
2 (µ
mo
l/l)
time (min)
Ozonation chamber
Air Oxygen Argon
64
Simultaneously, oxidation of nitrite to peroxynitrite (O=NOOH), which is an isomer of nitrate has
also been reported (r.6-22). However, in an acidic environment, peroxynitrite is unstable and is
rapidly converted to nitrate (Lukes et al., 2014).
OHNOOHOHOHNO 2222 r. 6-22
According to Locke & Shih (2011), hydrogen peroxide formation in plasma reactors can be
correlated, to some extent, with the amount of hydroxyl radicals produced in the gas phase.
There, hydrogen peroxide is predominately produced through the recombination of hydroxyl
radicals (r.6-23) or through excitation of water molecules by hydroxyl radicals (r.6-24)
22OHOHOH r. 6-23
22*
2 OHOHOH r. 6-24
Because the amount of hydroxyl radicals (and thus hydrogen peroxide production) varies with the
power dissipated to the reactor, the energy yield for hydrogen peroxide production has been
proposed as an alternative indicator for reactor efficiency (Locke & Shih, 2011). In single pass
experiments, the energy efficiency is calculated according to eq. 6-3:
t.P
V.M.cG 2222
22
OHOH
OH eq. 6-3
With cH2O2 the concentration hydrogen peroxide concentration in mol l-1, MH2O2 the molar mass of
hydrogen peroxide in g mol-1, V the treated volume in l, P the applied power in kW and t the total
treatment time in hours.
The results shown in figure 6-6 indicate that the measured H2O2 concentrations in the DBD
reactor was quite low. At a power of 40 W plasma discharges in air, argon and oxygen
corresponds to an H2O2 energy yield of 25.3 , 44.1 and 49.2 mg/kWh respectively. Locke & Shih
(2011) have reviewed the H2O2 production yield in different plasma reactors. Overall, typical H2O2
energy yields between 0.04 g/kWh and 80 g/kWh were observed. The highest energy yields (80
g/kWh) were found for reactor designs in which the plasma was generated directly into the liquid
phase. Other reactor types resulted in lower energy yields. For most DBD reactors, the H2O2
energy yield was estimated around 2.70 g/kWh. Other recent scientific reports have confirmed
these calculated energy yields. In this study, the H2O2 energy was even lower (25.3 mg/kWh in air,
44.1 mg/kWh in argon and 49.2 mg/kWh in oxygen). Low energy yields for hydrogen peroxide
production in the plasma reactor used in this research can be explained by the presence of
Zorflex® textile. Indeed, activated carbon is known for its decomposition properties towards
hydrogen peroxide (Khalil et al., 2001; Takaoka et al., 2007). It has been suggested that the
decomposition of hydrogen peroxide is initiated by the exchange of a hydroxyl group, bounded on
the activated carbon surface, with a hydroperoxyl (OH2-) group originating from the ionization of
65
hydrogen peroxide in water (r-6.31). Subsequently, the formed AC-OOH group can further
decompose, resulting into the regeneration of the activated carbon surface (r.6-32) (Huang et al.,
2003):
OHOOHACOOHHOHAC 2 r.6-31
2222 OOHOHACOHOOHAC r.6-32
Low amounts of hydrogen peroxide were also determined in the ozone chamber. The highest
hydrogen peroxide production was found when oxygen is used. The production of hydrogen
peroxide from ozone is the result from the self-decomposition of ozone in the plasma chamber.
Lower hydrogen peroxide production by ozonation with air can be explained by the fact that
ozone content in air plasma gas is lower.
6.3 CONCLUSION
The plasma chemistry is, both in the gas and liquid phase, highly dependent on the plasma
discharge medium. Therefore, production of active species was studied in the gas phase by optical
emission spectroscopy as well as in the liquid phase through investigation of solution parameters
such as pH and conductivity. For plasma discharges in air it was found that, in contrast to plasma
discharges in oxygen and argon, a considerable amount of nitrogen containing species such as
nitrite and nitrate were detected. For these species, it is known that they can act as a scavenger
for hydroxyl radicals, ozone and hydrogen peroxide. Due to scavenging reactions, air plasmas are
considered to be less effective for micropollutant removal. Due to the absence of scavengers in
argon and oxygen plasmas, higher amounts of reactive species are available for micropollutant
decomposition.
In addition, the plasma-catalytic reactor system was characterized towards the energy yield for
hydrogen peroxide production (GH2O2). Although the review of Locke & Shih claimed that this
parameter is a good representative for hydroxyl radical production, and thus micropollutant
decomposition, this is probably not entirely correct. According to their review, the highest energy
yields for hydrogen peroxide production are obtained for reactor systems with plasma discharges
directly in the liquid. However, another review reported that this reactor type is one of the worst
performing reactors according to the G50 energy yield parameter (Malik, 2010). For these reasons,
there are some serious doubts about the suitability of both energy yield parameters (G50 and
GH2O2) to assess reactor performance. Moreover the presence of Zorflex® activated carbon cloth in
the reactor enhances hydrogen peroxide decomposition, causing additional hydroxyl radical
formation. For these species, it is assumed that they are important contributors for
micropollutant elimination. Hence the presence of Zorflex possibly leads to a more efficient
removal of micropollutants.
66
Chapter 7 Optimization of a DBD plasma reactor
This chapter summarizes the optimization results for the removal of eight model compounds in a
DBD plasma reactor. In the first section, the influence of micropollutant adsorption on Zorflex®
active carbon cloth is investigated. Then, the reaction kinetics for micropollutant removal in batch
configuration are elucidated. The third section, discusses the micropollutant removal in the single
pass mode. Section four discusses the operational parameter optimization for the removal of
atrazine in the plasma reactor, operating in single pass configuration. After discussion of the
influence of each individual parameter the energy efficiency was summarized. Finally, the
optimization results are summarized and optimal settings for an energy efficient removal of
micropollutant is proposed
7.1 ADSORPTION
Plasma-assisted destruction of micropollutants in the plasma chamber can be considered as the
result of micropollutant adsorption on Zorflex® active carbon cloth, and micropollutant
decomposition by means of plasma interaction. In order to elucidate the exact role of
micropollutant removal by adsorption on the active carbon cloth, a single component adsorption
test for both alachlor and diuron was performed in a preliminary experiment. In this adsorption
experiment, the initial concentration was set on 100 µg l-1. Figure 7-1 shows the adsorption
removal profiles for alachlor and diuron.
Figure 7-1: Diuron and alachlor removal by adsorption on Zorflex® active carbon cloth
From the experimental results, the total removal rate of alachlor and diuron after 30 minutes of
adsorption were calculated by eq. 7-1:
0
t
C
C1R eq. 7-1
0,00
0,10
0,20
0,30
0,40
0,50
0,60
0,70
0,80
0,90
1,00
0 5 10 15 20 25 30
C/C
0
Time (min)
Alachlor Diuron
67
With R the total removal efficiency after 30 minutes of adsorption, C0 the initial concentration (µg
l-1), and Ct the final concentration (in µg l-1) after adsorption.
As can be seen from figure 7-1, a high adsorption potential for Zorflex® (> 75%) was found for
both micropollutants. Diuron exhibited the highest removal rate (96.9 %) after 30 minutes of
adsorption treatment. For alachlor a significant lower removal efficiency (76.6 %) was found. The
substantial lower removal efficiency for alachlor can be understood by a comparison of the
micropollutant physicochemical properties. Because adsorption on carbonaceous materials is
largely affected by non-polar interactions between adsorbent and adsorbate, adsorption
efficiency can be explained by the hydrophobicity of a micropollutant. Since the octanol-water
partition coefficient (log KOW) is a good representative for adsorption tendency, this physico-
chemical parameter is useful for the comparison of adsorption differences between
micropollutants. According to a general view in literature, alachlor is characterized by a lower
hydrophobicity (log KOW = 2.63) than the diuron (log KOW = 2.85) (El-Nahhal, 2015; Kumar et al.,
2013). Therefore, diuron theoretically tends to adsorb stronger on hydrophobic surfaces such as
active carbon, which was in agreement with our experimental observations.
Although Zorflex® has shown a high adsorption potential, due to its strong porosity and high
surface area, the extensive usage of Zorflex® in adsorption experiments is expected to lead to
complete exhaustion of adsorptive capacity. Moreover, it should be emphasized that only
micropollutant adsorption does not lead to micropollutant degradation. However, removal
percentage of micropollutants by adsorption did not change during the complete series of
experiments conducted in this work (data not shown), suggesting that Zorflex is continuously
regenerated by plasma treatment (Vanraes, 2016).
7.2 MICROPOLLUTANT REMOVAL IN BATCH REACTOR CONFIGURATION
In order to investigate the combined effect of adsorption and plasma initiated decomposition on
the micropollutant degradation in the DBD reactor a batch experiment for the single compound
removal of alachlor and diuron was performed. Again, an initial concentration of 100 µg l-1 was
maintained. Further, the experiment was performed with standard operational settings, as
presented in table 7-1. These settings were initially chosen as standard settings in accordance
with earlier experiments conducted with this plasma reactor (Vanraes et al., 2015b).
Experimentally determined degradation kinetics for alachlor and diuron removal in the plasma
reactor are depicted in figure 7-2.
68
Table 7-1: Standard operational settings in batch experiments
Operational parameters Standard settings
Gas type Air
Gas flow 1 SLM*
Water flow 66.30 ml min-1
Concentration 100 µg l-1
Duty cycle 0.15
Power 40 W
* 1 SLM = 1 standard liter per minute. This unit is defined as the gas flowrate in liter per minute, under standard
conditions of temperature (273.15 K) and pressure (101325 Pa)
Figure 7-2: Alachlor removal in DBD plasma reactor
In both cases the micropollutants exhibited a fast removal, with a total removal rate higher than
99 % (99.1 % for alachlor and 99.5 % for diuron) after 30 minutes of plasma treatment in the
batch reactor. Hence, the experimental results reveal that the combination of adsorption with
plasma treatment is very effective in the removal of alachlor and diuron.
For low initial concentrations, micropollutant decomposition by plasma is mostly described by a
first order kinetics (Hijosa-Valsero et al., 2013; Mizrahi & Litaor, 2013). This is also the case for
micropollutant decomposition with other AOPs. If micropollutant decomposition obeys a first
order kinetics, first order reaction rate constants for plasma destruction can be obtained by fitting
a first order kinetic model (eq. 7-2) to the experimental data.
t.kC
Cln
0
t
eq. 7-2
In this equation, C0 represents the initial micropollutant concentration (100 µg l-1), Ct is the final
micropollutant concentration (µg l-1) after a certain treatment time t (min), and k is the first order
reaction rate constant (min-1).
0
0,2
0,4
0,6
0,8
1
0 5 10 15 20 25 30
C/C
0
time (min)
Alachlor Diuron
69
Following this approach, first order reaction rate constants kA = 0.51 min-1 for alachlor removal,
and kD = 0.78 min-1 for diuron removal were obtained. These reaction constants were about 5
times and 7.5 times higher than the removal rate constants calculated for alachlor and diuron
removal by adsorption respectively. Due to the higher reaction rate constant for removal of
diuron, a better energy efficiency (EEO = 3.90 kWh/m³) was achieved in comparison with the
energy efficiency of alachlor removal (EEO = 6.10 kWh/m³) (table 7-2).
Table 7-2: Comparison of micropollutant removal by adsorption and plasma decomposition
Diuron Alachlor
k (min-1) % removal k (min-1) % removal
Adsorption 0.10 96.9 0.10 76.6
Adsorption +
plasma
0.78 99.5 0.51 99.1
EEO (kWh/m³) 3.90 6.10
Thus, a higher micropollutant removal efficiency have been observed in the adsorption + plasma
experiment in comparison with the adsorption experiment. The higher observed micropollutant
removal, together with the higher first order reaction rate constants in the case of the combined
adsorption and plasma process indicated that the process efficiency was significantly increased
when plasma treatment has been applied in the setup. Indeed, it is known that plasma combines
the production of a wide variety of reactive oxidizers (OH•, O3, H2O2), which could stimulate
additional micropollutant destruction after to adsorption on Zorflex®. No plasma experiment was
performed with only plasma, in the absence of the Zorflex® textile, since the presence of the
textile is required for formation of a stable water film.
Based on the experimental findings, following explanation has been proposed. First,
micropollutants present in the bulk liquid phase diffuse from the bulk liquid to the surface of the
Zorflex®. Next, micropollutants are adsorbed on the Zorflex®. This adsorption process allows an
increased local micropollutant concentration in the region near the plasma-liquid interface,
resulting in more interactions between adsorbed micropollutants and plasma produced species.
Next to the interaction of plasma active species with molecules adsorbed on the Zorflex®, plasma
produced species are also able to initiate micropollutant decomposition in the bulk liquid.
According to the kinetic model of Hong et al. (1996), micropollutant abatement in the liquid phase
is dominated by i) direct ozonation and ii) the peroxone process.
Experimental evidence for the claim that micropollutants are predominately degraded at the site
of adsorption was delivered by Vanraes et al. (2015a). Atrazine destruction was investigated in a
DBD reactor equipped with an adsorptive nanofiber membrane. The exact details about the used
reactor setup are discussed elsewhere (Vanraes et al., 2015a). In the absence of the nanofiber
membrane, a degradation efficiency of 61.0 % was observed, whereas a significant higher
degradation efficiency of 84.6% is found in the presence of the nanofiber membrane (Vanraes et
70
al., 2015a). Moreover, simulation of atrazine degradation by direct oxidation, the peroxone
process, and a combination of both processes could only explain 3.4 %, 10.4 % and 13.5 %
atrazine degradation, respectively (Vanraes et al., 2015a). Hence, it can be reasonably assumed
that adsorption on a reactor membrane in combination with plasma-assisted destruction on the
site of adsorption, and in the liquid results in higher micropollutant degradation efficiencies.
7.3 MICROPOLLUTANT REMOVAL IN SINGLE PASS REACTOR
CONFIGURATION
Batch plasma reactor systems are ubiquitously found in literature. In such reactor systems, a
certain volume of aqueous solution is introduced, and pumped around in the reactor system for a
certain treatment time. Although such reactor designs are particularly important for studying the
chemical kinetics of micropollutant decomposition, they are unpractical for the treatment of large
volumes of wastewater. Moreover, the future implementation of a plasma reactor in existing
MWWTPs, requires a certain degree of continuity, since large volumes needs to be treated.
Therefore, micropollutant removal in a continuous flow (single pass) reactor configuration is more
attractive. Following this line of thought, the batch reactor system was adapted to a single pass
configuration. The prepared reactor influent consists of a synthetic wastewater containing all
eight micropollutants (dichlorvos, diuron, atrazine, pentachlorophenol, alachlor, bisphenol A,
carbamazepine and 1,7-α-ethinylestradiol) dissolved in deionized water. The initial concentration
was set on 100 µg l-1. Standard settings, as denoted in table 7-1 were used. In order to measure
the amount of micropollutant removal by adsorption of Zorflex®, a first sample was taken before
plasma was switched on. Subsequently, plasma was turned on and effluent samples
(approximately 60 ml) were taken at 2.5, 5, 10, 15, 20, 25 and 30 minutes after plasma treatment.
Figure 7-3 shows the measured effluent concentrations for bisphenol A and carbamazepine,
exposed to the combined adsorption and plasma treatment process in the continuous flow
reactor design. Note that in single pass experiments equilibrium concentrations in the effluent are
measured, in contrast to reaction kinetics in batch mode.
Figure 7-3: Micropollutant removal of bisphenol A (BISA) and carbamazepine (CARB) in single pass mode under
reference conditions, mentioned in table 7.1
0,00
0,20
0,40
0,60
0,80
1,00
0 5 10 15 20 25 30
C/C
0(a
.u)
Time (min)
BIS A CARB
Only adsorption
Adsorption + plasma
71
From the experimental results, given in figure 7-3 it could be seen that steady-state conditions are
quasi immediately reached, as indicated by the stationary concentrations, achieved within the
first 5 minutes of operation. Further, it was seen that that the combined adsorption-plasma
treatment was efficient for the degradation of bisphenol A and carbamazepine, although
differences in total removal percentage could be observed. Table 7-3 summarizes the contribution
of adsorption on Zorflex® and plasma destruction to the removal of all micropollutants in the
plasma reactor. The removal efficiency through adsorption on Zorflex® and plasma destruction
was calculated according to eq. 4-1 mentioned in chapter 4. Total removal efficiency is obtained
by summation of the individual micropollutant removal efficiency through adsorption and plasma
destruction.
The total removal efficiency was found to be different for each micropollutant, as summarized in
table 7-3. For example, bisphenol A was removed for 48.1 % by adsorption on the Zorflex® active
carbon textile. Next, bisphenol A was further degraded by plasma interaction. A total removal
efficiency of 66.7 % was reached after 30 minutes of treatment. On the other hand,
carbamazepine showed a similar adsorption behaviour on Zorflex® (46.5 %), but a higher total
removal percentage was observed at the end of the experiment (75.0 %), indicating a lower
decomposition resistance towards the degradation by DBD plasma, in comparison with bisphenol
A. Further, it is noteworthy that the observed degradation percentages of alachlor and diuron
were lower in the continuous flow reactor configuration (66.0 % for alachlor , 63.2 % for diuron)
than in the batch reactor configuration (99.1 % for alachlor, 99.5 % for diuron), due to the higher
volume of treated water and the absence of additional plasma gas bubbling.
Table 7-3: Micropollutant removal in the continuous flow reactor design
Compound Removal by
adsorption (%)
Additional oxidation
by plasma (%)
Total removal (%)
DVOS 33.2 50.4 83.6
DIU 50.6 12.5 63.2
ATR 37.0 19.5 56.4
PCF 68.0 10.3 78.3
ALA 31.7 34.4 66.0
BISA 48.1 18.6 66.7
CARB 46.5 28.4 75.0
ETH 58.9 17.2 76.1
These lower removal efficiencies, observed in the continuous flow reactor design, can be partly
explained by a calculation of the hydraulic retention time (HRT). This parameter describes the
mean residence time of the treated solution in the plasma reactor. Since a constant volume (500
ml) was treated for 30 minutes in the batch reactor, the hydraulic retention time is 30 minutes. In
the continuous flow reactor design, the hydraulic retention time is calculated according to eq. 7-3:
72
F
V eq. 7-3
With V the treated volume in the plasma chamber (400 ml) and F the influent flow rate
(66.30 ml min-1), a hydraulic retention time of 6.03 min-1 was calculated. This was approximately
5 times lower. Therefore, it was expected that the removal efficiency is lower in the continuous
flow reactor design than in the batch reactor design. According to eq.7-3, the hydraulic retention
time is dependent on the volume in the plasma chamber (V), and the water flowrate (F). Hence,
micropollutant removal in continuous flow design can be easily increased, by decreasing the
water flow rate.
Although plasma technology is a promising technique to degrade micropollutants, a possible
disadvantage of this AOP is its large energy demand. The costs associated with the plasma
treatment are obviously an important factor that should be taken into consideration when
selecting the most suitable water polishing method. Since the electrical cost largely contributes to
the overall energy cost, an estimation of the electrical energy consumption is important. The
energy demand of a certain AOP system can be evaluated using the electrical energy per order
figure-of-merit (EEO). This important parameter was already introduced in chapter 2 as the
required electrical energy, expressed in kWh, for 90 % conversion of a certain compound,
occurring in 1 m³ of water (Bolton et al., 1996). For continuous flow reactors the EEO value is
calculated with eq. 2-11. The energy efficiency for plasma destruction was calculated for each
compound. These EEO values are highlighted in table 7-4.
Table 7-4: Energy efficiency expressed as EEO for micropollutant degradation in the plasma reactor
Micropollutant EEO (kWh/m³)
DVOS 12.9
DIU 23.4
ATR 27.4
PCF 17.7
ALA 26.5
BISA 19.7
CARB 7.15
ETH 15.5
From table 7-4 it can be concluded that the energy efficiency of micropollutant degradation in the
plasma reactor can substantially differ among the micropollutants. Plasma destruction was found
to be most energy efficient for the removal of the pharmaceutical carbamazepine and the
pesticide dichlorvos, as indicated by their low EEO value. Other compounds were less effective
removed, as indicated by the higher EEO values. This means that experiment as conducted under
the standard conditions, mentioned in table 7-1, requires relative much energy for persistent
compounds. For both diuron and alachlor, a higher EEO is found in comparison with the batch
experiment (EEO = 23.4 vs 3.9 kWh/m³ for diuron and 26.5 vs 6.1 kWh/m³ for alachlor). The lower
73
EEO value observed in the batch experiment is attributed to additional plasma gas bubbling in the
ozonation chamber. Also, it should be pointed out that the calculated EEO values were obtained
from a lab scale test on a synthetic wastewater, contaminated with only the previously
mentioned micropollutants. In practice, pilot scale experiments with real wastewater will surely
be characterized by the presence of other contaminants and radical scavengers, possibly reducing
the energy efficiency of the plasma treatment (Hijosa-Valsero et al., 2013).
7.4 OPTIMIZATION OF OPERATIONAL PARAMETERS
In the standard experiment it was shown that plasma treatment required high energy
consumption, especially for the removal of persistent compounds such as pesticides. However, it
will be possible to lower the energy consumption by performing an optimization of the
operational parameters. In this research the individual effect of different physical and chemical
parameters on the energy efficiency of micropollutant removal in continuous flow reactor
configuration was investigated by adapting a classical one-parameter-at-a-time approach. The
operational parameters studied in this research included the working gas type, gas flowrate,
water flowrate, duty cycle, applied power and initial micropollutant concentration. Figure 7-4
illustrates the effect of each individual parameter on the energy efficiency (EEO) for the removal
of atrazine. Additional information about the relation between the operational parameters and
the energy efficiency for the other compounds is presented in addendum (appendix I).
7.4.1 Effect of the working gas
In order to investigate the effect of the working gas on the energy efficiency, the standard
experiment was slightly adapted. Although all parameters mentioned in table 7-1 were held
constant, the working gas was changed in order to test the effect on the energy efficiency. Three
different working gas types (air, argon and oxygen) were investigated, and results were presented
in figure 7-4 (a). As could be seen in figure 7-4 (a), the energy efficiency for atrazine removal
decreased in the order oxygen > argon > air. The highest energy efficiency was achieved when
oxygen was used as a working gas.
The effect of the working gas has been extensively studied in literature. In most studies, air was
used as a feed gas ( Kobayashi et al., 2009; Dojčinović et al., 2011; Lesage et al., 2014; Aonyas et
al., 2016). Plasma discharges in nitrogen (Feng et al., 2016), oxygen (Bubnov et al., 2007; Mok et
al., 2008; Bobkova et al., 2014) and noble gases such as argon and helium (Hijosa-Valsero et al.,
2013) were also reported. In most studies, plasma discharges in oxygen were reported as the
most energy efficient (Feng et al., 2016). Although argon perform worse than oxygen, it was
sometimes recognized as the most energy efficient working gas, especially for the degradation of
phenolic compounds (Shiota et al., 2013).
The contribution of the working gas to the observed energy efficiency in the plasma reactor can
be interpreted by the relative production of long and short living active species (OH•, O3, H2O2,
NO3-, NO2
-, peroxynitrite…) in the bulk liquid. If the plasma discharge was conducted in air, the low
74
energy efficiency in comparison with argon and oxygen plasmas could be predominately ascribed
to scavenging reactions of ozone (O3) and hydroxyl radicals (OH•). Both species can be scavenged
by nitrogen containing species. Especially nitrite (NO2-) is generally considered as the most
important scavenger for ozone and hydroxyl radicals. The production of nitrogen containing
species was already proven in chapter 6. In the presence of nitrite, ozone is scavenged through
the reaction (r.7-1):
3223 NOONOO r. 7-1
For this reaction a relative high reaction rate constant (1.6 - 5.0 x 105 M-1 s-1) has been reported by
several authors, indicating its high scavenging potential (Garland et al., 1980; Hoigné et al., 1985).
Moreover an acidic environment (pH = 3.17) has been observed in the plasma reactor for plasma
discharges in air. This high acidity inhibits further dissolution of ozone in water. Hence, the
contribution of ozone to micropollutant degradation in the plasma chamber is expected to be
relative small. Furthermore, nitrite is not only a known ozone scavenger, but also a hydroxyl
radical scavenger (r. 7-2) (Nikitenko et al., 2004; Son et al., 2011):
22 NOOHNOOH r. 7-2
The reaction rate of this reaction has been reported in the order k = 6.0 – 14 x 105. Note that OH•
scavenging by nitrite gives rise to the production of NO2 radicals (NO2•). A redox potential
E0 = 1.04 V has been reported for NO2 radicals, which is significantly lower than the redox
potential of hydroxyl radicals (2.80 V). Therefore, its contribution to the degradation of
micropollutants in the plasma chamber is considered to be significantly lower.
When the plasma discharge took place in pure oxygen (100 % O2) vs 21 % O2 in air, a higher
production of OH• and O3 in the gas phase is expected. Moreover, ozone and radical scavenging
reactions does not occur, since nitrogen containing species are absent. Considering both
conditions, it is obvious that oxygen plasmas are related with a relative higher OH• and O3
production than in air plasmas. Due to higher concentrations of these active species in oxygen
plasmas, a more complete micropollutant removal, and thus higher energy efficiency can be
explained.
Plasma discharges in argon does not produce ozone. However, OH• radical production in argon
plasmas is observed, due to electron impact ionization of water molecules. In comparison with air,
no nitrogen containing species are produced. Therefore, an energy efficiency in between the
energy efficiency for plasma discharges in oxygen and air could be expected.
7.4.2 Effect of the gas flowrate
Because optimization of the working gas type has shown that oxygen was the preferred working
gas our reactor system was further optimized by changing gas flow rate. Hence, oxygen gas was
introduced into the reactor at four different gas flow rates (0.1 SLM, 0.225 SLM , 0.5 SLM and 1
SLM). The influence of the gas flow on the energy efficiency is presented in figure 7-4b. As could
be seen in this figure, the energy efficiency for atrazine removal increased from EEO = 25.0 kW/m³
75
at a flowrate of 1 SLM to a EEO value of 7.55 kW/m³ at a flow rate of 0.1 SLM. The results indicate
that a higher gas flow rate exerted a detrimental impact on the energy efficiency of atrazine
removal in the reactor. Higher gas flow rates were thus considered as less efficient.
Similar results were found by Feng et al. (2014), who has studied the effect of the oxygen flow
rate in a DBD reactor with falling water film on the degradation of Rhodamine B, and concluded
that an increase in gas flow rate attributed to a decrease in energy efficiency. However, in
another DBD reactor with falling water film, the effect of the oxygen flow on the degradation of
methylene blue was found to be not significant on the degradation on methylene blue
(Magureanu et al., 2008). In bubble discharges DBD reactors, increases in gas flow usually
contribute to higher energy efficiencies, as reported by Reddy et al. (2013) for textile dyes, and
Kim et al. (2013) for pharmaceuticals.
7.4.3 Effect of the water flowrate
In a next optimization step, the effect of different water flow rates (28.17, 50.42, 66.30, 89.55,
116.0 and 160 ml min-1) was tested. In general, lower water flow rates resulted in higher
degradation percentages. Moreover, higher micropollutant removal through adsorption on
Zorflex® was observed. Indeed, the application of a higher water flow rate is correlated with a
decrease in mean residence time in the active plasma zone (Grinevich et al., 2011 ; Feng et al.,
2016). A higher residence time will result in a more effective degradation of micropollutants, since
the probability that micropollutants collide with active species appears to be higher.
Consequently, micropollutant degradation will be promoted by lower water flow rates.
Although higher degradation percentages were reached by lower flow rates, it was concluded
that a higher water flow rate corresponds with a lower energy efficiency as presented in figure 7-
4 (c). Indeed, the water flow rate (F) exerted a large influence on the energy efficiency, as
illustrated by the EEO formula (eq. 2-11).
However, only a few studies are conducted to unravel the effect of the water flowrate on the
overall degradation efficiency. Only Magureanu et al. (2008) reported about the effect of the
water flowrate on the degradation of micropollutants in a DBD reactor. In their research the
effect of only two solution flow rates (30 ml min-1 and 90 ml min-1) were studied. In contrast to
our experimental findings, the authors reported a slightly higher degradation efficiency at the
highest flowrate.
In conclusion, high water flow rates are preferred for obtaining high energy efficiencies in the
range of the used settings. Nevertheless, maintaining a high water flow rate would consume high
volumes of micropollutant solutions in 30 minutes of optimization experiments. Therefore, the
water flowrate was kept at reference setting (66.30 ml min-1) in further optimization experiments.
76
7.4.4 Effect of duty cycle
At the end of the water flow optimization experiments, residual micropollutant concentrations in
the plasma treated samples were very low. Further optimization would obtain final
micropollutant concentrations, below GC-MS limit of detections. For this reason, further
optimization experiments were performed with an initial micropollutant concentration of
200 µg l-1 instead of 100 µg l-1. In a next optimization step, the effect of the different duty cycles,
ranging from DC = 0.03 to DC = 0.30 on the energy efficiency is investigated. As pointed out
earlier, applied duty cycle represents the percentage of time that power is applied to the reactor.
From this point of view, increasing the applied duty cycle will be related with an increase in the
total power dissipated in the reactor. The experimental results are presented in figure 7-4 (d).
Although the experimental results were variable, the general trend was that the EEO value
decreases for lower duty cycles. However, further research in this reactor type is definitely
needed to confirm these observations.
Moreover, the effect of applied duty cycle to the energy efficiency of micropollutant
decomposition is only poorly described in literature. To the best of our knowledge, Olszewski et
al. (2014) was the only group who has reported about the effect of the duty cycle on the
degradation of micropollutants. In their research, the effect of a 25 % , 50 % and 75 % duty cycle
on the degradation of methyl orange was investigated. A linear decrease in duty cycle from 75 %
to 25 % resulted in an increase in energy efficiency with a factor 2.11. Therefore, their conclusions
are in line with our experiment results.
7.4.5 Effect of power
As a last factor, the influence of the applied power on the energy efficiency was tested. Building
further on the experimental results obtained from the duty cycle optimization, a low duty cycle
should be preferred. However, 30 % duty cycle was initially chosen instead of a lower one, in
order to test reactor performance during heavy duty cycle conditions. With a constant duty cycle
of 0.30, five different power settings (40 W, 52.5 W, 65 W, 77.5 W and 90 W) were used. The
results are presented in figure 7-4 (e). For the two highest power settings, residual atrazine
concentrations were below GC-MS limit of detection. Therefore, only, results for 40 W, 52.5 W
and 65 W are shown in figure 7-4 (e). The total degradation efficiency of atrazine was 98.0 % for a
power of 40 W and 99.6 % for a power of 65 W. Although residual atrazine concentrations
drastically dropped with increased power, the EEO value raised slightly with increasing power,
indicating a less energy efficient atrazine removal. Hence it could be concluded that, within the
tested power settings, a low power should be preferred as most energy efficient setting. The
limited effect of power on EEO indicates power as the control parameter of preference for
applications where removal percentage should be adjusted according to the influent
micropollutant concentrations.
As the power increases, electrons produced by the plasma discharges will gain more electrical
energy from the electric field. Therefore, more gas molecules will be ionized through electron
77
impact ionization (Zhang et al., 2007; Rong & Sun, 2014). Eventually, this will induce a higher
production of active species production in the gas and liquid phase and stronger micropollutant
decomposition.
7.4.6 Effect of the initial concentration
In order to check the limited dependence of EEO on initial concentration, the influent
concentration was varied. Figure 7-4 (f) shows the energy efficiency of atrazine degradation by
different initial concentrations (50 µg l-1, 100 µg l-1 , 200 µg l-1 and 300 µg l-1). In this concentration
range, only a small increase in EEO was observed (EEO = 5.38 for 50 µg l-1 EEO = 7.21 for 300 µg l-
1). Therefore, EEO is indeed a useful comparative parameter between different reactors.
Figure 7-4: Optimization of operational parameters for the removal of atrazine
0,00
5,00
10,00
15,00
20,00
25,00
30,00
EEO
(kW
h/m
³)
Micropollutant
a) Gas type
Air Argon Oxygen
0
5
10
15
20
25
30
0 500 1000
EEO
(kW
h/m
³)
Gas flow (sccm)
b) Gas flow
0
5
10
15
20
0 50 100 150 200
EEO
(kW
h/m
³)
Water flow (ml/min)
c) Water flow
0
2
4
6
8
10
12
14
0 0,1 0,2 0,3 0,4
EEO
(kW
h/m
³)
Duty cycle
d) Duty cycle
0
2
4
6
8
10
40 50 60 70
EEO
(kW
h/m
³)
Power (W)
e) Power
0
2
4
6
8
10
0 100 200 300 400
EEO
(kW
h/m
³)
Concentration (µg/l)
f) Concentration
78
7.5 CONCLUDING REMARKS
In the previous section, the influence of several operational parameters on the energy efficiency
of micropollutant removal in a DBD plasma reactor was discussed. Starting from reference
settings, the individual effect of each parameter on the energy efficiency was investigated. Table
7-6 summarizes the overall optimization process and the increase in energy efficiency for all
micropollutants. In the first row of table 7-6 calculated EEO values are presented for
micropollutant removal under standard settings (working gas: air, gas flowrate 1 SLM, water
flowrate: 66.30 ml min-1, initial concentration: 100 µg l-1, duty cycle 0.15 and power 40 W). The
initial EEO values varied between 12.9 kWh/m³ for dichlorvos, and 27.4 kWh/m³ for atrazine.
Subsequently, Table 7-6 presents the most optimal EEO value for each series of experiments
described in sections 7.4.1 to 7.4.5. In brackets, the percentage of decrease in EEO is given as
compared to the EEO corresponding to the reference value of the varied parameter in the same
series of experiments.
Because there was worked with unoptimized settings for water flow and duty cycle during the last
experiments, an accurate determination of EEO values after full reactor optimization is not
performed. Instead, the lowest observed EEO value for optimal working gas, gas flowrate and
water flowrate parameters is considered as the most optimal EEO value. These values are
reported in the last line of table 7-6. Note that working with an optimized duty cycle of 0.03 will
definitely lead to lower EEO values. Within the range of the investigated parameters, it was
concluded that the gas flowrate, and the applied duty cycle exerted the highest impact on the
increase in energy efficiency. The influence of the applied working gas, on the other hand, was
rather limited, especially for atrazine (16.0 % decrease in EEO) and diuron (25.7 % decrease in
EEO). However, other micropollutants, i.e. 1,7-α-ethinylestradiol were more effectively degraded
when the plasma discharge was performed in oxygen (47.2 % decrease in EEO).
Generally, a decrease in gas flow from 1 SLM to 0.1 SLM resulted in a strong decrease in EEO for
DIU, ATR, ALA, BIS A. However, the gas flow had less influence, on a decrease in energy efficiency
for dichlorvos and 1,7-α-ethinylestradiol. A comparable behaviour was found for the duty cycle.
The average increase in energy efficiency was around 45 % for DVOS, DIU, ATR, ALA & BISA, in
comparison with the reference setting (DC = 0.15). For 1,7-α-ethinylestradiol, the influence of the
duty cycle was even higher (69.4 % decrease in EEO).
Table 7-5: Overview of most optimal EEO in each series of experiments of figure 7-4. The percentages between brackets gives the decrease in EEO in comparison with the EEO of the corresponding reference value of the varied parameter in the same series of experiments.
DVOS DIU ATR ALA BISA ETH
Standard
settings 12.9 23.0 27.4 26.5 19.7 15.5
Gas type:
Oxygen
7.25
(43.8 %)
17.1
(25.7 %)
23.0
(16.0 %)
17.3
(34.7 %)
12.5
(36.5 %)
8.19
(47.2 %)
Gas flow: 5.36 6.40 7.66 6.84 6.54 5.34
79
0.1 SLM (29.3 %) (60.5 %) (69.8 %) (72.4 %) (55.6 % (34.4 %)
Water
flow: 160
ml min-1
2.82
(45.7%)
3.49
(45.9%)
4.16
(42.5 %)
3.88
(42.3 %)
3.34
(48.5 %)
3.64
(27.3 %)
DC: 0.03 2.71
(47.6 %)
3.52
(45.7 %)
4.35
(39.2 %)
3.75
(45.1 %)
2.80
(56.3 %)
1.04
(69.4 %)
P: 40 W 4.72 5.46 5.78 5.62 5.24 4.44
Optimal
EEO < 2.82 < 3.49 < 4.16 < 3.88 < 3.34 < 3.64
Based on the data presented in table 7-6, and the relationships between the operational
parameter settings and measured energy efficiency (figure 7-4), it could be concluded that the
reactor was working most energy efficient for the following settings: gas type oxygen , gas
flowrate 0.1 SLM, water flowrate 160 ml.min-1, duty cycle 0.03 and power 40 W.
7.6 TOXICITY
Potential implementation of AOP systems in an existing wastewater treatment plant does not
only depend of the energy efficiency, but also on the toxicity of the reactor effluent. In this
regard, additional attention was paid on toxicity testing of plasma treated water. Three
experiments were performed in single pass reactor configuration with air, argon and oxygen as
working gas. Other operational parameters were kept at reference settings (gas flow 1 SLM, water
flow 66.30 ml min-1, concentration 100 µg l-1, duty cycle 0.15 and power 40 W). These settings
were estimated to result in significant differences in the effluent toxicity for the different gases,
while this differences were expected to be smaller at the optimized conditions. In order to
measure the toxicity after micropollutant adsorption on Zorflex®, a first sample was taken before
plasma was turned on. The total collected volume was 1000 ml. In order to investigate the effect
of long living oxidants on the toxicity of plasma treated water, half of the sample volume (500 ml)
was boiled. The other part was left untreated. Subsequently, plasma was turned on, and another
1000 ml was collected. Again, half of the sample volume was boiled, and to other part was left
untreated.
Effluent toxicity of all samples was tested by a grow rate inhibition test on micro-algae (P.
subcapitata), in accordance with the OECD guideline for growth inhibition toxicity tests with algae
and cyanobacteria (OECD, 2016). All toxicity tests were performed at three dilution levels (0x, 10x
and 100x), over a total period of 3 days. These dilutions were prepared by serial dilution from the
initial micropollutant solution (100 µg l-1), before toxicity testing.
Since the presence of toxic compounds could induce algae death, toxicity was tested by
observation of algae growth kinetics. Exponential growth of the algae population can be described
by a first order kinetics (Kessick, 1974) (eq. 7-4):
X.µdt
dX eq. 7-4
80
With X the algae concentration at time t (days), and µ the algae growth rate (d-1). By integrating
and rearranging eq.7-4, an expression is obtained which enables algae growth rate calculation
from the experimental data (eq. 7-5):
t.µ0 e.xx eq. 7-5
x0 is the initial cell density, µ the specific growth rate (d-1) and t the total duration of the toxicity
test (3 days). After 3 days of operation (72 h) cell densities were measured and algae growth rates
were calculated. If toxic compounds were present in the test mixture, inhibition of algae growth
could be experimentally observed by a decay in specific growth rate (µ). Hence, the observed
growth rate after three days (72 h) of toxicity testing, is an indicator of overall mixture toxicity.
For the adsorption and plasma experiments calculated average growth rates, as obtained after
three days of exposure, are presented in figure 7-5.
In figure 7-5 (a), algae average growth rates after 3 days of exposure are shown for the liquid
sample, collected after adsorption on Zorflex textile®. These samples did not undergo any plasma
treatment. In comparison to the initial sample, experimentally determined average algae growth
rates are significantly higher in the unboiled samples. Therefore the toxicity of the samples was
lower than in the initial micropollutant mixture. This was expected, since Zorflex® has shown
excellent adsorptive capacity, as already pointed out in chapter 7, section 7.1. Remarkably, the
boiled samples have shown a higher toxicity of the initial mixture. This can be explained as
follows. Micropollutants are only partially adsorbed on Zorflex® in single pass experiments. For
most micropollutants the average adsorption was determined around 50 % (see table 7-3). Thus,
significant amounts - up to half of the initial micropollutant concentration – should be expected to
remain still unremoved in the liquid phase. By boiling the samples, micropollutant decomposition
could be initiated through thermal degradation. Possibly, toxic compounds are formed in this
thermal degradation process, and thus largely increasing the overall sample toxicity.
Figures 7-5 (b) and 7-5 (c) give the toxicity results for plasma treated samples with argon and air
used as working gas, respectively. For both working gases, comparable growth rates at the 0x and
10x dilution were observed. However, average algae growth rates were significantly lower at the
100x dilution, relative to the growth rate measured in the initial sample. Furthermore boiling the
samples had not much influence on the average growth rate. Accordingly, it is assumed that
plasma discharges in air and argon induce a higher toxicity of the plasma treated samples due to
the formation of long-living aqueous oxidants or toxic by-products.
In contrast to the plasma discharges in air and argon, application of oxygen as discharge medium
resulted in lower sample toxicity of the plasma treated samples, in comparison with the initial
mixture. The lowest toxicity, as indicated by the highest algae growth rate, was observed in the
boiled sample. (figure 7-5 (d)). These results confirmed that plasma treated samples with oxygen
significantly reduces the effluent toxicity.
81
Figure 7-5: Average growth rates after three days of exposure
Based on the experimental findings, presented in figure 7-5 it was concluded that plasma
discharges in air and argon resulted in a toxicity level which was higher than the original toxicity of
the initial mixture. Probably, toxicity increase for treatment with air and argon can be attributed
to the formation of harmful by-products. Indeed, a complete micropollutant mineralization of
micropollutants due to interaction plasma treatment is rarely achieved. Consequently, plasma
treatment will produce some by-products. These by-products can potentially have a higher
toxicity than the original micropollutants. However, little is known about the exact decomposition
mechanism of micropollutants by plasma technology. Further research is definitely needed to
unravel the contribution of by-product formation on the initial toxicity.
There is still some lack about literature data for toxicity tests after plasma treatment. Moreover,
no comparative toxicity tests are reported for non-thermal plasma treatment with air, argon and
oxygen plasmas. However, some reports about air plasmas are available in literature.
Nevertheless, mostly easily degradable test compounds such as phenolic compounds and textile
dyes were used. For example, Dojcinovic et al. (2011) studied the effect of DBD plasma treatment
in air on the toxicity of three different textile dyes (Reactive Black 5, Reactive Yellow 125 and
Reactive Green 15). Textile dyes were applied in high initial concentration, varying between 50 –
100 mg L-1. Only a low toxicity on A.Salina was reported for Reactive Green 15 (10 % mortality),
whereas zero mortality was found for Reactive Black 5 and Reactive Yellow 125. Furthermore,
0
0,5
1
1,5
2
0x 10x 100x
Gro
wth
rat
e µ
, 3 d
ays
afte
r ex
po
sure
Dilution
a) Adsorption
initial boiled unboiled
0
0,5
1
1,5
2
0x 10x 100x
Gro
wth
rat
e µ
, 3 d
ays
afte
r ex
po
sure
Dilution
b) Argon
initial boiled unboiled
0
0,5
1
1,5
2
0x 10x 100x
Gro
wth
rat
e µ
, 3 d
ays
afte
r ex
po
sure
Dilution
c) Air
initial boiled unboiled
0
0,5
1
1,5
2
0x 10x 100x
Gro
wth
rat
e µ
, 3 d
ays
afte
r ex
po
sure
Dilution
d) Oxygen
initial boiled unboiled
82
Krugly et al. (2015) showed that DBD plasma in air raised the initial toxicity of 2-naphtol with only
5 %.
In comparison with non-thermal plasma, other AOPs are more frequently investigated. Especially
the effect of ozonation on the effluent toxicity has been extensively studied. Most studies
reported a decrease in toxicity after ozonation (Reungoat et al., 2011; Margot et al., 2013 R).
Contrarily, other studies reported about an increase in toxicity (Petala et al., 2008; Magdebrug et
al., 2012). Interestingly, Petala et al. found that the toxic potential of treated effluent significantly
increased when high ozone doses were applied. According to these findings, it might be assumed
that effluent toxicity is largely affected by the amount of active species, produced by the AOP
process. Since the relative production of active species is influenced by the operational parameter
settings, reactor optimization in function of effluent toxicity might be interesting.
Based on the current toxicity results, plasma discharges in air and argon in single pass reactor
configuration should be avoided as a stand-alone technique for water treatment. However, higher
effluent toxicity does not necessary indicate a lower biodegradability of the wastewater. This
makes plasma treatment still feasible as a pre-treatment method for wastewater treatment, prior
to biodegradation in MWWTPs (Comninellis et al., 2008; Mantzavinos & Psillakis, 2004).
83
Chapter 8 Optimization of reactor configuration
In the previous chapter, a DBD plasma reactor with moving water film and activated carbon
textile was optimized for the removal of 8 micropollutants from a synthetic wastewater. The
following operational parameters were found to yield the highest energy efficiency: working gas
oxygen, gas flowrate 0.1 SLM, water flowrate 66.30 ml min-1, duty cycle: 0.03 and applied power:
40 W. When these optimal settings are applied an optimal EEO value of 4.16 kWh/m³ for removal
of atrazine was found. A comparison with literature data showed that the determined energy
efficiency was comparable with other reactor systems used for atrazine degradation. Energy
efficiency of atrazine removal in different plasma reactors described in literature is reported in
table 8-1. Based on the calculated EEO values, it was found that only one plasma reactor
performed a better energy efficiency than our optimized reactor system. The most energy
efficient plasma reactor consists of a gas phase plasma reactor based on a corona discharge over
a falling water film. Moreover, this reactor was the only plasma reactor which operated in single
pass configuration. For this reactor system, an EEO value of 3.7 kWh/m³ was calculated for the
removal of atrazine (Gerrity et al., 2010).
Table 8-1: Energy efficiency for the removal of atrazine in different plasma reactor systems, adapted from Vanraes et al., (2015b)
Reactor type Initial concentration
(mg l-1)
EEO (kWh/m³) References
Pulsed corona over water
film
0.0011 3.7 (Gerrity et al.,
2010)
AC DBD in oxygen over
falling water film with
Zorflex®
0.1 4.16 Our work
(optimized
settings)
Pulsed corona in liquid phase 5 4.9 (Mededovic &
Locke, 2007)
AC DBD in air 21.6 19.7 (Feng et al., 2016)
Pulsed arc in liquid phase 0.11 19.7 (Karpel Vel Leitner
et al., 2005)
AC DBD in air over falling
water film with Zorflex®
0.1 27.4 Our work
(standard settings)
AC DBD in He over falling
water film
5.0 50.6 (Maria Hijosa-
Valsero et al.,
2013)
Pulsed corona over water
surface
25.9 72.3 Hoeben et al.,
(2000)
Ac corona for ionized air
bubbling
5.0 86.9 (Wohlers et al.,
2008)
DBD for UV irradiation and
ionized air bubbling 5.8 106 (Zhu et al., 2014)
84
8.1 OPTIMIZATION OF REACTOR CONFIGURATION
Although already an excellent energy efficiency was reached with the optimized plasma reactor
system, further increase in energy efficiency is still possible by slightly adapting the reactor
configuration. This was tested in a next series of experiments. To this end, the single pass reactor
configuration (1P) used in the optimization experiments, described in previous chapter, was
adapted to three alternative reactor configurations, denoted as the 1O, 1P2O and 1O2P reactor
configuration. All these alternative reactor designs operated in single pass mode. Information
about the exact reactor operation was explained in the materials and methods section (see
section 4.3.2). Briefly, for the 1O reactor configuration, micropollutant solution was introduced in
the ozonation chamber. Next, working gas (air and argon) was introduced in the plasma reactor,
and plasma gas (mainly containing ozone) was produced when plasma was applied. Note that in
the 1O configuration deionized water was introduced into the plasma reactor, instead of
micropollutant solution. Subsequently, produced plasma gas was bubbled through the
micropollutant solution.
The 1P2O and 1O2P reactor configurations were constructed in order to investigate the synergetic
effect of plasma treatment with additional plasma gas bubbling. In the 1P2O reactor, the
micropollutant solution was first treated with plasma in the plasma chamber, and subsequently
transferred to the ozonation chamber. There, plasma gas produced in the ozonation chamber was
bubbled through the micropollutant solution. In the 1O2P configuration, the micropollutant
solution was first ozonated with plasma gas produced in the plasma reactor and subsequently
treated with plasma in the plasma chamber.
All experiments were conducted in single pass configuration, with following settings: working gas
oxygen, gas flowrate 1 SLM, water flowrate 66.30 ml min-1, duty cycle 0.15 and power 40 W. The
initial micropollutant concentration was set on 200 µg L-1. Note that non-optimized settings were
used in these series of experiments. Figure 8-1 shows the effluent concentration for atrazine in
function of treatment time for the reactor configurations 1O, 1P, 1P2O and 1O2P. Note that all
experiments were performed in single pass mode, resulting in the measurement of equilibrium
concentrations, instead of reaction kinetics.
Steady state concentrations were achieved in all reactor configurations, within the first 10
minutes of operation. In all reactor configurations, the application of oxygen as plasma discharge
medium resulted into the highest atrazine removal efficiency (1P: 65.6 %, 1P2O: 94.2 %, 1O2P:
95.4 %). Further, the highest removal efficiency for atrazine decomposition was achieved when
the 1O2P configuration was used (95.4 %). Similar results were found for the other
micropollutants (data not shown).
85
Figure 8-1: Micropollutant removal of atrazine in 1P, 1O, 1P2O and 1O2P reactor configuration
Additionally, energy efficiency was calculated for micropollutant decomposition in the different
reactor designs 1P, 1O, 1P2O and 1O2P, and for different working gases (oxygen, argon and air)
tested in this research. For discharges in air, argon and oxygen the results are shown in figures 8-2
to 8-4, respectively. Due to the synergetic effect of plasma treatment with subsequent ozonation,
highest energy efficiencies were achieved in the 1P2O and 1O2P configurations. For all
micropollutants and all working gases, it was found that the EEO values decreased in the order:
1O > 1P > 1P2O > 1O2P
Moreover, the application of oxygen gas as discharge medium yielded the lowest EEO values.
Highest EEO values were found for discharges in air. Moreover, the results presented in figures 8-
2 – 8-4 reveal that plasma treatment combined with additional ozonation significantly enhances
the energy efficiency of our reactor system. Both the 1P2O and 1O2P reactor configurations show
a better energy efficiency than only plasma treatment (1P). Only ozonation (1O) of
micropollutants resulted in very poor removal efficiencies for most micropollutants. This was
illustrated by the high EEO values, especially for persistent pesticides such as atrazine, alachlor
and diuron. The low energy efficiency is the result of a poor micropollutant decomposition
efficiency by direct ozonation.
0,0
0,5
1,0
0 10 20 30
C/C
0
time (min)
1O
air argon
0,0
0,5
1,0
0 10 20 30
C/C
0
time (min)
1P
air argon oxygen
0,0
0,5
1,0
0 10 20 30
C/C
0
time (min)
1P2O
air argon oxygen
0,0
0,5
1,0
0 10 20 30
C/C
0
time (min)
1O2P
air argon oxygen
86
Figure 8-2 Comparison of micropollutant removal in 1O, 1P, 1P2O and 1O2P reactor configurations for plasma
discharges in air
Figure 8-3: Comparison of micropollutant removal in 1O, 1P, 1P2O and 1O2P reactor configuration for discharges in
argon
0,00
20,00
40,00
60,00
80,00
100,00
120,00
DVOS DIU ATR PCF ALA BISA CARB ETH
EEO
(kW
h/m
³)
Micropollutants
1O 1P 1P2O 1O2P
0,00
20,00
40,00
60,00
80,00
100,00
120,00
140,00
160,00
180,00
200,00
DVOS DIU ATR PCF ALA BISA CARB ETH
EEO
(kW
h/m
³)
Micropollutants
1O 1P 1P2O 1O2P
87
Figure 8-4: Comparison of micropollutant removal in 1O, 1P, 1P2O and 1O2P reactor configuration for discharges in
oxygen
Micropollutant degradation efficiency mainly depends on two factors: the value of the second
order reaction rate constant for the reaction of micropollutants with ozone (kO3), and the amount
of ozone dissolved in the liquid phase. No ozone concentrations in the gas and liquid phase were
measured in this work. According to a general view in literature, it was found that kO3 values for
reactions of micropollutants with ozone are quite low for most micropollutants. Typical values
between 10-5 and 1.6.109 M-1s-1 are reported (Jin et al., 2012; Sudhakaran & Amy, 2013; Von
Gunten, 2003). Table 8-2 gives an overview of all kO3 values for the micropollutants investigated
throughout this work.
Table 8-2: Overview for first order reaction rate constants of ozone with micropollutants studied in this work
Micropollutant kO3 (M-1s-1) Reference
Alachlor 3.8 Von Gunten et al. (2003)
Atrazine 6.0 Acero et al. (2000)
Diuron 19.02 Solis et al. (2016)
Pentachlorophenol 10.05 Kim & Moon (2000)
Dichlorvos - -
Bisphenol a 1.6.109 Umar et al. (2013)
Carbamazepine 3.105 Huber et al. (2003)
1,7-α-ethinylestradiol 3.106 Huber et al. (2003)
For alachlor, atrazine, diuron and pentachlorophenol, very low kO3 values, ranging between 3.8 –
19.02 M-1 s-1 were found. Due to the very low kO3 values a slow reaction between ozone and these
micropollutants is assumed. Contrarily, significant higher kO3 values (106 - 109 M-1.s-1) are typically
reported for reactions of ozone with pharmaceutical compounds (Huber et al., 2003 ; Von Gunten
et al., 2003). Therefore, micropollutant decomposition occurs in a very selective way.
Micropollutants with a high kO3 value are rapidly degraded, whereas micropollutants with a low
0,00
5,00
10,00
15,00
20,00
25,00
30,00
DVOS DIU ATR PCF ALA BISA CARB ETH
EEO
(kW
h/m
³)
Micropollutants
1P 1P2O 1O2P
88
kO3 value tend to react slowly with ozone. This can also be seen in figure 8-2. For compounds with
a low kO3 value (atrazine, diuron and alachlor) EEO values higher than 60 kWh/m³ were reported,
indicating a low energy efficiency. Lower EEO values ( EEO < 40 kWh/m3) are found for
carbamazepine, 1,7-α-ethinylestradiol and bisphenol A, showing a more energy efficient
micropollutant elimination. If argon gas was used in the 1O configuration, almost no
micropollutant degradation was observed. Indeed, plasma discharges in argon do not yield any
ozone production (Shainsky et al., 2012).
Plasma treatment in the plasma reactor (1 P), on the other hand, resulted in a significant higher
micropollutant removal and energy efficiency. Micropollutant decomposition in the plasma
chamber is the result of a complex interaction between micropollutants and a wide spectrum of
oxidative species, combined with other chemical phenomena such as UV radiation (Ghezzar et al.,
2013 ; Jiang et al., 2014). The synergetic effect of all chemical species produced by the DBD
plasma discharge creates a very unselective environment. Often, the hydroxyl radical is referred
to as the major chemical species. The unselective nature of hydroxyl radicals is illustrated by the
high kOH• values (> 109 M-1 s-1) for reactions with micropollutants. For each micropollutant used in
this work, the kOH• value is highlighted in table 8-3. By comparing, the kO3 and kOH values,
presented in table 7-2 and 7-3 respectively, it is seen that the kOH values are a lot higher than the
kO3 values, indicating a faster decomposition with OH radicals. This is certainly the case for
alachlor, atrazine, diuron and pentachlorophenol. Bisphenol A, carbamazepine and 1,7-α-
ethinylestradiol exert higher kO3 values (1.6 . 109 , 3.105 and 3.106 M-1.s-1, respectively), but even
for these compounds the kOH• second order reaction rate constants are still higher (1.0 . 1010, 8.8
.1010 and 9.8 109 M-1.s-1, respectively). Hence, micropollutant degradation by plasma alone was
expected to be more energy efficient than micropollutant degradation by direct ozonation alone,
which is in agreement with our experimental findings.
Table 8-3: Overview for first order reaction rate constants of hydroxyl radicals with micropollutants studied in this work
Micropollutant kOH• (M-1s-1) Reference
Alachlor 7.0 . 109 Von Gunten et al (2003)
Atrazine 3.0 . 109 Acero et al. (2000)
Diuron 6.6 . 109 Solis et al. (2016)
Pentachlorophenol 3.7 . 109 Weavers et al. (2000)
Dichlorvos - -
Bisphenol A 1.0 . 1010 Rosenfeldt et al. (2004)
Carbamazepine 8.8 . 109 Huber et al. (2003)
1,7-α-ethinylestradiol 9.8 . 109 Huber et al (2003)
A comparison of the reactor configurations 1P2O and 1O2P with only plasma treatment in the
plasma chamber (1P), revealed that both the 1P2O and 1O2P reactor configurations were much
more energy efficient. The 1O2P process was found to be the most energy efficient reactor
configuration. The superiority of the 1O2P configuration in terms of energy efficiency could be
related to the specific chemical reactions, occurring in the plasma and the ozone chamber.
89
In the 1P2O reactor configuration, the influent is first treated in the plasma chamber. There,
various chemical species are formed in the liquid phase. In air plasmas, OH• radicals, ozone (O3)
and hydrogen peroxide (H2O2) are formed as reactive oxygen species. Due to the presence of
nitrogen in air, nitrogen containing species such as nitrate (NO3-), nitrite (NO2
-) and peroxynitrite
(ONOO-) are also produced in the bulk liquid through different reactions, as already explained in
chapter 6. In oxygen plasmas, only oxygen species (OH•, O3 and H2O2) are formed. Furthermore,
oxygen plasmas are characterized by a higher O3 production. Hence, the more energy efficient
micropollutant degradation in oxygen plasmas is ascribed to a higher active species production,
combined with less scavenging reactions, due to the absence of nitrite and nitrate. The exact
scavenging mechanisms were extensively described in section 7.4.1. In a next step, the plasma
treated solution is transported to the ozonation chamber, and subsequently bubbled through
with the exhausted plasma gas (mainly ozone). During ozonation of the solution treated with
plasma gas, ozone in the plasma gas reacts with residual hydrogen peroxide, present in the
plasma treated water. This chemical process is known as the peroxone process (Merényi et al.,
2010). Micropollutant degradation in the peroxone process is initiated by the rapid dissociation of
hydrogen peroxide into hydrogen (H+) and hydroperoxyl (HO2-) ions:
HHOOH 222 r. 8-1
Next, hydroperoxyl ions initiate further ozone decomposition through a series of reactions (r. 8-2
– r. 8-5):
3232 OHOOHO r. 8-2
HOHO 22 r. 8-3
33 HOHO r. 8-4
OHOHO 23 r. 8-5
Ideally, 1 mole of hydrogen peroxide reacts with 2 mole of ozone to yield 2 mole hydroxyl radicals
and 3 mole oxygen gas (r. 8-6):
2223 O3OH2OHO2 r. 8-6
Thus, in addition to the production of OH• radicals in the plasma chamber, a significant amount of
OH• radicals is produced in the peroxone process. This higher OH• production is the reason for the
higher atrazine degradation and the higher energy efficiency, in comparison with only plasma
treatment in the plasma chamber.
On the other hand, in the 1O2P process, the micropollutant solution is first ozonated in the ozone
chamber with plasma gas generated in the plasma chamber. If air or oxygen is used as discharge
medium, the plasma gas contains mainly ozone. When the plasma gas is introduced in the
ozonation chamber, self-decomposition of ozone is initiated due to slight alkaline conditions
90
(initial pH micropollutant solution = 8.49). It gives rise to the production of multiple radical
species, such as OH• and OH2• (Beltran, 2003).
The different chemical species formed during the decomposition of ozone enhance
micropollutant decomposition. In contrast to the 1P2O reactor configuration, no nitrogen
containing species are present in the ozonation chamber. Therefore, radical scavenging by NO2- is
supposed to be negligible. Subsequently, additional micropollutant elimination is achieved by
interaction between micropollutants and active species produced in the plasma chamber. Due to
the absence of scavenging reactions, this configuration is the most optimal one.
91
Chapter 9 Conclusions and future perspectives
9.1 CONCLUSIONS
In the present research, the applicability of a DBD plasma reactor with moving water film and
Zorflex® textile was investigated for the degradation of eight micropollutants (dichlorvos, diuron,
atrazine, pentachlorophenol, alachlor, bisphenol A, carbamazepine and 1,7-α-ethinylestradiol) in
a synthetic medium. In order to get a deeper understanding about the generation of active
species in the gas phase and the micropollutant decomposition mechanisms occurring in the
liquid phase characterization techniques such as optical emission spectroscopy, and pH –
conductivity measurements were applied.
Because the future implementation of any certain advanced treatment will be largely dependent
on the energy efficiency of the system, micropollutant removal in the plasma chamber was
optimized in function of the energy efficiency. The influence of different operational parameters,
including the working gas, gas flow rate, water flow rate, duty cycle, applied power and initial
concentration) on the energy efficiency of micropollutant removal was studied. In a first
experiment with standard settings (gas type: air, gas flowrate: 1 SLM, water flowrate: 66.30 ml
min-1, initial concentration: 100 µg l-1, duty cycle 0.15 and power: 40 W), an initial EEO value of
27.4 kWh/m³ was observed for the removal of atrazine. An investigation of the effect of individual
settings on the degradation of micropollutants showed that the most energy efficient removal of
micropollutants was observed if oxygen was applied as working gas. Further, a decrease in gas
flowrate and duty cycle combined with an increase in water flowrate had a huge impact on an
energy efficiency, whereas the influence of power was rather limited. Furthermore, following
parameter settings will led to the most efficient micropollutant degradation: gas type: oxygen, gas
flowrate: 0.1 SLM, water flow rate: 160 ml min-1, duty cycle 0.03 and power: 40 W. The
concentration only had a small effect on the EEO value. After method optimization, a final EEO
value of 4.16 kWh/m³ was found for atrazine, which correlates with an overall increase in energy
efficiency of 84.8 % in comparison with the EEO value calculated under reference settings. Similar
effects were observed for the removal of other investigated compounds. Hence, it could be
reasonably concluded that operational parameter optimization is a very effective way to enhance
the energy efficiency of the DBD plasma reactor. Moreover, comparison of the energy under
optimal settings, showed that our reactor system performed well in comparison with other
reactor systems described in literature. Only one plasma reactor yielded a better energy efficiency
for the removal of atrazine (EEO = 3.7 kWh/m³) than the optimized plasma reactor studied in this
research (EEO = 4.16 kWh/m³).
Fortunately, the overall reactor performance could be further enhanced. This was tested in a next
series of experiments. To this end, the single pass reactor configuration was slightly adapted. With
our reactor system, three alternative configurations could be constructed. In the first construction
only ozonation with plasma gas, produced in the plasma chamber was performed. This
experiment was referred to as the 1O reactor configuration. Comparison with the initial standard
experiment in the plasma chamber revealed the energy efficiency for ozonation of atrazine was
92
higher (EEO, 1O = 63.2 kWh/m³, EEO, 1P = 28.4), and thus less efficient. The combined effect of
plasma treatment with ozonation was also studied. Two reactor configurations were possible. In
the first reactor configuration, the micropollutant solution was first ozonated and subsequently
treated with plasma (1O2P). In the second reactor configuration, the solution and subsequently
ozonated with plasma gas produced in the plasma chamber (1P2O). From both experiments, it
could be concluded that plasma treatment, combined with additional plasma gas bubbling
through the micropollutant solution resulted in a better micropollutant degradation, and hence a
higher energy efficiency. It was found that the EEO value was in both cases lower than when only
plasma treatment was applied (EEO, 1P2O = 8.12 kWh/m³; EEO, 1O2P = 6.73 kWh/m³). The best,
i.e. most energy efficient results were thus observed when the ozone chamber was placed before
the plasma chamber (1O2P configuration), due to the absence of scavenging reactions by nitrites
and nitrates.
Next to the energy efficient removal of micropollutants, toxicity testing of plasma treated water is
important. In this regard, reactor effluent toxicity, is tested after plasma discharges in air, argon
and oxygen. It was found that plasma discharges in air and argon resulted in a higher effluent
toxicity, in comparison with the original micropollutant mixture toxicity. On the other hand,
plasma discharges in oxygen significantly decreased the effluent toxicity. Moreover, in all cases
(air plasmas, argon plasmas and oxygen plasmas) it was observed that boiling the initial sample
resulted into a decrease in effluent toxicity. This indicates that the presence of long living
oxidants, such as hydrogen peroxide and peroxynitrite influence the effluent toxicity in an
important way, although also toxic by-product formation by thermal decomposition can play a
role..
9.2 FUTURE PERSPECTIVES
Based on the presented results in this work, it can be concluded that plasma assisted destruction
of micropollutants is a promising alternative AOP for water treatment, as indicated by the high
degradation percentages and energy efficiencies. Although much research is done, and the
research results are promising, future investigation of plasma for water treatment is still needed.
First, it should be emphasized that still very little is known about the gas phase chemistry,
occurring in the plasma. A more complete understanding about these phenomena should
enhance general knowledge about the mechanisms contributing to micropollutant decomposition
in the liquid phase. Future application of plasma technology for water treatment will be
dependent on the energy efficiency of micropollutant degradation. Hence, reactor optimization
will be important. Although reactor optimization was already initiated in this research it was
concluded that further improve in terms of energy efficiency is still possible. Since it was found
that the single pass 1O2P configuration was more efficient than the 1P single pass configuration,
further research should concentrate on the optimization of this reactor configuration.
Furthermore, there is still a general lack on literature data about the toxicity of plasma treated
water. In this regard a preliminary experiment was already performed in our research group, and
the first results reveal that plasmas generated in oxygen may significantly contribute to a
93
decrease in effluent toxicity. However, plasma discharges in argon and air yielded a higher
effluent toxicity in comparison with the initial mixture. With toxicity testing, the observed
increase in toxicity could not be addressed to one single component. Therefore, further
investigation of possible harmful by-product formation in the liquid phase could be interesting.
This can be achieved by advanced chromatographical techniques such as GC-MS and LC-MS.
When all further experimental results are promising, the treatment of real wastewater streams in
large scale reactors will be the next step. Further optimization of process efficiency will be needed
since real wastewater contains a multitude of chemicals and lower degradation and energy
efficiencies could be expected.
94
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Addendum 1
A. DICHLORVOS
0,00
5,00
10,00
15,00
20,00
25,00
30,00
EEO
(kW
h/m
³)
Micropollutant
a) Gas type
Air Argon Oxygen
0
1
2
3
4
5
6
7
8
9
0 500 1000 1500EE
O (
kWh
/m³)
Gas flow (sccm)
b) Gas flow
0
2
4
6
8
10
12
14
0 50 100 150 200
EEO
(kW
h/m
³)
Water flow (ml/min)
c) Water flow
0
2
4
6
8
10
0 0,1 0,2 0,3 0,4
EEO
(kW
h/m
³)
Duty cycle
d) Duty cycle
0
1
2
3
4
5
6
7
40 50 60 70
EEO
(kW
h/m
³)
Power (W)
e) Power
0
1
2
3
4
5
6
7
0 100 200 300 400
EEO
(kW
h/m
³)
Concentration (µg/l)
f) Concentration
116
B. DIURON
0,00
5,00
10,00
15,00
20,00
25,00
30,00
EEO
(kW
h/m
³)
Micropollutant
a) Gas type
Air Argon Oxygen
0
2
4
6
8
10
12
14
16
18
0 500 1000 1500
EEO
(kW
h/m
³)
Gas flow (sccm)
b) Gas flow
0
2
4
6
8
10
12
14
16
0 50 100 150 200
EEO
(kW
h/m
³)
Water flow (ml/min)
c) Water flow
0
2
4
6
8
10
12
0 0,1 0,2 0,3 0,4
EEO
(kW
h/m
³)
Duty cycle
d) Duty cycle
0
1
2
3
4
5
6
7
8
9
40 50 60 70
EEO
(kW
h/m
³)
Power (W)
e) Power
0
1
2
3
4
5
6
7
8
0 100 200 300 400
EEO
(kW
h/m
³)
Concentration (µg/l)
f) Concentration
117
C. ALACHLOR
0,00
5,00
10,00
15,00
20,00
25,00
30,00
EEO
(kW
h/m
³)
Micropollutant
a) Gas type
Air Argon Oxygen
0
5
10
15
20
25
30
0 500 1000 1500
EEO
(kW
h/m
³)
Gas flow (sccm)
b) Gas flow
0
2
4
6
8
10
12
14
16
18
0 50 100 150 200
EEO
(kW
h/m
³)
Water flow (ml/min)
c) Water flow
0
2
4
6
8
10
12
14
0 0,1 0,2 0,3 0,4
EEO
(kW
h/m
³)
Duty cycle
d) Duty cycle
0
1
2
3
4
5
6
7
8
40 50 60 70
EEO
(kW
h/m
³)
Power (W)
e) Power
0
1
2
3
4
5
6
7
8
0 100 200 300 400
EEO
(kW
h/m
³)
Concentration (µg/l)
f) Concentration
118
D. BISPHENOL A
0,00
5,00
10,00
15,00
20,00
25,00
30,00
EEO
(kW
h/m
³)
Micropollutant
a) Gas type
Air Argon Oxygen
0
2
4
6
8
10
12
14
16
0 500 1000 1500
EEO
(kW
h/m
³)
Gas flow (sccm)
b) Gas flow
0
2
4
6
8
10
12
14
0 50 100 150 200
EEO
(kW
h/m
³)
Water flow (ml/min)
c) Water flow
0
2
4
6
8
10
12
0 0,1 0,2 0,3 0,4
EEO
(kW
h/m
³)
Duty cycle
d) Duty cycle
0
1
2
3
4
5
6
7
8
40 50 60 70
EEO
(kW
h/m
³)
Power (W)
e) Power
0
1
2
3
4
5
6
7
8
0 100 200 300 400
EEO
(kW
h/m
³)
Concentration (µg/l)
Concentration
119
E. 1,7-Α-ETHINYLESTRADIOL
0,00
5,00
10,00
15,00
20,00
25,00
30,00
EEO
(kW
h/m
³)
Micropollutant
a) Gas type
Air Argon Oxygen
0123456789
0 500 1000 1500
EEO
(kW
h/m
³)
Gas flow (sccm)
b) Gas flow
0
2
4
6
8
10
12
14
0 50 100 150 200
EEO
(kW
h/m
³)
Water flow (ml/min)
c) Water flow
0
1
2
3
4
5
6
7
0 0,1 0,2 0,3 0,4
EEO
(kW
h/m
³)
Duty cycle
d) Duty cycle
4,2
4,3
4,4
4,5
4,6
4,7
40 50 60 70
EEO
(kW
h/m
³)
Power (W)
e) Power
0
1
2
3
4
5
6
7
8
0 100 200 300 400
EEO
(kW
h/m
³)
Concentration (µg/l)
f) Concentration
120
F. PENTACHLOROPHENOL
G. CARBAMAZEPINE
0
2
4
6
8
10
12
0 500 1000 1500
EEO
(kW
h/m
³)
Gas flow (sccm)
Gas flow
0
2
4
6
8
10
12
14
0 500 1000 1500
EEO
(kW
h/m
³)
Gas flow (sccm)
Gas flow
