Surrogate-Based Correlation Models in View of Real-Time Control of

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Surrogate-based correlation models in view of realtime control of ozonation of secondary treated municipal wastewater - model development and dynamic validation Michael Chys, Wim T.M. Audenaert, Emma Deniere, Severine Therese F. C. Mortier, Herman Van Langenhove, Ingmar Nopens, Kristof Demeestere, and Stijn W.H. Van Hulle Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b04905 • Publication Date (Web): 27 Nov 2017 Downloaded from http://pubs.acs.org on November 28, 2017

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Environmental Science & Technology

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Surrogate-based correlation models in view of real-

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time control of ozonation of secondary treated

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municipal wastewater - model development and

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dynamic validation

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Michael Chys1,*, Wim T.M. Audenaert1,‡, Emma Deniere1,2, Séverine Thérèse F.C. Mortier3,4,

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Herman Van Langenhove2, Ingmar Nopens3, Kristof Demeestere2,†, Stijn W.H. Van Hulle1,†

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1

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Graaf Karel de Goedelaan 5, B-8500 Kortrijk, Belgium

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2

LIWET, Department of Industrial Biological Sciences, Ghent University Campus Kortrijk,

EnVOC, Department of Sustainable Organic Chemistry and Technology, Ghent University,

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Coupure Links 653, B-9000 Ghent, Belgium

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3

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University, Coupure Links 653, B-9000 Ghent, Belgium

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4

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Ghent, Belgium

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‡

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B-9112 Sint-Niklaas, Belgium

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†

BIOMATH, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent

Department of Pharmaceutical Analysis, Ghent University, Ottergemsesteenweg 460, B-9000

present affiliation: AM-TEAM, Advanced Modelling for Process Optimisation, Hulstbaan 63,

These authors contributed equally to the work.

ACS Paragon Plus Environment

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ABSTRACT

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New robust correlation models for real-time monitoring and control of trace organic contaminant

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(TrOC) removal by ozonation are presented, based on UVA254 and fluorescence surrogates, and

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developed considering kinetic information. The abatement patterns of TrOCs had inflected

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shapes, controlled by the reactivity of TrOCs towards ozone and HO radicals. These novel and

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generic correlation models will be of importance for WRRF operators to reduce operational costs

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and minimize by-product formation. Both UVA254 and fluorescence surrogates could be used to

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control ∆TrOC, although fluorescence measurements indicated a slightly better reproducibility

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and an enlarged control range. The generic framework was validated for several WRRFs and

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correlations for any compound with known kinetic information could be developed solely using

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the 2nd order reaction rate constant with ozone (kO3). Two distinct reaction phases were defined

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for which separate linear correlations were obtained. The first was mainly ozone controlled,

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while the second phase was more related to HO reactions. Furthermore, parallel factor analysis

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of the fluorescence spectra enabled monitoring of multiple types of organic matter with different

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O3 and HO• reactivity. This knowledge is of value for kinetic modelling frameworks and for

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achieving a better understanding of the occurring changes of organic matter during ozonation.

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INTRODUCTION

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Water resource recovery facilities (WRRFs) have been identified as a major pathway through

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which trace organic contaminants (TrOCs) enter the aquatic environment.1 TrOCs such as

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pharmaceuticals may trigger unwanted ecological effects (e.g. bacterial resistance, chronic

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toxicity, endocrine disruption and feminization of fish) or might expose human individuals due

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to increased water reuse.2–5 Driven by pending (European) legislation6,7 and/or as a precaution to

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protect the aquatic environment and drinking water sources, operators of WRRFs are preparing

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for plant upgrades with advanced tertiary treatment. Ozonation, activated carbon filtration and

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membrane filtration have been extensively tested in this respect.8 Ozonation is a major

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technology currently being implemented in Switzerland (regulatory driven) but also in other

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countries.6,9 At ozone dosages up to 1 g O3 g-1 DOC (dissolved organic carbon), the parent

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TrOCs can be removed above 80 % or even below detection limits for components with kO3 > 10

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M-1 s-1.10

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Although WRRF effluent ozonation is rapidly growing, currently applied control strategies for

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ozone dosage (e.g. flow-based) result in sub-optimal operation. Not only the effluent flow rate,

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but also the ozone demand of secondary effluent are highly dynamic in time. Optimal ozone

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dosing is required to 1) lower operational costs, 2) consistently and adequately remove target

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TrOCs and 3) minimize by-product formation. With respect to the latter, bromate and N-

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nitrosodimethylamine (NDMA), both examples of carcinogenic by-products, can be formed

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when exceeding effluent-specific threshold values, mostly at higher ozone dosages.11–13 From a

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monitoring perspective, frequent TrOC monitoring is costly and labor intensive.


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Therefore, readily available and real-time surrogate measurement techniques are essential for

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both process monitoring and ozone dose control. Effluent organic matter (EfOM) is a main

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influencer of the ozonation process,14 consuming significant amounts of ozone and indirectly

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initiating hydroxyl radical (HO•) production.15,16 As EfOM is easily characterized using (online)

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measurements, the monitoring of ozone and HO• induced changes of EfOM can be translated

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into indirect monitoring of oxidant exposure, and hence TrOC removal. The following

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measurements have shown to correlate well to ozone dose and TrOC removal: dissolved organic

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carbon (DOC)10,17,18, UV absorption at 254 nm (UVA254)19–26 and total fluorescence (TF)22.

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So far, mostly linear relationships were considered which only appear to produce a good

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correlation for TrOCs highly susceptible to ozone (i.e. mainly O3 rather than HO• controls their

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degradation). Often moderate or poor linear correlations are achieved between the decrease of

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the surrogate (mostly ∆UVA254) and the removal of ozone-recalcitrant TrOCs.10,27 This might

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lead to an over or under prediction of the actual TrOC removal, potentially resulting in

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inefficient TrOC abatement or ozone overdosing, associated with higher operational costs and/or

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a potential for by-product formation. Components with a low reactivity towards ozone (kO3 < 10

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M-1 s-1) show a more convex curved behavior with only limited degradation in ∆UVA254 ranges

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below 20%.24,27 In literature, it is insufficiently addressed how the TrOC-specific properties (e.g.

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kO3) are influencing the linearity of these correlations. HO• production will clearly have a greater

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influence on the removal of TrOCs with a lower affinity for direct ozone reactions. Kinetic

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modelling efforts showed significant deviations between measured and predicted removal for the

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more recalcitrant TrOCs.18 It became clear that HO• production is highly dependent on the

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specific organic matrix. More recently, Chon et al.28 indicated a shift in electron donating

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capacity of the EfOM when increasing the ozone dose, influencing the relation between ∆TrOCs


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and ∆UVA254. Especially ozone resistant species showed no significant removal within a first

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phase of reaction. Ozone decrease follows pseudo first order kinetics after an initial, fast reaction

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phase.29 Consequently, this shift in reaction pathways is likely to influence the used correlation

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models. Further elaboration, with the aim of providing generic models independent of TrOCs

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and effluent samples used during development is now needed.

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Recently, the use of fluorescence spectroscopy has gained a lot of interest.22,25 Compared to a

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single-wavelength UV absorption approach, a more detailed view of the entire (effluent) organic

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matrix (e.g. different chemical moieties) can be extracted from these type of measurements.

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Empirical correlations between differential TF (∆TF) and removal of a wide range of TrOCs

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have been developed.22 Although much information is present, often (parts of) the fluorescence

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Excitation Emission Matrices (EEMs) are used as such, without any statistical processing.

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Transformation rates may differ among different major groups of components (i.e. humic and

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fulvic acid-like, soluble microbial products).30 Statistical methods, deriving the component

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specific zones within the EEMs, are useful to study these transformations. For example, parallel-

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factorial (PARAFAC) analysis has been applied to monitor natural water,31 and different stages

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of wastewater treatment.32,33 Although, PARAFAC analysis is a multi-way method that requires

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model development, a certain number of samples and several minutes of instrumental

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measurement, it derives the wavelengths of interest within complex EEMs. In view of online

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monitoring and control, the measurement time can be significantly reduced by only selecting the

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EEM regions of interest.

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Given the limitations of the current (linear) surrogate models and the possibility to use different

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spectral measurements, the aim of this study was to develop improved correlation models based

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on UVA254 and fluorescence for TrOCs having a wide range of ozone reactivity (kO3 from < 1 to


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106 M-1 s-1) and for a broad range of ozone doses (up to ± 2 g O3 g-1 DOCeq). The shape of the

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correlations was studied to gain in depth process understanding and to provide generic

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knowledge that can be used to construct correlations for any compound for which kO3 and kHO•

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are known. Consequently, a generic framework for an unlimited amount of TrOCs was put

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forward and validated for several WRRFs. Fluorescence data was thoroughly processed using

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PARAFAC to study the EfOM moieties and their reactivity towards ozone and HO•.

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MATERIALS AND METHODS

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Standards and reagents

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All chemicals used were of analytical grade with a purity of at least 98%. Nine TrOCs (all

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pharmaceuticals) were selected based on their ozone reactivity, environmental relevance, and

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occurrence in WRRF effluents to develop the model. Individual stock solutions of 1 g TrOC L-1,

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stored at -18°C in the dark, were used to prepare diluted mixtures. The studied TrOCs can be

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classified in three groups according to their reaction rates with ozone (Table S1). This

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classification is rather arbitrary and – similar to other studies23,34,35 – mainly aimed to facilitate

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the discussion. The individual second order reaction rate constants (kO3) of the TrOCs will be

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most important in developing the correlation model framework.

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Experimental procedures

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Secondary effluent samples were collected from a conventional activated sludge process at the

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WRRF Harelbeke, Belgium (116,100 I.E., operated by Aquafin NV). Three sampling campaigns

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were performed at different times (further denoted as effluent 1, 2 and 3 – effluent 3 was used to

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verify conclusions drawn based on effluent 1 and 2). Clear differences in effluent characteristics

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were noticed when rain fall occurred before sampling (24 and 72 hours), due to dilution effects.

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Data on the effluent characteristics and rain fall before sampling can be found in Table S2.

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Batch ozonation experiments at room temperature were conducted in 1 L glass reactors equipped

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with a mechanical mixer at a speed of 200 rpm (Janke & Kunkel GmbH & Co.KG, Germany).

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Effluent was spiked with a mixed stock solution of the selected TrOCs up to a concentration of

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10 µg L-1 (effluent 1) and 1 µg L-1 (effluent 2 and 3) for each compound. These different

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concentrations were applied to assess the impact of TrOC levels on the correlations. Freshly

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prepared ozone stock solution was added to the spiked effluent with ozone doses varying

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between 0 and 17.3 mg L-1 (or a specific dose up to ± 2 g O3 g-1 DOCeq). The stock solution had

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an ozone concentration of around 90 mg O3 L-1 and was prepared by purging ice-cooled distilled

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water with an ozone/oxygen gas mixture (300 mL min-1) using an oxygen-fed ozone generator

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(up to 8 g O3 h-1, COM-AD-02, Anseros GmbH, Germany). After ozone addition and complete

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reaction (30 min), each reactor was brought to 800 mL with distilled water. As such, equal

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dilution was obtained, independent of the volume of ozone stock added (i.e. dilution < 20 %).

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The samples were analyzed for surrogate parameters and TrOCs, after storage for maximum 3

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weeks at 4°C and -20°C, respectively. Information on handling samples for TrOC analysis is

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given in SI-Text 1.


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For validation of the proposed correlation model, additional effluent was collected from four

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other municipal WRRFs across Belgium with capacities ranging from 500 to 80,000 I.E. The

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plants were selected based on receiving water (i.e. influent), treatment train configuration (i.e.

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size and type of treatment) and effluent characteristics. Details of those WRRFs are given in

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Table S3. The diverse effluent water characteristics are displayed in Figure S1 and discussed in

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SI-Text 2.

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Six effluent points have been sampled twice except for the Waregem plant (only once before and

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after a tertiary sand filtration step) resulting in a total of 10 different effluents. Ozonation

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experiments were identical to those for model development although effluent was spiked with a

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stock solution of pCBA (100 µg L-1) as HO• probe compound (kO3 < 0.1 M-1 s-1, kHO• = 5.0×109

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M-1 s-1).36 It could be assumed that, if pCBA was removed to a certain level, other more ozone

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susceptible TrOCs would at least be removed to the same extent. Six different ozone doses,

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ranging between 0 and 15 mg O3 L-1, were added to each effluent.

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Analytical methods

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TrOCs were analyzed using a validated SPE-UHPLC-HRMS method, making use of a benchtop

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Q-ExactiveTM Orbitrap mass spectrometer (Thermo-Fisher Scientific, USA). Details about the

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analytical procedure and validation characteristics are provided by Vergeynst et al.37 and SI-Text

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1. Alkalinity (mg L-1 CaCO3) was determined according to standard methods.38 Nitrite (NO2--N),

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nitrate (NO3--N), ammonium (NH4+-N) and COD were determined spectrophotometrically

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following the standard methods38 using Hach-Lange cuvettes and a DR2800 spectrophotometer

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(Hach, Belgium). Conductivity (EC) and pH were registered by a HQ30D (Hach-Lange,


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Belgium). Turbidity was measured with a portable Hi 98703 (Hanna Instruments, USA). The

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instantaneous ozone demand (IOD), defining the rapid ozone consumption within 5 seconds after

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dosing, was determined based on Hoigné & Bader39 and Roustan et al.40 More information is

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given in SI-Text 3. Ozone concentrations in the stock solution were measured based on the

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indigo method of Bader & Hoigné.41 HO• exposure was determined (see SI-Text 4) using either

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pCBA (during effluent characterization) or metronidazole (during model development) as probe

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components due to their low kO3 (pCBA: < 0.1 M-1 s-1; metronidazole: < 1 M-1 s-1)34,36 and their

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high kHO• (pCBA: 5.0×109 M-1 s-1; metronidazole: = 6.2×109 M-1 s-1)34,36. HPLC-DAD was used

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to measure pCBA concentrations (see SI-Text 4).

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UV-Visible (UV-VIS) absorption spectra between 200 and 800 nm with 0.5 nm increments were

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obtained using a Shimadzu UV-1601 spectrophotometer and 1cm quartz cuvettes. Fluorescence

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EEMs were obtained using 1 cm quartz-cuvettes and a Shimadzu RF-5301 Fluorimeter. No

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dilution was performed as UVA254 < 0.3 cm-1 for all samples.31,42,43 Fluorescence intensities were

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measured at excitation wavelengths of 220-450 nm in 5 nm increments, and emission

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wavelengths of 280-600 nm in 1 nm increments. Excitation and emission slit widths were set at 5

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nm, and a response time of 0.25 s was utilized. Raman scans of distilled water were obtained at

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an excitation wavelength of 350 nm over an emission wavelength range of 365-450 nm in 0.2 nm

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increments, for the calculation of the Raman peak. The area of the Raman peak was used to

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normalize the fluorescence intensity of all spectra, finally expressed as RU (Raman units).44,45

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PARAFAC analysis

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Fluorescence spectral corrections and PARAFAC analysis were performed by adapting the

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drEEM® toolbox in Matlab.44 Correction of fluorescence EEMs was undertaken to minimize

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bias due to sample and instrumental related variability. Raw spectral data were blank corrected

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and sample specific matrices of correction factors for inner filter effects were applied, calculated

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based on UV absorbance spectra. Normalization to RU was performed afterwards and Rayleigh-

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Tyndell and Raman scatter lines were removed for quantitative analysis and to allow EEMs to be

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displayed uniformly. All samples for model development were used for subsequent PARAFAC

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analysis and for integration to determine the total fluorescence (TF) of each sample.

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Fluorescence intensities below an excitation wavelength of 225 nm and an emission wavelength

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of 281 nm were excluded from all EEMs for PARAFAC analysis based on leverage plots.

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Outlier samples were excluded based on outlier tests (i.e. examining the structure in the error

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residuals) and leverage plots. A non-negativity constraint and random initialization were applied

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for the models. A model with four components could be identified (Figure S2). All components

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could be associated with different types of chemical groups with specific properties according to

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Chen et al.30 Split-half analysis was performed for validation of this model using following

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settings: four alternating determined splits, three runs per model of two combined splits and a 10-

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10

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‘overall result’ of the drEEM toolbox. Further details comparing the different developed models

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(i.e. loadings) during split-half validation are given in Figure S3-6. Intensity values at peak

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wavelengths of the defined PARAFAC components are expressed as Fmax1, 2, 3 and 4 (in RU).

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The development requires significant computational power and is time consuming, making it

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impossible to perform online for control applications. After successfully constructing the

convergence criteria. The model was ‘validated for all comparisons’ as stated by the given


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PARAFAC model, this model can be applied to other measurements without any additional

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model construction or development necessary (as is done within this research).

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Component one (Ex. 235 & 310 nm; Em. 385 nm) and two (Ex. 235 & 380 nm; Em. 487 nm)

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both represent fulvic acid-like moieties based on prior studies defining the region of intensities

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related to specific chemical moieties.30 Component two is expected to consist of hydrophobic

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compounds whereas component one is probably less hydrophobic with a smaller molecular size.

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Ishii & Boyer 32 also showed, indicated by the shorter excitation and emission peak wavelengths,

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a positive association between compounds in the former region and a smaller molecular size.

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Component three (Ex. 260 & 350 nm; Em. 440 nm) is affiliated with humic acid-like moieties,

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hydrophobic in nature, and a rather large molecular size (indicated by higher peak wavelengths).

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Tryptophan and protein-like compounds with a rather small molecular size exhibit fluorescence

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intensity in the region of component four (Ex. 225 & 280 nm; Em. 345 nm).30 More information

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on the location of the defined fluorescence components is given in Table S4. Although the main

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goal of this manuscript was to develop a reliable framework of correlation models rather than to

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define specific regions in EEM spectra, these peak allocations provided supporting information

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for data interpretation.

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Model validation

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The correspondence between measured and predicted data was evaluated using (i) an unpaired t-

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test of which the null-hypothesis (difference is not significant) was rejected by p < 0.05 and

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which could also be used to compare different models with equal sample size, (ii) the Theil’s

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inequality coefficient (TIC) of which a value below 0.3 is commonly seen as an indicator for a


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good agreement (see SI-Text 5)46 and (iii) a visual inspection to detect outliers or regions with

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deviations supported with the determination of the MAE (mean averaged error). The predictive

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power of the newly developed model was compared to single correlation models (1) developed

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with the same data as for the new developed model and (2) earlier published models17,19,20,22

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specifically developed for the prediction of pCBA removal based on ∆UVA254. The accuracy of

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the new model in comparison with single correlation models was best exemplified when using

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UVA254 as model input since EEM deduced PARAFAC components are not as widely used in

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literature for describing correlations with ∆TrOCs. Several authors mention correlations for

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selected TrOCs while using UVA254, but the number of TrOCs in these studies was limited and

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the selection of TrOCs was different. The correlation of ∆pCBA with ∆UVA254, on the contrary,

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is mentioned by multiple authors and forms a good basis for comparison. The equations and

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slopes to establish the different single correlation models are given in SI-Text 6 and Table S5-S6.

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RESULTS AND DISCUSSION

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Surrogate correlation patterns of TrOCs

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The abatement pattern of each TrOC was related to changes in UVA254, TF and Fmax1-4. Figure 1

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shows the percentage of elimination of each TrOC in relation with the decrease of UVA254 (a)

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and Fmax1 (b), respectively. The TrOC abatement in relation with the decrease of TF and other

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PARAFAC components (Fmax2, 3 and 4) is given in Figure S7-S10. Group I compounds

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(diclofenac, levofloxacin and trimethoprim) with the highest kO3 showed complete removal at

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25% ∆UVA254, 52% ∆TF, 54% ∆Fmax1, 37% ∆Fmax2, 61-65% ∆Fmax3 and 51% ∆Fmax4. Group II

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compounds (amitriptyline, ciprofloxacin and venlafaxine) only showed complete removal


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starting from 29-33% ∆UVA254, 56-66% ∆TF, 60-70% ∆Fmax1, 42-54% ∆Fmax2, 68-73% ∆Fmax3

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and 57-70% ∆Fmax4. Amantadine, flumequine and metronidazole (Group III) were not

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completely removed at the observed maximal ∆UVA254 (47%), ∆TF (81%), ∆Fmax1 (84%),

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∆Fmax2 (70%), ∆Fmax3 (83%) and ∆Fmax4 (90%). It should, however, be stated that the data

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related with Fmax4 were highly scattered (hence its 4th rank during PARAFAC analysis) which

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makes it hard to draw strong conclusions from this surrogate parameter. Nevertheless, other Fmax

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parameters showed clear abatement patterns with only limited scattering.

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TrOC abatement related to ∆UVA254

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A linear relationship, as assumed by most authors19–26, was not able to describe in a

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representative way the observed trend over the full ∆UVA254 and ∆TrOC range in Figure 1a,

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especially for the group III compounds. The shape of the curves is more complex and can be

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related to the main occurring reaction mechanisms. At low ozone doses, and corresponding low

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∆UVA254, removal of pollutants and EfOM is mainly due to direct ozone reactions. This is

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supported by the relatively high values for rapid ozone consumption, often referred to as

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“instantaneous” ozone demand, (7.7 and 10 mg O3 L-1, considering dilution during

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experimentation and nitrite correction). Systematically increasing the ozone dose (and

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consequently higher ∆UVA254) results in a significant increased removal of the pharmaceuticals

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within group III, while the removal of group I compounds slightly levelled off as function of

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∆UVA254. Overall, two main hypotheses are put forward to explain the observed phenomena: (i)

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transformation of the organic matrix leads to products with higher HO• production yields, as

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proposed by Audenaert et al.19 and Lee et al.10, and/or (ii) after an initial reaction phase with fast


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direct ozone reactions, the residual dissolved ozone can form HO• via autocatalytic

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decomposition.16 It is clear that HO• production relies on a complex mechanism that is

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significantly affected by oxidant induced changes of EfOM.29 During the initial phase, very high

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transient HO• concentrations have been observed, playing an important role during oxidation.29

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For example, HO• exposure of all effluents, measured during IOD determination, amounted on

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average (1.37 ± 0.24)×10-10 M.s, i.e. 56% of the maximum obtained HO• exposure during all

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experiments (see further, Figure 3). The degree of hydroxylation when ozonation proceeds might

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increase and new electron rich moieties (e.g. phenolic compounds) may be formed which

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accelerate HO• production.19,29,47 Prolonged ozonation results in dissolved ozone residual, and

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the process can be assumed to be further controlled radical chain reactions and less by direct

288

reactions with EfOM moieties. This in contrast with the initial phase in which chain reactions or

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autocatalytic decomposition seem not to play a key role.48 Such radical chain reactions are

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extensively described elsewhere.16,49

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Based on the reactions discussed above, the entire range of ∆UVA254 can be divided into two

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distinct regions (Figure 1a). In phase 1, rapid ozone reactions dominate whereas in phase 2,

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indirect (less selective) HO• radicals dominate the reaction pathways. For both phases and for

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each TrOC, a distinct linear relationship could be obtained between ∆TrOC and ∆UVA254. No

295

intercept was considered for the first part as it was not statistically significant (p > 0.05 for all

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nine TrOCs separately). The location of the inflection point (19% ∆UVA254) was determined

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minimizing the standard error between the predicted values (based on the two part linear

298

function) and the experimental data. A more detailed approach compared to most already

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published studies (e.g.20–23) was used, as up to 20 data points/measurements were taken during

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each experiment.


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Relating the slope of phase 1 (ap1, obtained from Figure 1a) to kO3 showed a logarithmic relation

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with a good fit (R² = 0.77, n = 7, Figure 2a). Therefore, a linear regression was performed

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according to eq. 1. Logically, ozone-based degradation of TrOCs in a near neutral pH

304

environment strongly depends on kO3.10 Zimmerman et al.12 already indicated that mostly a

305

correlation between the removal of the component and the kO3 occurs. Lee et al.34 observed

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similar TrOC abatement in different WRRF effluents when normalizing the ozone dose to the

307

initial DOC. The relationship as presented in Figure 2a (and eq. 1) can be generically used to

308

construct correlations for every TrOC for which kO3 is known.

309

The correlation in phase 2 of Figure 1a shows no dependency on the kO3 (Figure S11a). In

310

addition, no statistical difference was noticed between the average slopes of each TrOC group,

311

with an overall averaged slope of 2.54 ± 0.60. This supports the hypothesis that, independent of

312

the affinity of each TrOC towards ozone, unselective reactions with HO• play a more prominent

313

role in phase 2. In contrary to the kO3 values of the nine TrOCs which span several orders of

314

magnitude, the kHO has a much smaller variation (see Table S1). Consequently, it is difficult to

315

make a strong correlation between the kHO and slope in phase 2.

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The given approach is somewhat in line with recent available literature also including a two

317

phase relation between ∆TrOCs and ∆UVA254 or ∆TF. Park et al.50 used a kinetic approach by

318

including the Rct constant and empirically determined parameters, attributing the contribution of

319

ozone and HO during two reaction phases. Also Nanaboina and Korshin24 used a kinetic

320

approach including empirically determined parameters (i.e. water or TrOC specific) to correlate

321

∆TrOCs and ∆UVA254. However, the currently considered fixed empirical parameters are

322

depending on a varying water quality (or are TrOC specific). Therefore some additional offline

323

work (i.e. plant or effluent specific empirical parameters such as e.g. Rct) still needs to be done


15


Page 16 of 44

324

before applying the model in a full-scale installation. For online control, the empirical correlation

325

model of the current work seems advantageous at the current timing as no additional offline

326

work is necessary once the model is established, although both types of models (requiring

327

effluent dependent offline work or not) are of value for full-scale applications.

328

The above observations taken into account, the ∆TrOC can be predicted using eq. 2 (∆S ≤ I) and

329

eq. 3 (∆S ≥ I), with ∆S the decrease of the surrogate measurement (e.g. ∆UVA254) and I the

330

inflection point between both correlations. This prediction allows for online monitoring if

331

combined with eq. 1. Non-continuous correlations were used for curve fitting to handle both

332

phases separately and to be able to interpret the relations during each phase independently.

333

Correlations for the TrOCs (excluding amantadine) showed minor changes (< 7% for ∆UVA254)

334

near the intersection or inflection point, resulting in almost equality of both curves for most

335

TrOCs at the inflection point. The deviation of amantadine had to deal with a lot of scattering,

336

especially in the low ∆UVA254 region. This component was consequently not used further for the

337

regression between the slope of phase 1 and kO3.

338

The uncertainty on both ap1 and kO3 has been experimentally determined during previous curve

339

fitting (ap1) or has been based on a conventional order of error that is often seen for reaction rate

340

constant (i.e. 50% for kO3). These uncertainties might explain the outlier value of venlafaxine

341

(Figure 2a). Noteworthy is that kO3 values (at pH 7) should be used with caution and

342

uncertainties thereon should always be considered as actual values can be strongly influenced by

343

experimental errors or by even slight changes in conditions (such as pH).15 Also the presence of

344

large molecular structures, e.g. humic acid-like moieties, having low dissociation rates can affect

345

species speciation having pKa values close to near neutral pH (i.e. the natural effluent pH). These

346

uncertainties have been taken into account for the determination of both m and b (and the error


16

Page 17 of 44


347

thereon) in the linear regression (eq. 1) by using the technique proposed by York et al.51

348

Applying Sobol sampling52 with 10.000 samples, the uncertainty on the model predictions was

349

determined. The model parameters included in the analysis and their uncertainty are also listed in

350

Table S7. Finally, the 95% confidence intervals for ∆UVA254, ∆TF and ∆Fmax1-3 are given in

351

Figures S13-S17.

352 353

ap1 = m×ln(kO3) + b

eq. 1

354

∆TrOC0→inflection = ap1×∆S

eq. 2

355

∆TrOCinflection→… = ap1×I + ap2×( ∆S – I )

eq. 3

356 357


17


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358 359

Figure 1. Abatement pattern of TrOCs in relation to ∆UVA254 (a) and ∆Fmax1 (b), divided in two

360

phases related to the main occurring mechanisms (dotted line indicates the inflection point).


18

Page 19 of 44


361

362 363

Figure 2. Relationship between the TrOC kO3 (see Table S1) and the slope of the first phase for

364

(a) ∆TrOC vs ∆UVA254, and (b) ∆TrOC vs ∆Fmax1. Data points are given from left to right for

365

metronidazole, amitriptyline, venlafaxine, ciprofloxacin, levofloxacin, trimethoprim and

366

diclofenac. The error on ap1 and kO3 is shown by the circles around the data points.

367 368

TrOC abatement related to fluorescence based surrogates

369

The relationship between ∆TrOC and both ∆TF and ∆Fmax1-3 could be described in a similar

370

way as for ∆UVA254. Surrogates including the largest share of the EEMs, and therefore

371

containing the most spectral information (i.e. representing absorbance or intensities for a broader

372

range of chemical moieties) showed a higher maximal removal (∆TF = 81%, ∆Fmax1 = 84%,

373

∆Fmax2 = 70% and ∆Fmax3 = 83%; Figure 1 and S7-S10) than UVA254 (47%). Nevertheless, it

374

was clear that each fluorescent component exhibited a different behavior. A higher decrease of


19


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375

fluorescence intensity was observed before reaching the inflection point, compared to UVA254.

376

Inflection of the curves was only established at 55% ∆Fmax3 and at 47% ∆TF, 44% ∆Fmax1 and

377

26% ∆Fmax2. Fmax4 was not further considered as no significant correlations were found, most

378

likely due to the limited amount of effluent samples used for building the correlations and the

379

high variability (i.e. high degree of scattering) in Fmax4 data.

380

The relationship between the slope of the linear correlations in phase 1 (through the origin) and

381

the kO3 showed a logarithmic behavior with a good match (R² = 0.75 - 0.76, n = 7, Figure 2b and

382

S10a,c,e). Also here, the slope of the correlation in phase 2 showed no dependency on the kO3

383

(Figure S11b and S12b,d,f) and with similar slopes for each TrOC group (Table S10). Hence, for

384

fluorescence similar equations as for UVA254 (eq. 1-3) can be used.

385 386

Applicability of the correlation models

387

The models for both UVA254 and fluorescence were applied on an independent set of data

388

(effluent 3) and were able to describe ∆TrOC with good accuracy. This is confirmed by Theil’s

389

inequality coefficient (TIC)46 with all values below 0.3. Some variation was noticed on the

390

individual data although mostly near the 95% confidence interval of the model (Figure S13-S17),

391

especially using fluorescence measurements (TF and Fmax1-3), being a potential indication of

392

higher robustness of fluorescence models. Effluent 3 had a higher turbidity than effluents 1 and 2

393

which might have affected the ozonation process and/or the spectroscopic measurements (i.e. the

394

increased degree of scattering for effluent 3). Zucker et al.53 and Zimmerman et al. 12 already

395

pointed towards the presence of solids as potential reasons for over- or under-prediction.


20

Page 21 of 44


396

Although not investigated, the model compounds had different log Kow values (Table S1)

397

potentially causing different sorption behavior.53

398

The different plots in Figure 1, S7-S9 and S13-S17 also indicate that a certain stable minimum

399

level of UVA254 or fluorescence intensity exists. Especially group III components were still

400

significantly reducing while further surrogate reduction was very limited. Comparable findings

401

by Nöthe et al.47 indicated that a maximal decrease of UVA254 and fluorescence should be

402

considered. In this context, van der Helm54 hypothesized a stable background UVA254 level in

403

drinking water ozonation. Potentially, the first stage of oxidation results in a significant reduction

404

of absorbance or fluorescence related to the breakdown of complex organic matter and

405

simultaneous reduction of aromaticity, as also observed during chlorination and ozonation of

406

natural organic matter.36,55,56 The EfOM reactions could also generate new components (e.g.

407

aldehydes or ketones) that continue to absorb.

408 409

HO• exposure

410

Measuring HO• exposure requires measurements of probe compounds such as pCBA.10 The HO•

411

exposure determined in three different effluents (used for model development) at various initial

412

ozone concentrations was plotted in Figure 3a and shows a maximum of 2.4×10-10 M.s. The HO•

413

exposure increased exponentially (R² = 0.75, n = 48) with an increasing ozone dose. The little

414

amount of HO• formed at low ozone doses was presumably due to the ozone consumption by

415

nitrite (between 0.2 and 0.7 mg O3 L-1, Table S2; associated with maximum 0.1 g O3 g-1 DOC),

416

and by direct ozonation of EfOM. Although Lee et al.10 described a linear relation between the

417

specific ozone dose (up to 1.75 g O3 g-1 DOC) and HO• exposure, they also determined a


21


Page 22 of 44

418

threshold value (varying between 0.06 – 0.24 g O3 g-1 DOC) below which no or little radical

419

production was noticed. The non-linear relationship between HO• exposure and specific ozone

420

dose as observed in Figure 3a was likely noticed due to the availability of data at very low ozone

421

doses (i.e. higher resolution) and the high IOD of the effluent samples. The abatement profile

422

group III TrOCs clearly shows similarities with the regression in Figure 3a. Similar to this

423

research, also Nöthe et al.47 observed only a significant removal of such components beyond ±

424

0.5 g O3 g-1 DOC.

425

The determined HO• exposures were exponentially correlated to the surrogate measurements (n

426

= 48) with good agreements for ∆UVA254 (R² = 0.82), ∆TF (R² = 0.89), ∆Fmax1 (R² = 0.90),

427

∆Fmax2 (R² = 0.86) and ∆Fmax3 (R² = 0.90). The correlations presented in Figure 3b-f show high

428

potential for online estimation of HO exposure. Although the curves were similar, ∆Fmax3

429

exhibited a more inflected curve with steeper incline going beyond exponential behavior (grey

430

shaded in Figure 3f). To quantify these differences, the reductions in surrogate signals

431

corresponding to HO• exposures of 25, 50, 75 and 100% of the maximal value was plotted in

432

Figure S18Error! Reference source not found.. It is clear that a HO• exposure increasing

433

above 25% of its maximum value has only limited impact on ∆Fmax3, ∆Fmax1 and – to a slightly

434

lesser extent – ∆TF, compared to ∆UVA254 and ∆Fmax2. A maximum additional decrease of 49

435

and 48% was noticed for respectively ∆UVA254 and ∆Fmax2, while this amounted to only 32 and

436

27% for ∆Fmax1 and ∆Fmax3. This exemplifies the difference in behavior of the different

437

surrogate variables. While UVA254 and Fmax2 seem to be associated with both direct and indirect

438

ozone reactions, Fmax1 and Fmax3 are most likely dominated by direct ozone reactions. A more in-

439

depth kinetic study is recommended to further elucidate these findings. Nevertheless, support can

440

be found also in considering the properties of the different chemical groups showing intensities


22

Page 23 of 44


441

within the Fmax1 and Fmax3 spectral regions. Both are described as fulvic and humic acid-like

442

matter, respectively, prone to molecular weight changes57 and probably with a high degree of

443

aromaticity.42 Swietlik et al.57 also concluded that especially hydrophobic acids consisting of C5-

444

C9 aliphatic carboxylic acids and humic acids, characterized with a high degree of aromaticity,

445

showed high ozone reactivity. Although UVA254 is known to be related to aromaticity, it is also

446

known that less aromatic (and less reactive) moieties absorb at this wavelength as well.54

447

Organic matter associated with Fmax2 (fulvic acid-like) indicates to be less reactive towards

448

ozone, and is therefore assumed to contain a lower degree of aromaticity.

449


23


Page 24 of 44

450 451

Figure 3. HO• exposure determined in three different effluent samples, correlated exponentially

452

with the initial O3:DOCeq dose (a), ∆UVA254 (b), ∆TF (c) and ∆Fmax1-3 (d-f). (dotted line

453

indicates the 25% level of the maximally obtained HO• exposure)

454


24

Page 25 of 44


455

Model validation using effluents of different plants

456

Predictability of the inflected correlation model

457

The newly developed and calibrated correlation model for UVA254, Fmax1-3 and TF adequately

458

predicted pCBA abatement in WRRF effluents. Model predictions are shown in Figure 4a and b

459

for ∆UVA254 and ∆Fmax1, respectively. Considering the total data set (n = 60), the good

460

performance of the model by using ∆UVA254 was exemplified by both a t-test (p-value of 0.41)

461

and a TIC value of 0.13. Good results were also obtained when using ∆Fmax1 as the surrogate (p

462

= 0.44; TIC = 0.14). Visually, a slightly better prediction was observed when using ∆Fmax1

463

instead of ∆UVA254, which was confirmed by the MAE being 8.3% (∆Fmax1) and 9.2%

464

(∆UVA254), respectively. The number of data points outside the model’s 95% confidence interval

465

was especially lower in the ∆pCBA range above 50% (4 compared to 9 on a total of 21). This

466

was also reflected in the slightly higher p-value of the t-test. Other surrogates such as ∆Fmax2-3

467

or ∆TF were not able to further increase the predictive power (i.e. enhance the results of the t-

468

test, TIC or MAE). For example, the TIC- and MAE-values obtained with ∆Fmax2 (0.31) and

469

∆Fmax3 (0.52) (slightly) exceeded the threshold value, while that of ∆TF (0.28) was just below.

470

Also the MAE (15.8-29.2%) was clearly higher, indicating an overall lower agreement in

471

comparison to ∆UVA254 and ∆Fmax1. This is also graphically clear from Figure S19.

472

All p- and TIC-values are summarized in Table S6, making a differentiation among the different

473

sampling locations. The model performance was the weakest for the WRRF effluent of

474

Waregem. ∆Fmax2 – although its overall TIC of 0.31 – showed to be a good predictor for all

475

effluents except for that of Waregem (p = 0.064; TIC = 0.75). That plant treated a significant

476

amount of wastewater originating from the textile industry. The dyes consisting of aromatic,

477

heterocyclic and nitrogen containing moieties such as azobenzene or triazines typically show a


25


Page 26 of 44

478

low reactivity towards ozone, while affecting the spectral signals significantly.36,58,59 ∆Fmax3 and

479

∆TF did not provide a sufficiently high predictive power for the effluents of Waregem and

480

Kruiseke for those models to be validated.

481

The highest model robustness is thus obtained with ∆UVA254, ∆Fmax1 and ∆Fmax2. This is logical

482

for Fmax1 and 2 since these components contain most of the spectral information of the EEMs (1st

483

and 2nd rank). The presence (i.e. signal strength) of Fmax1 and also Fmax4 (less ozone reactive

484

moieties) was clearly higher than that of Fmax2 or 3 (Figure S20) resulting in a better

485

correspondence after the point of inflection. UVA254 and Fmax2 were previously indicated to be

486

affiliated with both direct and indirect ozone reactions, giving a representation of all ongoing

487

reactions.

488


26

Page 27 of 44


489 490

Figure 4. Abatement patterns of predicted (full lines) versus measured (dots) ∆pCBA in relation

491

to ∆UVA254 or ∆Fmax1 by applying the inflected model (a and b), a single correlation model

492

based on own data (c and d), and the single correlation models reported by Audenaert et al.19 (e)

493

and Sharif et al.17 (f). The shaded grey areas indicate the 95% confidence interval of the models.

494

The abbreviations of the WRRF are explained in Table S1.


27


Page 28 of 44

495 496

Comparison of the inflected and single correlation models

497

A single correlation model for ∆UVA254 constructed with the same data as used for the

498

development of the inflected model showed a lower predictive power, based on the t-test (p =

499

0.17 versus 0.41), and to a lesser extent based on TIC-value (0.14 versus 0.13) and MAE (9.8%

500

versus 9.2%). Additionally, applying four previously reported correlations17,19,20,22 for ∆pCBA to

501

our experimental data shows TIC-values between 0.11-0.22, thus all below 0.3. The t-test

502

indicates a good predictive power for the correlation from Audenaert et al.19 (p-value of 0.36),

503

Gerrity et al.22 (p-value of 0.56) and Sharif et al.17 (p-value of 0.91), but not for that from Wert et

504

al.20 (p-value of 0.006). In accordance, the MAE was consistently in line with those observed for

505

the inflected model (7.6-9.2%) with the exception of that from Wert et al.20 (14.0%). When

506

looking at the data graphically (Figure 4c,e,f and S21), it becomes clear that that ∆UVA254 based

507

single correlations do not optimally describe the data trend in certain ∆UVA254 regions. The

508

models by Audenaert et al.19 (Figure 4e) and Gerrity et al.22 (Figure S21b) are showing a slight

509

under-prediction at high ∆UVA254 (> 30%), similar to the own single correlation model (Figure

510

4c). This is also supported by the MAE being higher (between 12.3-14.1%) compared to the

511

inflected model (11.4%) at these high ∆UVA254. The model of Sharif et al.17 (Figure 4f) resulted

512

in a slightly lower MAE above 30% ∆UVA254 (10.3%). Additionally, the model by Gerrity et

513

al.22 showed an over-prediction of ∆pCBA for low ∆UVA254. Although MAE values were

514

relatively close to each other, it is visually noticed that all single correlation models are not

515

following the trend of the data, i.e. situating the measured data more below the model at low

516

∆UVA254 and above at high ∆UVA254. The model by Wert et al.20 (Figure S21a) showed an


28

Page 29 of 44


517

under-prediction for the full range of ∆UVA254. Such outcomes might lead to additional

518

operational costs or by-product formation due to overdosing of ozone.

519

For the single correlation model of Fmax1 in Figure 4d, the under-prediction is even more

520

pronounced at higher ∆pCBA values (MAE = 11.0% compared to 6.8% if ∆Fmax1 > 50%). This

521

is the important range to reduce pCBA levels (or other TrOCs that react slowly with O3). The

522

newly developed inflected model (Figure 3b) was superior compared to that model. Whereas

523

85% of all samples (n = 60) were within a maximum absolute deviation of 20% for the single

524

model (MAE = 9.1%), this increased up to 92% for the inflected model (MAE = 8.3%). The

525

better performance of the latter was also exemplified by the p-value (0.44 versus 0.19). More

526

details concerning the different correlation models and their predictive power are given in Table

527

S8-S10.

528 529

Considerations for full-scale applications

530

The correlation models will determine the zones for ozone dose control in practice. The inflected

531

correlation models have shown to predict ∆TrOC in an adequate manner, solely based on

532

reported kO3 values. This is an important feature given the large amount of TrOCs potentially

533

present in effluent. The kO3 of the targeted TrOCs can be determined experimentally, based on

534

literature or by QSAR, reducing the need for extensive experimentation.60,61 The exact value of

535

kO3 might vary based on the method of determination but the established model allows a

536

straightforward prediction of the abatement pattern within the 95% confidence interval.

537

Both UVA254 or fluorescence surrogates seem adequate candidates. Real-time measurement of

538

∆UVA254 has already been established and sensors have been applied extensively. Stable and

539

reliable fluorescence sensors are still under development, although commercial sensors are


29


Page 30 of 44

540

starting to become available. Therefore, the use of UVA254 might be preferable at the current

541

timing for online ∆TrOC monitoring. Nevertheless, the more information rich EEMs might pose

542

advantages compared to the less selective measurement of UVA254 or TF. PARAFAC analysis

543

showed large potential with regard to enhancement of process understanding.

544 545

ASSOCIATED CONTENT

546

Supporting Information.

547

SI-Texts 1-6, Tables S1-S10 and Figures S1-S21 provide further information addressing

548

experimental procedures, data and discussion on TrOC analyses, determination of IOD and HO•

549

exposure, abatement profiles of TrOCs in relation to UVA254 and fluorescence measurements,

550

and statistical evidence supporting statements on the model validity. This information is

551

available free of charge via the Internet at https://pubs.acs.org

552 553

AUTHOR INFORMATION

554

Corresponding Author

555

*E-mail: [email protected], Phone: 00 32 56 24 12 06, Fax: 00 32 56 24 12 24

556

Notes

557

The authors declare no competing financial interest.

558

ACKNOWLEDGEMENTS


30

Page 31 of 44


559

The financial support (AUGE/11/016) from the Hercules Foundation of the Flemish Government

560

is acknowledge for the UHPLC-Q-ExactiveTM mass spectrometry equipment. Ghent University

561

is acknowledged for the PhD grant of Michael Chys and The Special Research Fund (Ghent

562

University)for funding the automated SPE equipment (01B07512). The authors further like to

563

thank the staff of Aquafin NV (Belgium) for their help during sampling. This project was

564

initiated within the LED H2O project which belongs to the LED network (www.lednetwerk.be)

565

and is financially supported by the Flemish Knowledge Center Water (Vlakwa vzw).

566 567

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