Elsevier

Microchemical Journal

Volume 171, December 2021, 106799
Microchemical Journal

Analytical control in advanced oxidation processes: Surrogate models and indicators vs traditional methods

https://doi.org/10.1016/j.microc.2021.106799Get rights and content

Highlights

  • Advanced oxidation processes need a careful analytical control.

  • Performance and limits of different analytical approaches have been compared.

  • Chemometric tools improve information provided by UV and fluorescence spectroscopy.

  • Surrogate methods are suitable for real-time, inline process monitoring.

  • Traditional and surrogate methods are complementary for in depth AOP control.

Abstract

Advanced oxidation processes (AOPs) have been studied since many years for the removal of organic bio recalcitrant molecules and proposed as tertiary treatment for effluent waters.

Mostly ozonation and UV-H2O2 have found application in water treatment utilities plant.

It is highly relevant establish reliable measurement for real-time monitoring of the AOPs course in order to allowing fast intervention in case of malfunctioning. Sophisticated and powerful analytical techniques based on mass spectrometry do not fulfil requirements for being used inline. Surrogate-based models have been therefore considered for their suitability, together with the use of carefully selected indicator molecules for the creation of correlation models representative of groups of pollutants.

The most relevant features of the above mentioned approaches are discussed evidencing their strength and weakness as well as their complementary role when optimizing AOP, at laboratory scale for an in depth system characterization, and at plant scale for the process monitoring during its application at real cases.

Introduction

To face the challenge of a more safe and efficient management of water resources, Advanced Oxidation Processes (AOPs) have been proposed so far for industrial and drinking water treatments, integrating biological processes for the removal of toxic, not biodegradable and recalcitrant organic pollutants [1], [2], [3], [4], [5], e.g. dyes, pesticides, pharmaceuticals, flame retardants, phenols and derivatives. AOPs include photocatalysis, (photo) Fenton-like processes, ozonation, electrochemical processes UV-H2O2, the use of ultrasound and wet oxidation processes. Mostly ozonation and UV-H2O2 have already found application in water treatment utilities plant. As whole, AOPs can be considered a family of technologies having in common the production of highly reactive species mainly, but not exclusively, hydroxyl radicals [6] able to initiate the oxidation of organic substrates; in the presence of oxygen the process can proceed until the complete substrate mineralization to CO2 and water is attained.

Before mineralization organic substrates undergo different reaction, e.g. hydroxylation, H+ abstraction, OH/O2 addition to double bonds, yielding the formation of intermediate degradation products, usually showing a progressive increasing in their hydrophilicity, together with ring opening in the case of aromatic substrates. Under a kinetic point of view, the generation and further transformation of intermediates, in the function of treatment time, results in overlapping profiles.

Beside the engineering aspects, the development of suitable reagents and the in depth comprehension of reaction mechanisms, the attentive analytical control of the process is of fundamental relevance in order to follow the abatement of target molecules and exclude the formation and persistence of toxic transformation products. This task is made more complicate by the unknown identity of intermediates that correspond therefore to uncalibrated compounds, often featuring overlapping analytical signals.

Among the “traditional” analytical methodologies adopted to study AOPs at laboratory scale, mass spectrometry coupled with LC and GC is a sensitive and versatile technique suitable not only to detect pollutants at very low concentration but also to provide the identification of their transformation products. The outcomes of these techniques allow the assessment of the degradation path(s) through identification of intermediates formed during the treatment. They are of fundamental relevance when the AOP is studied at laboratory scale, in order to investigate the effect of operational parameters on process efficiency and mechanism.

Scaling up from laboratory dimension to real cases, to treat polluted water for water reuse, the monitoring step becomes more structured and complex, as additional requirements have to be satisfied. One of the main challenges is the development of analytical techniques able to give fast response and to provide reliable data, allowing fast intervention on the process, to guarantee continuous efficacy in spite of variability of water composition entering the treatment plant. Moreover, it is highly desirable the use of instrumentation neither expensive nor complex to be managed and maintained.

Under this perspective, classic analytical approach does not provide response sufficiently fast and easy to be interpreted and immediately transformed in valuable indication(s).

An additional analytical issue is represented by the presence in treated waters of high number of compounds at very low concentration levels (Trace Organic Chemicals, TOrCs), whose nature is even unknown in some cases and whose identification and quantification are hindered by the very low concentration and require a huge effort in terms of analytical instrumentation and skills. Despite the capability of advanced analytical techniques of simultaneously detect several dozens of contaminants [7], more than 80,000 individual chemicals are estimated to be present in municipal wastewater effluents [8], therefore stimulating researchers to set-up monitoring schemes through rationale process to prioritize chemicals for reclaimed water monitoring programs [8].

On the other hand, water utilities and regulators need monitoring approaches to assess the capability of conventional and advanced treatment systems to remove these different contaminants potentially present. There is therefore the need of defining key parameters, easy to be monitored and reliable with respect to the process efficacy and efficiency.

The exploitation of spectroscopic measurements allows to obtain an overall analytical information that includes the contribution of each detectable compound present in the samples (i.e. the matrix, the target pollutants and related transformation products), without a specific response for a single compound. Recently it has been reviewed the use of analytical strategies based on advanced chemometric models [9] (and references therein) allowing the analysis of samples with a significant UV–VIS absorption profile overlapping and even in the presence of uncalibrated species such as degradation intermediates or by-products. Analogously, the review reports on the handling of excitation-emission fluorescence matrices (EEMs) by means of Parallel Factor Analysis (PARAFAC).

This kind of approach has been exploited in the so-called “surrogate” models, featuring the advantage of obtaining fast response, with reduced cost, by means of relatively simple instrumental apparatus, which can be easily deployed for inline control. Together with surrogate models, “ad hoc” selected compounds can be chosen as “indicators”, to give valuable feedback for the efficiency of the different AOPs applied [10], [11], [12], [13], [14], [15], [16]. A possible direction is indeed using data on a limited subset of readily measurable compounds and general water quality parameters.

The present work aims to provide a discussion about the employment of surrogate models and indicators in comparison with traditional techniques for the analytical control of AOPs in water treatments, from laboratory level up to water treatment plants.

Section snippets

Traditional methods

The advanced LC-MS and GC–MS technologies allow the simultaneous determination of a wide and simultaneous range of multiclass compounds in real complex matrices, such as wastewater, with high performances in terms of specificity and selectively [17], [18], [19], [20], [21].

In particular, the tandem mass spectrometry with Triple Quadrupole (QqQ) or Hybrid Triple Quadrupole-Linear Ion Trap mass spectrometer (QqLIT) have shown to be powerful equipment to provide accurate quantification of target

Surrogate models and indicators

In the literature a clear definition of surrogate and indicator has been given [51], [52]: a surrogate is a quantifiable parameter within bulk water which can serve as a performance measure of treatment processes that relates to the removal of specific contaminants. Surrogate parameters provide a means of assessing water quality characteristics without conducting difficult trace contaminant analysis. Possible example of surrogates can be UV absorbance (UVA) at 254 nm, Total Organic Carbon

Comparison between “traditional” and surrogate methods. A case study

In a recent work, Merel et al [12] compared three analytical approaches for monitoring the efficacy of UV and UV/H2O2 processes applied to treat secondary wastewater effluent. In the first analytical approach UV absorbance and fluorescence were employed as surrogate methods that are suitable to be implemented in online sensors. The obtained results evidenced that UV absorption monitoring was not giving useful information since only a very limited variation of the UV spectrum was observed both

Conclusion

Different analytical approaches can be used to assess the efficiency of AOPs for the removal of organic pollutants from water. Based on the results presented in this work, it can be concluded that there is not the “best” option; rather the choice of the most suitable technique could be represented by the best compromise among user expectation in term of analytical performance (sensitivity and specificity), cost, time, and complexity. Chromatography coupled to mass spectrometry is unequalled

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

University of Torino is kindly acknowledged for financial support to local research (RILO funds).

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