Skip to main content
NCBI home page
ACS AuthorChoice logoLink to ACS AuthorChoice
. 2023 Oct 12;57(47):18410–18419. doi: 10.1021/acs.est.3c04086

Predicting Transformation Products during Aqueous Oxidation Processes: Current State and Outlook

Daisuke Minakata †,*, Urs von Gunten ‡,§,*
PMCID: PMC10691424  PMID: 37824098

Abstract

graphic file with name es3c04086_0006.jpg

Water quality and its impacts on human and ecosystem health presents tremendous global challenges. While oxidative water treatment can solve many of these problems related to hygiene and micropollutants, identifying and predicting transformation products from a large variety of micropollutants induced by dosed chemical oxidants and in situ formed radicals is still a major challenge. To this end, a better understanding of the formed transformation products and their potential toxicity is needed. Currently, no theoretical tools alone can predict oxidatively induced transformation products in aqueous systems. Coupling experimental and theoretical studies has advanced the understanding of reaction kinetics and mechanisms significantly. This perspective article highlights the key progress made concerning experimental and computational approaches to predict transformation products. Knowledge gaps are identified, and the research required to advance the predictive capability is discussed.

Keywords: aqueous oxidation, chemical oxidants, radical species, transformation products, prediction, reaction kinetics, reaction mechanisms, micropollutants

Short abstract

Computational in silico tools and algorithms complement experiments to predict oxidative transformations of micropollutants in water.

Background and Significance of Transformation Products Formation during Oxidation of Micropollutants

In addition to the primary purpose of disinfection, chemical oxidation technologies are currently applied for the abatement of organic compounds (e.g., taste and odor compounds, pesticides, pharmaceuticals, cyanotoxins, etc.) and color in water and wastewater treatment processes.1−4 Often, biologically active sites in target compounds react with chemical oxidants and the degree of inactivation depends on the attack of the oxidants such as ozone (O3), chlorine (HOCl/OCl–), chlorine dioxide (ClO2) and permanganate (MnO4–) on the biologically active sites. However, it has been demonstrated that conventional oxidation technologies cannot abate all micropollutants effectively.5 O3 and ultraviolet (UV) light-based advanced oxidation processes (AOPs), which produce reactive radical species (e.g., hydroxyl radicals, •OH)6,7 in situ, have been applied for the abatement of a broader range of micropollutants.5,8−13 Generally, oxidation processes do not lead to a mineralization of micropollutants9,10,12,14−16 with an ensuing formation of transformation products.5,16 This typically leads to a loss of the biological activity of the target compound,17−22 but in sometimes transformation products can exert adverse health effects or ecotoxicity (e.g., minor formation of halogenated or nitro/nitroso compound from the reaction with halogen or nitrogen-containing radicals).23−34 The reactions of dosed chemical oxidants with water matrix components such as bromide, iodide, and dissolved organic matter (DOM) lead to many different disinfection byproducts, potentially yielding an increased (eco)toxicity and adverse health effects to humans and aquatic biota.5,35−45

In the past two decades, identifying transformation products induced by chemical oxidation has been an active area of research, fueled by development of sensitive high resolution mass spectrometry and protocols for the evaluation of detected features in nontarget analysis.15,16 Considering that more than 350 000 chemicals are currently in commercial production and use,46−48 selective screening the universe of chemicals is vital to prioritize toxicologically important chemicals and transformation products for experimental investigations as shown in the ongoing efforts to link DBPs with the toxicity.49,50 With limited commercial availabilities of authentic standards for transformation products, a theoretical approach is critical to tentatively predict reaction products. Toward predicting oxidative transformation products, this perspective article emphasizes the key progress made in experimental measurement of kinetics and transformation products, and theoretical determination of rate constants and elucidation of reaction mechanisms. Knowledge gaps will be identified, and the strategies to advance predictive tools will be discussed.

Oxidative Transformation of Micropollutants: From Experiments to Prediction

To predict the transformation products, information on both kinetics and reaction mechanisms is needed. Knowing the reactivity of different moieties in micropollutants helps to determine reactive sites and primary attack points by chemical oxidants. Coupling this information with reaction rules for oxidative transformation of different functional groups will yield prediction of transformation products. Kinetic information is also useful to assess the reactivity of transformation products with the selected dosed oxidant and/or radicals and will enable of a prediction of higher generation transformation products. Numerical solutions of ordinary differential equations (ODEs) developed based on the kinetics and mechanisms can predict the evolution of transformation products. However, this endeavor is challenging for radical-type reactions because of the lower selectivity leading to a higher number of reactive sites and the corresponding products. This will be discussed in detail below. Figure 1 summarizes experimental and computational tools available to understand and predict reaction kinetics and mechanisms for oxidative transformation of organic compounds including micropollutants.

Figure 1.

Figure 1

Open in a new tab

Experimental and theoretical tools to understand and predict the oxidative transformation of organic compounds including micropollutants. The following abbreviations are used: LC: liquid chromatography; MS: mass spectrometry; MS/MS: tandem mass spectrometry; GC: gas chromatography; IRMS: isotope ratio mass spectrometry; UHR: ultrahigh-resolution; IC: ion chromatography; SFC: supercritical fluid chromatography; NMR: nuclear magnetic resonance; and QSAR: quantitative structure activity relationship.

Reaction Rate Constants

Experimental Measurements of Second-Order Rate Constants (k)

Kinetics for the reactions of chemical oxidants with micropollutants and water constituents provide important information on the efficiency of oxidation reactions. Second-order rate constants, k, for the reactions of dosed chemical oxidants and in situ formed radicals with a wide variety of organic compounds including micropollutants containing reactive functional groups (e.g., olefins, aromatic compounds, nitrogenous, and sulfurous compounds) have been compiled in databases51−55 or review papers/books.1,2,56−58 Notably, experimental determination of k values needs rigorous control of pH, solution temperature, buffer and substrate concentrations, and the critical experimental details. The k values for common functional groups in micropollutants and DOM are decisive to understand the reactivities of structurally complex compounds. While the k values of dosed chemical oxidants vary by more than 10 orders of magnitude because of their selective nature toward various reactive sites, k values of in situ formed radicals have a much smaller range of a few orders of magnitude (Figure 2). A large number of experimental k values for chemical oxidants has been compiled (chemical oxidants such as ferrate(VI) which are not applied in full-scale are not included). Nevertheless, k values are also needed for newly detected emerging contaminants. Furthermore, there is lack of information on the reactivity of in situ formed halogen- and nitrogen-based radical species which is also linked to the formation of halogen- or nitrogen-containing transformation products. As a compliment to experimental approaches, predictive tools can estimate the k values for structurally diverse organic compounds. As such, k values obtained by different researchers and techniques require critical evaluations for the development of comprehensive tools.

Figure 2.

Figure 2

Open in a new tab

Apparent second-order rate constants, kapp (M–1 s–1) at pH 7, for the reactions of selected functional groups with dosed chemical oxidants and in situ formed radicals.2,53,55−66 Note that the compounds in the different classes are not always the same for all oxidants, which may lead to differences in reactivity ranges.

Theoretical Determination of k Values

Currently, it is not feasible to calculate the k values for structurally diverse organic compounds including micropollutants using first-principles methods.67 For example, using the Eyring’s transition state theory,68 an accuracy of ±0.4 kcal/mol of the free energy of activation is required to calculate the k values within a difference of a factor of 2 compared to experimental values. However, current theoretical approaches are not able to achieve this accuracy due to the uncertainty of the solvation process. To overcome these limitations, various regression models have been developed for correlations including experimental k values (Table 1). Quantitative structure–activity relationships (QSARs) are well-known approaches for correlating physical-chemical and structural properties of entire or fragmented parts of organic compounds with k values.69 The parameters used to correlate with k values can be Hammett- or Taft-type constants (classical),70 or parameters obtained by quantum chemical (QC) computations.33,67,71−77 Because correlations between properties and k values can be nonlinear, machine learning (ML) approaches have been recently applied for such QSARs.78 Assessment of the predictability of such tools is important when combined with mechanistic information to predict transformation products.

Table 1. Prediction of Second-Order Rate Constants by QSARa.

dosed chemicals
  
  method descriptors number and group of compounds (R2 values) ref
O3 classical ∑σ+o,m,p 24 phenols (0.81), 13 dissociated phenols (0.96) (70)
∑σ–o,m,p 14 anilines (0.85)
∑σ+p 50 benzene derivatives (0.93)
∑σ* 48 olefins (0.86) and 54 amines (0.86)
QC EHOMO 35 phenols (0.94), 16 anilines (0.85), 17 alkoxybenzenes (0.87), 4 methoxybenzenes (0.95), (79)
ENBO,LP,N 40 benzene derivatives (0.82), 45 olefins (0.84) 59 aliphatic amines (0.76)
ML MD 484 (0.87 from training and 0.45 from testing) (78)
MF 484 (0.76 from training and 0.46 from testing)
HOCl/OCl– classical ∑σ–o,m,p 22 phenoxides (0.88) (70)
∑σ* 14 primary and secondary amines (0.88), 7 tertiary amines (0.91)
ML MD 188 (0.94 from training and 0.60 from testing) (78)
MF 188 (0.93 from training and 0.43 from testing)
ClO2 classical ∑σ+o,m,p 28 phenols (0.86) (70)
∑σ–o,m,p 28 dissociated phenols (0.95)
ML MD 143 (0.88 from training and 0.49 from testing) (78)
MF 143 (0.97 from training and 0.47 from testing)
in situ formed radicals
  
  method descriptors number and group of compounds ref
•OH classical Eact 434 aliphatic compounds, alkenes, aromatics and S-, N-, P-containing compounds (0.58) (79)
QC ΔGactaq 26 neutral aliphatics and alkenes (0.87) (74,76,77)
15 dissociated halocarboxylic acid (0.68)
14 benzene derivatives (0.81)
ML MF 1089 organics (0.89–0.91 from training and 0.63–0.78 from testing) (80)
SO4•– QC ΔGreactaq 76 aromatic compounds (R2 not available) (81)
ML MD 342 organics (0.88 from training and 0.72 from testing) (78)
MF 342 organics (0.81 from training and 0.62 from testing)
Cl• QC ΔGactaq 20 aliphatic compounds (0.71) (73)
Cl2•– classical ∑σ+o,m,p 15 phenols (0.92), 9 alkoxy benzenes (0.95), 7 anilines (0.88) (60)
Br• classical ∑σ+o,m,p 13 phenols (0.90), 8 anilines (0.83), and 8 alkoxy benzenes (0.93) (61)
∑σ+p 11 aromatic compounds (0.82)
QC ΔGactaq 20 aromatic compounds (0.77) (33)
Br2•– classical ∑σ+o,m,p 16 phenols (0.95), 7 anilines (0.94), and 9 alkoxy benzenes (0.95) 15 phenolates (0.85) (61)
CO3•– classical ∑σ+ 8 phenoxides (0.84), 9 anilines (0.90), 12 phenylurea compounds (0.65) (82)
QC ΔE°aq 22 phenols and phenolates (0.58) and 70 N-containing compounds (0.75) (62)
ΔGactaq 12 aromatic compounds (0.89) and 7 phenolates (0.76) (71)
Open in a new tab
a

Summary of classical, quantum chemical (QC)-, and machine learning (ML)-based QSARs with types of descriptors, organic compounds and R2 values (in parentheses) associated with experimental k values. Note that there are no major QSARs developed for MnO4–. MD and MF denote molecular descriptors and molecular fingerprints, respectively.

Classical QSARs

Readily available Hammett and Taft constants were used for correlations with k values for the reactions of O3, HOCl/OCl–, and ClO2 with different electron-rich organic moieties.70 QSARs for MnO4– have not been developed due to the scarcity of k values. Eighteen QSARs were determined, including 412 k values for the dissociated/nondissociated forms of phenols, anilines, benzene derivatives, olefins, amines and amine derivatives, and aromatic compounds.70 The accuracy of these QSARs was within a factor of 1/3–3 for almost 90% of the micropollutants used in the validation set. This accuracy of second-order rate constants influences the estimated relative abatement of micropollutants with an uncertainty of ≤68%, depending on the extent of degradation.70 Classical QSARs with Hammett constants were also applied to correlate with k values for in situ formed radicals such as the halogen radicals X• and X2•– (where X = Cl or Br) and CO3•–.60,61,82 In situ formed •OH react less selectively with organic compounds and Hammett or Taft constants representing the reactivities for specific moieties are not applicable. Thus, a group contribution method (GCM) that accounts for all possible reactive sites in organic compounds was developed.79 GCM showed an accuracy within a factor of 0.5–2 compared to experimental k values.

QC-QSARs

Because Hammett or Taft constants for some functional groups are not available and the GCM is limited to compounds with previously calibrated functional groups, QC parameters describing aqueous-phase energies of organic compounds33,71−77,83 were applied for the development of QC-QSARs. While the computational uncertainty of calculated energy values requires careful assessment, QC descriptors can be calculated essentially for any compound. Second-order rate constants for the reactions of ozone with target compounds, kO3, from a QC-QSAR approach were within a factor of 4 compared to experimental values (Figure S1 in the Supporting Information (SI)).83 Comparison of the classical and QC-QSARs for kO3 indicates that both approaches can be used complementarily when parameters are not available.83 A series of QC-QSARs were developed based on the most reactive site of compounds determined by the smallest ΔGactaq value from all the ΔGactaq values of reactive sites.84 (Figure S1 in the SI). QC-QSARs based on the ΔGactaq values indicate a predictability of k values within a factor of 2–5 compared to the experimental k values.74,76,77 While QC computations typically provide accurate energies sufficient to observe relative trends in the reactivities of chemical oxidants, an incremental input of an electronic structure and careful monitoring of calculations are labor-intensive.

Machine Learning Approaches

Considering that hundreds of k values for O3, HOCl, ClO2, •OH and SO4•– are available in literature, a combination of classical QSAR with emerging ML tools has been tested.78,80 A molecular fingerprints (MF) approach has been used to identify structural features of 1089 organic compounds and a deep neural model was applied to train those features for kHO• values.80 Similar approaches have been used for O3, HOCl, ClO2, and SO4•– based on the ML algorithms78 with both molecular descriptors (MD) and MF. While these emerging approaches are highly statistical because of the empirical descriptors such as MF and MD, data-driven approaches can handle many compounds more efficiently than classical and QC-QSAR approaches. Correlations observed by ML approaches are comparable to those by QC-QSARs for •OH. However, the correlations obtained by ML approaches (R2 = 0.4–0.6) in particular from testing of k values for dosed chemicals are inferior to those by classical- or QC-QSARs (R2 = 0.8–0.9) because of smaller numbers of k values, indicating the uncertainties of required data to train the ML algorithm. Because ML approaches require large data sets that are essentially obtained by experimental measurements, their applications are currently limited to k values for •OH.

Reaction Mechanisms and Transformation Products Formation

Determination of Transformation Products Experimentally

Determination of transformation products using advanced analytical equipment such as LC-MS/MS, GC-MS/MS, or NMR helped to elucidate major reaction mechanisms based on stable end products.1,2,56,85−92 Very polar transformation products were also identified using IC-MS or SFC-MS/MS.93,94 Experiments without quenching for dosed chemicals or in situ formed radicals followed by direct injection of samples to a MS/MS system may help to determine transient transformation products.95 Some transient radicals can be identified by EPR techniques, although the available spin trapping agents are limited to nonhalogen or -nitrogen radicals. Figure 3 summarizes the major mechanisms for the reactions of dosed chemical oxidants and in situ formed radicals with organic compounds and their primary transformation products identified by product studies from the oxidation of model compounds. Three common major mechanisms of dosed chemical oxidants (O3, HOCl/OCl–, ClO2) include: (1) oxygen transfer; (2) single electron transfer; and (3) addition. Note that there is insufficient mechanistic information on MnO4– reaction mechanisms and therefore it is not included in Figure 3. The use of high-resolution mass spectrometry and stable isotopes has also advanced the experimental detection of transformation products in oxidative and subsequent biological filtration treatment processes.15,16

Figure 3.

Figure 3

Open in a new tab

Major reaction mechanisms induced by ozone, free available chlorine (FAC), chlorine dioxide and the in situ formed radicals (•OH, SO4•–, Cl•, Cl2•–, Br•, Br2•–, NO•, NO2•, CO3•–).

When the rate of a reaction (i.e., the product of k value and oxidant exposure) of in situ formed radicals is in the same order of magnitude or greater than for the dosed chemical oxidants, radical reactions become equal or dominant for the transformation of organic compounds including micropollutants. Three major reaction mechanisms of in situ formed radicals (•OH, SO4•–, X•, X2•–, and XO• where X = Cl or Br, NO•/NO2•, and CO3•–) include: (1) hydrogen (H)-atom abstraction, (2) addition, and (3) single electron transfer. The primary reaction products undergo different reactions such as molecular oxygen addition to a carbon-centered radical formed by H-atom abstraction and radical addition, or hydrolysis of a radical cation produced by single electron transfer. Ultimately, stable transformation products are ketones and carboxylic compounds9,10 because of their smaller reactivities with both dosed chemical oxidants and in situ formed radicals.

Theoretical Elucidation of Reaction Mechanisms

Rule-Based Prediction Systems

Experimental product studies elucidated common patterns for transformation product formation from the oxidation of organic compounds. Algorithms using matrix rearrangement and logical programming have been used to develop in silico pathway generators that enumerate experimentally determined reaction mechanisms for reactions with HO•, chlorine-based radicals96,97 as well as for ozone.98 For example, for the UV/free chlorine process, a pathway generator predicts 497 trichloroethylene products and 6608 elementary reactions induced by UV photolysis, free chlorine, and in situ formed radicals (i.e., HO• and chlorine-based radicals97). An ozone pathway generator with 340 reaction rules can predict transformation products of micropollutants.98 The current versions of rule-based pathway generators do not include prediction of pathways with specific base structures (e.g., aromatic compounds) for HO• because of the lack of a complete understanding of the underlying mechanisms. Therefore, there is a need for a better experimental understanding of such reaction mechanisms.

Quantum Chemical-Based Approaches

Currently, the use of QC computations cannot predict reaction mechanisms as a standalone method, but complement experimentally identified reaction mechanisms, which are often based on detection of stable end products, by providing elementary reaction steps embedded in reactions proposed based on experiments. QC computations provide thermodynamic and kinetic properties of elementary reactions such as aqueous-phase free energies of reaction and activation (Figure S2 in the SI). The energy profiles per reaction coordinate facilitate comparison of multiple reaction pathways, which could determine thermodynamically or kinetically dominant pathways that form transformation products. A thorough analysis of HO•-induced acetone transformation pathways including acetonyl peroxyl radicals and their kinetics in the UV/H2O2 process predicted 88 elementary reaction pathways including acetonyl peroxyl radical pathways (Figure S2 in the SI).92 The decay of the acetonyl radical occurred by 20% via alkoxyl radical, 27% via the Bennett reaction, and 55% via the reaction with HO2•, which was consistent with experimental findings (15%, 25%, and 60%, respectively).99,100 The evolutions of transformation products were found to be within a factor of 2–5 compared to results from laboratory experiments.99 Studies also found embedded pathways during ozonation of N,N-dimethylsulfamide- and bromide-containing waters with the formation of the toxic N-nitrosodimethylamine (Figure S2 in the SI). A rigorous QC approach provides (1) mechanistic insight into reaction pathways of embedded reactions including unstable reaction intermediates, possible formation of a precursor complex and a transition state and (2) electronic properties and geometries to identify possible reactive sites, which cannot be accessed experimentally.

Predicting Transformation Products

Once reaction rate constants and reaction mechanisms are elaborated by a combination of experimental and in silico results, ODEs could be developed and numerically solved to predict the evolution of a parent organic compound and transformation products. In this process, predicting the transformation products of dosed chemicals is more straightforward than that for in situ formed radicals. Dosed chemicals are generally more selective and react with a specific moieties within complex chemical structures of organic compounds. Thus, the subsequent reaction pathway and the ensuing transformation products are driven by one or sometimes a few reactive sites. After depletion of the reactive sites or if no reactive sites for dosed chemicals are present, in situ formed radicals can proceed by multiple parallel or subsequent pathways contributing to the evolution of transformation products. This leads to complex reaction systems, which make it difficult to elucidate transformation products experimentally, yielding often incomplete mass balances. To this end computer-based prediction systems, including all possible reactions could overcome the combinatorial challenges of such reaction systems.

Knowledge Gaps, Future Opportunities, and Outlook

Over the last decades both experimental and theoretical approaches have advanced the ability to predict transformation products during oxidative processes. Second-order rate constants for the reactions of dosed chemicals and in situ formed radicals with a wide range of organic compounds have been determined. Different types of QSARs have been developed based on parameters determined by classical Hammett-type approaches and advanced QC computations and emerging technologies such as ML. The use of HRMS has opened a new window for nontargeted analysis of transformation products. Advanced algorithms developed based on data-driven cheminformatics and the use of sophisticated QC computations helped to predict reaction intermediates/mechanisms which cannot be accessed experimentally. Nevertheless, knowledge gaps still exist to understand and predict the transformation of organic compounds (DOM moieties or micropollutants) for both dosed chemicals and in situ formed radicals. To further advance this field some of the major topics are

  • 1.

    Selection of priority compound classes: Due to the enormous number of organic chemicals, a tiered process is decisive to select compounds for which kinetic and mechanistic experimental information is needed based on e.g., expected toxicity. This will improve pathway prediction tools and allow evaluation of biodegradability of transformation products.

  • 2.

    Improved mass balances: In many transformation reactions, mass balances are poor. Such gaps should be filled with careful experimental studies with advanced analytical tools. In such mechanistic studies, QC computations and pathway prediction tools will help to identify intermediates and support chemical analyses.

  • 3.

    Improvement of in silico tools: This will allow to predict aqueous phase reactivity and reaction pathways of oxidative transformation processes by a better understanding of electronic structures of molecules and radicals in water. Realistic molecular level solvation processes will improve first-principles methods for calculation of second-order rate constants. QC computational methods have to be validated with experimental results for validation and extrapolation to other systems. Reaction pathway predictions will assist interpretation of results from nontarget analysis by HRMS.

  • 4.

    Processing of nontarget analysis: Robust algorithms will help to efficiently determine potential molecular formulas of transformation products. In a combination with other advanced analytical approaches, it will be possible to determine chemical structures for transformation products.

  • 5.

    Application of ML techniques: ML techniques need to be carefully evaluated for predicting kinetic/mechanistic parameters, because of the large data sets that are required for these methods. So far, it appears that there is a lack of data in most fields of oxidation reactions to successfully apply ML.

  • 6.

    Role of water matrix components: Eventually, prediction tools need to be developed for real water treatment systems by including matrix effects such as DOM and inorganic compounds (e.g., halide ions, carbonate, metal ions, etc.), as well as their oxidation products. This requires a better understanding of the chemistry of DOM moieties and inorganic species and their role in consuming oxidants and forming secondary oxidants.

Filling the knowledge gaps identified above will advance the understandings of the fate of micropollutants and transformation products and will be the basis for toxicity assessment during oxidation processes.

Acknowledgments

This work was partially supported by NSF CHM-1808052. D.M. acknowledges the support by the Swiss Federal Institute of Aquatic Science and Technology (Eawag) during his sabbatical.

Biographies

graphic file with name es3c04086_0001.jpg

Daisuke Minakata is an environmental engineer, who has worked on understanding and predicting the fate of organic compounds in oxidative water and wastewater treatment processes for about 20 years. Minakata’s research interests include physical chemical treatment processes, aquatic photochemistry, and environmental sustainability. Minakata and his colleagues have developed computational tools to predict the fate of a variety of organic compounds and micropollutants in membrane and advanced oxidation and reduction processes based on theoretical and experimental investigations.

graphic file with name es3c04086_0002.jpg

Urs von Gunten has been working on oxidative water and wastewater treatment for more than 30 years. His main research focus is on kinetic and mechanistic investigations of oxidative transformation reactions including micropollutants and water matrix components. By combination of different fields of environmental science, he has contributed significantly to a better understanding of the options and limitations of oxidation processes. Besides his academic activities, he emphasizes collaborations with practitioners from the water sector to transfer the findings of his research to real-world applications.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.3c04086.

  • Two figures include prediction of second-order rate constants by quantum chemical-based QSAR approaches and pathway elucidation by quantum chemical computations (PDF)

The authors declare no competing financial interest.

Supplementary Material

es3c04086_si_001.pdf (309.1KB, pdf)

References

  1. von Sonntag C.; von Gunten U.. Chemistry of Ozone in Water and Wastewater Treatment: From Basic Principles to Applications; IWA Publishing: London, 2012. [Google Scholar]
  2. Lim S.; Shi J. L.; von Gunten U.; McCurry D. L. Ozonation of Organic Compounds in Water and Wastewater: A Critical Review. Water Res. 2022, 213, 118053 10.1016/j.watres.2022.118053. [DOI] [PubMed] [Google Scholar]
  3. Hoigné J.Chemistry of Aqueous Ozone and Transformation of Pollutants by Ozonation and Advanced Oxidation Processes. In Quality and Treatment of Drinking Water II, Hrubec J., Ed.; Springer: Berlin/Heidelberg, 1998; pp 83–141. [Google Scholar]
  4. Gottschalk C. L., Judy Saupe A.. Ozonation of Water and Waste Water: A Practical Guide to Understanding Ozone and Its Applications.; Wiley: Hoboken, 2009. [Google Scholar]
  5. von Gunten U. Oxidation Processes in Water Treatment: Are We on Track?. Environ. Sci. Technol. 2018, 52 (9), 5062–5075. 10.1021/acs.est.8b00586. [DOI] [PubMed] [Google Scholar]
  6. Glaze W. H.; Kang J.-W.; Chapin D. H. The Chemistry of Water Treatment Processes Involving Ozone, Hydrogen Peroxide and Ultraviolet Radiation. Ozone Sci. Eng. 1987, 9 (4), 335–352. 10.1080/01919518708552148. [DOI] [Google Scholar]
  7. Glaze W. H.; Kang J. W. Advanced Oxidation Processes. Test of a Kinetic Model for the Oxidation of Organic Compounds with Ozone and Hydrogen Peroxide in a Semibatch Reactor. Ind. Eng. Chem. Res. 1989, 28 (11), 1580–1587. 10.1021/ie00095a002. [DOI] [Google Scholar]
  8. Stefan, M.I. edit. Advanced Oxidation Processes for Water Treatment: Fundamentals and Applications.; IWA Publishing: London, 2017. [Google Scholar]
  9. Stefan M. I.; Bolton J. R. Mechanism of the Degradation of 1,4-Dioxane in Dilute Aqueous Solution Using the Uv/Hydrogen Peroxide Process. Environ. Sci. Technol. 1998, 32 (11), 1588–1595. 10.1021/es970633m. [DOI] [Google Scholar]
  10. Stefan M. I.; Mack J.; Bolton J. R. Degradation Pathways During the Treatment of Methyl Tert-Butyl Ether by the Uv/H2o2 Process. Environ. Sci. Technol. 2000, 34 (4), 650–658. 10.1021/es9905748. [DOI] [Google Scholar]
  11. Acero J. L.; Haderlein S. B.; Schmidt T. C.; Suter M. J. F.; von Gunten U. MTBE Oxidation by Conventional Ozonation and the Combination Ozone/Hydrogen Peroxide: Efficiency of the Processes and Bromate Formation. Environ. Sci. Technol. 2001, 35 (21), 4252–4259. 10.1021/es010044n. [DOI] [PubMed] [Google Scholar]
  12. Acero J. L.; Stemmler K.; von Gunten U. Degradation Kinetics of Atrazine and Its Degradation Products with Ozone and Oh Radicals: A Predictive Tool for Drinking Water Treatment. Environ. Sci. Technol. 2000, 34 (4), 591–597. 10.1021/es990724e. [DOI] [Google Scholar]
  13. Rosario-Ortiz F. L.; Wert E. C.; Snyder S. A. Evaluation of UV/H2O2 Treatment for the Oxidation of Pharmaceuticals in Wastewater. Water Res. 2010, 44 (5), 1440–1448. 10.1016/j.watres.2009.10.031. [DOI] [PubMed] [Google Scholar]
  14. Stefan M. I.; Bolton J. R. UV Direct Photolysis of N-Nitrosodimethylamine (NDMA): Kinetic and Product Study. Helv. Chim. Acta 2002, 85 (5), 1416–1426. . [DOI] [Google Scholar]
  15. Gulde R.; Rutsch M.; Clerc B.; Schollée J. E.; von Gunten U.; McArdell C. S. Formation of Transformation Products During Ozonation of Secondary Wastewater Effluent and Their Fate in Post-Treatment: From Laboratory- to Full-Scale. Water Res. 2021, 200, 117200 10.1016/j.watres.2021.117200. [DOI] [PubMed] [Google Scholar]
  16. Gulde R.; Clerc B.; Rutsch M.; Helbing J.; Salhi E.; McArdell C. S.; von Gunten U. Oxidation of 51 Micropollutants During Drinking Water Ozonation: Formation of Transformation Products and Their Fate During Biological Post-Filtration. Water Res. 2021, 207, 117812 10.1016/j.watres.2021.117812. [DOI] [PubMed] [Google Scholar]
  17. Dodd M. C.; Rentsch D.; Singer H. P.; Kohler H.-P. E.; von Gunten U. Transformation of B-Lactam Antibacterial Agents During Aqueous Ozonation: Reaction Pathways and Quantitative Bioassay of Biologically-Active Oxidation Products. Environ. Sci. Technol. 2010, 44 (15), 5940–5948. 10.1021/es101061w. [DOI] [PubMed] [Google Scholar]
  18. Dodd M. C.; Kohler H.-P. E.; von Gunten U. Oxidation of Antibacterial Compounds by Ozone and Hydroxyl Radical: Elimination of Biological Activity During Aqueous Ozonation Processes. Environ. Sci. Technol. 2009, 43 (7), 2498–2504. 10.1021/es8025424. [DOI] [PubMed] [Google Scholar]
  19. Lee Y.; Escher B. I.; von Gunten U. Efficient Removal of Estrogenic Activity During Oxidative Treatment of Waters Containing Steroid Estrogens. Environ. Sci. Technol. 2008, 42 (17), 6333–6339. 10.1021/es7023302. [DOI] [PubMed] [Google Scholar]
  20. Mestankova H.; Escher B.; Schirmer K.; von Gunten U.; Canonica S. Evolution of Algal Toxicity During (Photo)Oxidative Degradation of Diuron. Aquat. Toxicol. 2011, 101 (2), 466–473. 10.1016/j.aquatox.2010.10.012. [DOI] [PubMed] [Google Scholar]
  21. Mestankova H.; Schirmer K.; Escher B. I.; von Gunten U.; Canonica S. Removal of the Antiviral Agent Oseltamivir and Its Biological Activity by Oxidative Processes. Environ. Pollut. 2012, 161, 30–35. 10.1016/j.envpol.2011.09.018. [DOI] [PubMed] [Google Scholar]
  22. Lange F.; Cornelissen S.; Kubac D.; Sein M. M.; von Sonntag J.; Hannich C. B.; Golloch A.; Heipieper H. J.; Möder M.; von Sonntag C. Degradation of Macrolide Antibiotics by Ozone: A Mechanistic Case Study with Clarithromycin. Chemosphere 2006, 65 (1), 17–23. 10.1016/j.chemosphere.2006.03.014. [DOI] [PubMed] [Google Scholar]
  23. Mestankova H.; Parker A. M.; Bramaz N.; Canonica S.; Schirmer K.; von Gunten U.; Linden K. G. Transformation of Contaminant Candidate List (Ccl3) Compounds During Ozonation and Advanced Oxidation Processes in Drinking Water: Assessment of Biological Effects. Water Res. 2016, 93, 110–120. 10.1016/j.watres.2015.12.048. [DOI] [PubMed] [Google Scholar]
  24. Kosaka K.; Asami M.; Konno Y.; Oya M.; Kunikane S. Identification of Antiyellowing Agents as Precursors of N-Nitrosodimethylamine Production on Ozonation from Sewage Treatment Plant Influent. Environ. Sci. Technol. 2009, 43 (14), 5236–5241. 10.1021/es900227g. [DOI] [PubMed] [Google Scholar]
  25. von Gunten U.; Salhi E.; Schmidt C. K.; Arnold W. A. Kinetics and Mechanisms of N-Nitrosodimethylamine Formation Upon Ozonation of N,N-Dimethylsulfamide-Containing Waters: Bromide Catalysis. Environ. Sci. Technol. 2010, 44 (15), 5762–5768. 10.1021/es1011862. [DOI] [PubMed] [Google Scholar]
  26. Schmidt C. K.; Brauch H.-J. N,N-Dimethylsulfamide as Precursor for N-Nitrosodimethylamine (Ndma) Formation Upon Ozonation and Its Fate During Drinking Water Treatment. Environ. Sci. Technol. 2008, 42 (17), 6340–6346. 10.1021/es7030467. [DOI] [PubMed] [Google Scholar]
  27. Prasse C.; von Gunten U.; Sedlak D. L. Chlorination of Phenols Revisited: Unexpected Formation of A,B-Unsaturated C4-Dicarbonyl Ring Cleavage Products. Environ. Sci. Technol. 2020, 54 (2), 826–834. 10.1021/acs.est.9b04926. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Prasse C.; Ford B.; Nomura D. K.; Sedlak D. L. Unexpected Transformation of Dissolved Phenols to Toxic Dicarbonyls by Hydroxyl Radicals and Uv Light. Proc. Natl. Acad. Sci. U.S.A. 2018, 115 (10), 2311–2316. 10.1073/pnas.1715821115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Mestankova H.; Schirmer K.; Canonica S.; von Gunten U. Development of Mutagenicity During Degradation of N-Nitrosamines by Advanced Oxidation Processes. Water Res. 2014, 66, 399–410. 10.1016/j.watres.2014.08.012. [DOI] [PubMed] [Google Scholar]
  30. Tentscher P. R.; Bourgin M.; von Gunten U. Ozonation of Para-Substituted Phenolic Compounds Yields P-Benzoquinones, Other Cyclic A,B-Unsaturated Ketones, and Substituted Catechols. Environ. Sci. Technol. 2018, 52 (8), 4763–4773. 10.1021/acs.est.8b00011. [DOI] [PubMed] [Google Scholar]
  31. Tentscher P. R.; Escher B. I.; Schlichting R.; König M.; Bramaz N.; Schirmer K.; von Gunten U. Toxic Effects of Substituted P-Benzoquinones and Hydroquinones in in Vitro Bioassays Are Altered by Reactions with the Cell Assay Medium. Water Res. 2021, 202, 117415 10.1016/j.watres.2021.117415. [DOI] [PubMed] [Google Scholar]
  32. Lei Y.; Lei X.; Westerhoff P.; Tong X.; Ren J.; Zhou Y.; Cheng S.; Ouyang G.; Yang X. Bromine Radical (Br• and Br2•−) Reactivity with Dissolved Organic Matter and Brominated Organic Byproduct Formation. Environ. Sci. Technol. 2022, 56 (8), 5189–5199. 10.1021/acs.est.2c00549. [DOI] [PubMed] [Google Scholar]
  33. Lim S.; Barrios B.; Minakata D.; von Gunten U. Reactivity of Bromine Radical with Dissolved Organic Matter Moieties and Monochloramine: Effect on Bromate Formation During Ozonation. Environ. Sci. Technol. 2023, 10.1021/acs.est.2c07694. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Thibaud H.; de Laat J.; Merlet N.; Doré M. Formation De Chloropicrine En Milieu Aqueux: Influence Des Nitrites Sur La Formation De Precurseurs Par Oxydation De Composes Organiques. Water Res. 1987, 21 (7), 813–821. 10.1016/0043-1354(87)90157-6. [DOI] [Google Scholar]
  35. Magdeburg A.; Stalter D.; Schlüsener M.; Ternes T.; Oehlmann J. Evaluating the Efficiency of Advanced Wastewater Treatment: Target Analysis of Organic Contaminants and (Geno-)Toxicity Assessment Tell a Different Story. Water Res. 2014, 50, 35–47. 10.1016/j.watres.2013.11.041. [DOI] [PubMed] [Google Scholar]
  36. Stalter D.; Magdeburg A.; Weil M.; Knacker T.; Oehlmann J. Toxication or Detoxication? In Vivo Toxicity Assessment of Ozonation as Advanced Wastewater Treatment with the Rainbow Trout. Water Res. 2010, 44 (2), 439–448. 10.1016/j.watres.2009.07.025. [DOI] [PubMed] [Google Scholar]
  37. Schindler Wildhaber Y.; Mestankova H.; Schärer M.; Schirmer K.; Salhi E.; von Gunten U. Novel Test Procedure to Evaluate the Treatability of Wastewater with Ozone. Water Res. 2015, 75, 324–335. 10.1016/j.watres.2015.02.030. [DOI] [PubMed] [Google Scholar]
  38. Richardson S. D.; Plewa M. J. To Regulate or Not to Regulate? What to Do with More Toxic Disinfection by-Products?. J. Environ. Chem. Eng. 2020, 8 (4), 103939 10.1016/j.jece.2020.103939. [DOI] [Google Scholar]
  39. Li X.-F.; Mitch W. A. Drinking Water Disinfection Byproducts (Dbps) and Human Health Effects: Multidisciplinary Challenges and Opportunities. Environ. Sci. Technol. 2018, 52 (4), 1681–1689. 10.1021/acs.est.7b05440. [DOI] [PubMed] [Google Scholar]
  40. MacKeown H.; von Gunten U.; Criquet J. Iodide Sources in the Aquatic Environment and Its Fate During Oxidative Water Treatment – a Critical Review. Water Res. 2022, 217, 118417 10.1016/j.watres.2022.118417. [DOI] [PubMed] [Google Scholar]
  41. Richardson S. D.; Ternes T. A. Water Analysis: Emerging Contaminants and Current Issues. Anal. Chem. 2018, 90 (1), 398–428. 10.1021/acs.analchem.7b04577. [DOI] [PubMed] [Google Scholar]
  42. Marron E. L.; Prasse C.; Buren J. V.; Sedlak D. L. Formation and Fate of Carbonyls in Potable Water Reuse Systems. Environ. Sci. Technol. 2020, 54 (17), 10895–10903. 10.1021/acs.est.0c02793. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Manasfi T.; Houska J.; Gebhardt I.; von Gunten U. Formation of Carbonyl Compounds During Ozonation of Lake Water and Wastewater: Development of a Non-Target Screening Method and Quantification of Target Compounds. Water Res. 2023, 237, 119751 10.1016/j.watres.2023.119751. [DOI] [PubMed] [Google Scholar]
  44. Houska J.; Manasfi T.; von Gunten U., Ozonation of Lake Water and Wastewater: Identification of Carbonous and Nitrogenous Carbonyl- Containing Oxidation Byproducts by Non-Target Screening. Submitted. [DOI] [PubMed]
  45. Wert E. C.; Rosario-Ortiz F. L.; Drury D. D.; Snyder S. A. Formation of Oxidation Byproducts from Ozonation of Wastewater. Water Res. 2007, 41 (7), 1481–1490. 10.1016/j.watres.2007.01.020. [DOI] [PubMed] [Google Scholar]
  46. Schwarzenbach R. P.; Escher B. I.; Fenner K.; Hofstetter T. B.; Johnson C. A.; von Gunten U.; Wehrli B. The Challenge of Micropollutants in Aquatic Systems. Science 2006, 313 (5790), 1072–1077. 10.1126/science.1127291. [DOI] [PubMed] [Google Scholar]
  47. Wang Z.; Walker G. W.; Muir D. C. G.; Nagatani-Yoshida K. Toward a Global Understanding of Chemical Pollution: A First Comprehensive Analysis of National and Regional Chemical Inventories. Environ. Sci. Technol. 2020, 54 (5), 2575–2584. 10.1021/acs.est.9b06379. [DOI] [PubMed] [Google Scholar]
  48. Chemical Abstract Service . A Division of the American Chemical Society. In https://www.cas.org/cas-data/cas-registry, 2022.
  49. Prasse C.; Stalter D.; Schulte-Oehlmann U.; Oehlmann J.; Ternes T. A. Spoilt for Choice: A Critical Review on the Chemical and Biological Assessment of Current Wastewater Treatment Technologies. Water Res. 2015, 87, 237–270. 10.1016/j.watres.2015.09.023. [DOI] [PubMed] [Google Scholar]
  50. Dong H.; Cuthbertson A. A.; Plewa M. J.; Weisbrod C. R.; McKenna A. M.; Richardson S. D. Unravelling High-Molecular-Weight Dbp Toxicity Drivers in Chlorinated and Chloraminated Drinking Water: Effect-Directed Analysis of Molecular Weight Fractions. Environ. Sci. Technol. 2023, 10.1021/acs.est.3c00771. [DOI] [PubMed] [Google Scholar]
  51. NDRL/NIST , Nist Solution Kinetics Database on the Web. In https://kinetics.nist.gov/solution/.
  52. Waldemer R. H.; Tratnyek P. G. Kinetics of Contaminant Degradation by Permanganate. Environ. Sci. Technol. 2006, 40 (3), 1055–1061. 10.1021/es051330s. [DOI] [PubMed] [Google Scholar]
  53. Hoigné J.; Bader H. Kinetics of Reactions of Chlorine Dioxide (Oclo) in Water—I. Rate Constants for Inorganic and Organic Compounds. Water Res. 1994, 28 (1), 45–55. 10.1016/0043-1354(94)90118-X. [DOI] [Google Scholar]
  54. Abdighahroudi M. S.; Jütte M.; Hupperich K.; Mutke X. A. M.; Schmidt T. C.; Lutze H. V., Chapter Three - Mechanisms and Byproduct Formation in the Application of Chlorine Dioxide. In Comprehensive Analytical Chemistry; Manasfi T., Boudenne J.-L., Eds.; Elsevier: Amsterdam, 2021; Vol. 92, pp 51–83. [Google Scholar]
  55. Lee J.; von Gunten U.; Kim J.-H. Persulfate-Based Advanced Oxidation: Critical Assessment of Opportunities and Roadblocks. Environ. Sci. Technol. 2020, 54 (6), 3064–3081. 10.1021/acs.est.9b07082. [DOI] [PubMed] [Google Scholar]
  56. Deborde M.; von Gunten U. Reactions of Chlorine with Inorganic and Organic Compounds During Water Treatment—Kinetics and Mechanisms: A Critical Review. Water Res. 2008, 42 (1), 13–51. 10.1016/j.watres.2007.07.025. [DOI] [PubMed] [Google Scholar]
  57. Laszakovits J. R.; Kerr A.; MacKay A. A. Permanganate Oxidation of Organic Contaminants and Model Compounds. Environ. Sci. Technol. 2022, 56 (8), 4728–4748. 10.1021/acs.est.1c03621. [DOI] [PubMed] [Google Scholar]
  58. Gan W.; Ge Y.; Zhong Y.; Yang X. The Reactions of Chlorine Dioxide with Inorganic and Organic Compounds in Water Treatment: Kinetics and Mechanisms. Environ. Sci. Water Res. Technol. 2020, 6 (9), 2287–2312. 10.1039/D0EW00231C. [DOI] [Google Scholar]
  59. Buxton G. V.; Greenstock C. L.; Helman W. P.; Ross A. B. Critical Review of Rate Constants for Reactions of Hydrated Electrons, Hydrogen Atoms and Hydroxyl Radicals (·Oh/·O– in Aqueous Solution. J. Phys. Chem. Ref. Data 1988, 17 (2), 513–886. 10.1063/1.555805. [DOI] [Google Scholar]
  60. Lei Y.; Cheng S.; Luo N.; Yang X.; An T. Rate Constants and Mechanisms of the Reactions of Cl• and Cl2•– with Trace Organic Contaminants. Environ. Sci. Technol. 2019, 53 (19), 11170–11182. 10.1021/acs.est.9b02462. [DOI] [PubMed] [Google Scholar]
  61. Lei Y.; Lei X.; Yu Y.; Li K.; Li Z.; Cheng S.; Ouyang G.; Yang X. Rate Constants and Mechanisms for Reactions of Bromine Radicals with Trace Organic Contaminants. Environ. Sci. Technol. 2021, 55 (15), 10502–10513. 10.1021/acs.est.1c02313. [DOI] [PubMed] [Google Scholar]
  62. Arnold W. A.; Oueis Y.; O’Connor M.; Rinaman J. E.; Taggart M. G.; McCarthy R. E.; Foster K. A.; Latch D. E. Qsars for Phenols and Phenolates: Oxidation Potential as a Predictor of Reaction Rate Constants with Photochemically Produced Oxidants. Environ. Sci. Process Impacts 2017, 19 (3), 324–338. 10.1039/C6EM00580B. [DOI] [PubMed] [Google Scholar]
  63. Alfassi Z. B.; Huie R. E.; Neta P. Substituent Effects on Rates of One-Electron Oxidation of Phenols by the Radicals Chlorine Dioxide, Nitrogen Dioxide, and Trioxosulfate(1-). J. Phys. Chem. 1986, 90 (17), 4156–4158. 10.1021/j100408a063. [DOI] [Google Scholar]
  64. Alfassi Z. B.; Huie R. E.; Neta P.; Shoute L. C. T. Temperature Dependence of the Rate Constants for Reaction of Inorganic Radicals with Organic Reductants. J. Phys. Chem. 1990, 94 (25), 8800–8805. 10.1021/j100388a011. [DOI] [Google Scholar]
  65. Bors W.; Michel C.; Dalke C.; Stettmaier K.; Saran M.; Andrae U. Radical Intermediates During the Oxidation of Nitropropanes. The Formation of Nitrous Oxide from 2-Nitropropane, Its Reactivity with Nucleosides, and Implications for the Genotoxicity of 2-Nitropropane. Chem. Res. Toxicol. 1993, 6 (3), 302–309. 10.1021/tx00033a008. [DOI] [PubMed] [Google Scholar]
  66. Huie R. E.; Neta P. Kinetics of One-Electron Transfer Reactions Involving Chlorine Dioxide and Nitrogen Dioxide. J. Phys. Chem. 1986, 90 (6), 1193–1198. 10.1021/j100278a046. [DOI] [Google Scholar]
  67. Tentscher P. R.; Lee M.; von Gunten U. Micropollutant Oxidation Studied by Quantum Chemical Computations: Methodology and Applications to Thermodynamics, Kinetics, and Reaction Mechanisms. Acc. Chem. Res. 2019, 52 (3), 605–614. 10.1021/acs.accounts.8b00610. [DOI] [PubMed] [Google Scholar]
  68. Eyring H.; Gershinowitz H.; Sun C. E. The Absolute Rate of Homogeneous Atomic Reactions. J. Chem. Phys. 1935, 3 (12), 786–796. 10.1063/1.1749593. [DOI] [Google Scholar]
  69. Karelson M.Molecular Descriptors in QSAR/QSPR.; Wiley: New York, 2000. [Google Scholar]
  70. Lee Y.; von Gunten U. Quantitative Structure–Activity Relationships (Qsars) for the Transformation of Organic Micropollutants During Oxidative Water Treatment. Water Res. 2012, 46 (19), 6177–6195. 10.1016/j.watres.2012.06.006. [DOI] [PubMed] [Google Scholar]
  71. Barrios B.; Minakata D. Aqueous-Phase Single-Electron Transfer Calculations for Carbonate Radicals Using the Validated Marcus Theory. Environ. Sci. Technol. 2023, 10 (2), 204–209. 10.1021/acs.estlett.2c00913. [DOI] [Google Scholar]
  72. Barrios B.; Mohrhardt B.; Doskey P. V.; Minakata D. Mechanistic Insight into the Reactivities of Aqueous-Phase Singlet Oxygen with Organic Compounds. Environ. Sci. Technol. 2021, 55 (12), 8054–8067. 10.1021/acs.est.1c01712. [DOI] [PubMed] [Google Scholar]
  73. Minakata D.; Kamath D.; Maetzold S. Mechanistic Insight into the Reactivity of Chlorine-Derived Radicals in the Aqueous-Phase Uv–Chlorine Advanced Oxidation Process: Quantum Mechanical Calculations. Environ. Sci. Technol. 2017, 51 (12), 6918–6926. 10.1021/acs.est.7b00507. [DOI] [PubMed] [Google Scholar]
  74. Minakata D.; Song W.; Mezyk S. P.; Cooper W. J. Experimental and Theoretical Studies on Aqueous-Phase Reactivity of Hydroxyl Radicals with Multiple Carboxylated and Hydroxylated Benzene Compounds. Phys. Chem. Chem. Phys. 2015, 17 (17), 11796–11812. 10.1039/C5CP00861A. [DOI] [PubMed] [Google Scholar]
  75. Minakata D.; Mezyk S. P.; Jones J. W.; Daws B. R.; Crittenden J. C. Development of Linear Free Energy Relationships for Aqueous Phase Radical-Involved Chemical Reactions. Environ. Sci. Technol. 2014, 48 (23), 13925–13932. 10.1021/es504491z. [DOI] [PubMed] [Google Scholar]
  76. Minakata D.; Song W.; Crittenden J. Reactivity of Aqueous Phase Hydroxyl Radical with Halogenated Carboxylate Anions: Experimental and Theoretical Studies. Environ. Sci. Technol. 2011, 45 (14), 6057–6065. 10.1021/es200978f. [DOI] [PubMed] [Google Scholar]
  77. Minakata D.; Crittenden J. Linear Free Energy Relationships between Aqueous Phase Hydroxyl Radical Reaction Rate Constants and Free Energy of Activation. Environ. Sci. Technol. 2011, 45 (8), 3479–3486. 10.1021/es1020313. [DOI] [PubMed] [Google Scholar]
  78. Zhong S.; Zhang Y.; Zhang H. Machine Learning-Assisted Qsar Models on Contaminant Reactivity toward Four Oxidants: Combining Small Data Sets and Knowledge Transfer. Environ. Sci. Technol. 2022, 56 (1), 681–692. 10.1021/acs.est.1c04883. [DOI] [PubMed] [Google Scholar]
  79. Minakata D.; Li K.; Westerhoff P.; Crittenden J. Development of a Group Contribution Method to Predict Aqueous Phase Hydroxyl Radical (HO•) Reaction Rate Constants. Environ. Sci. Technol. 2009, 43 (16), 6220–6227. 10.1021/es900956c. [DOI] [PubMed] [Google Scholar]
  80. Zhong S.; Zhang K.; Wang D.; Zhang H. Shedding Light on “Black Box” Machine Learning Models for Predicting the Reactivity of Ho Radicals toward Organic Compounds. Chem. Eng. J. 2021, 405, 126627 10.1016/j.cej.2020.126627. [DOI] [Google Scholar]
  81. Luo S.; Wei Z.; Dionysiou D. D.; Spinney R.; Hu W.-P.; Chai L.; Yang Z.; Ye T.; Xiao R. Mechanistic Insight into Reactivity of Sulfate Radical with Aromatic Contaminants through Single-Electron Transfer Pathway. Chem. Eng. J. 2017, 327, 1056–1065. 10.1016/j.cej.2017.06.179. [DOI] [Google Scholar]
  82. Canonica S.; Kohn T.; Mac M.; Real F. J.; Wirz J.; von Gunten U. Photosensitizer Method to Determine Rate Constants for the Reaction of Carbonate Radical with Organic Compounds. Environ. Sci. Technol. 2005, 39 (23), 9182–9188. 10.1021/es051236b. [DOI] [PubMed] [Google Scholar]
  83. Lee M.; Zimmermann-Steffens S. G.; Arey J. S.; Fenner K.; von Gunten U. Development of Prediction Models for the Reactivity of Organic Compounds with Ozone in Aqueous Solution by Quantum Chemical Calculations: The Role of Delocalized and Localized Molecular Orbitals. Environ. Sci. Technol. 2015, 49 (16), 9925–9935. 10.1021/acs.est.5b00902. [DOI] [PubMed] [Google Scholar]
  84. Eyring H.; Gershinowitz H.; Sun C. E. The Absolute Rate of Homogeneous Atomic Reactions. J. Chem. Phys. 1935, 3 (12), 786–796. 10.1063/1.1749593. [DOI] [Google Scholar]
  85. Gerrity D.; Snyder S. Review of Ozone for Water Reuse Applications: Toxicity, Regulations, and Trace Organic Contaminant Oxidation. Ozone Sci. Eng. 2011, 33 (4), 253–266. 10.1080/01919512.2011.578038. [DOI] [Google Scholar]
  86. Huling S. G.; Ross R. R.; Meeker Prestbo K. In Situ Chemical Oxidation: Permanganate Oxidant Volume Design Considerations. Ground Water Monit. Rem. 2017, 37 (2), 78–86. 10.1111/gwmr.12195. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Krembs F. J.; Siegrist R. L.; Crimi M. L.; Furrer R. F.; Petri B. G. Isco for Groundwater Remediation: Analysis of Field Applications and Performance. Ground Water Monit. Rem. 2010, 30 (4), 42–53. 10.1111/j.1745-6592.2010.01312.x. [DOI] [Google Scholar]
  88. Hu L.; Martin H. M.; Strathmann T. J. Oxidation Kinetics of Antibiotics During Water Treatment with Potassium Permanganate. Environ. Sci. Technol. 2010, 44 (16), 6416–6422. 10.1021/es101331j. [DOI] [PubMed] [Google Scholar]
  89. Jiang J.; Pang S.-Y.; Ma J.; Liu H. Oxidation of Phenolic Endocrine Disrupting Chemicals by Potassium Permanganate in Synthetic and Real Waters. Environ. Sci. Technol. 2012, 46 (3), 1774–1781. 10.1021/es2035587. [DOI] [PubMed] [Google Scholar]
  90. Acero J. L.; Piriou P.; von Gunten U. Kinetics and Mechanisms of Formation of Bromophenols During Drinking Water Chlorination: Assessment of Taste and Odor Development. Water Res. 2005, 39 (13), 2979–2993. 10.1016/j.watres.2005.04.055. [DOI] [PubMed] [Google Scholar]
  91. Lee Y.; von Gunten U. Advances in Predicting Organic Contaminant Abatement During Ozonation of Municipal Wastewater Effluent: Reaction Kinetics, Transformation Products, and Changes of Biological Effects. Environ. Sci. Water Res. Technol. 2016, 2 (3), 421–442. 10.1039/C6EW00025H. [DOI] [Google Scholar]
  92. Rodríguez E. M.; Acero J. L.; Spoof L.; Meriluoto J. Oxidation of Mc-Lr and -Rr with Chlorine and Potassium Permanganate: Toxicity of the Reaction Products. Water Res. 2008, 42 (6), 1744–1752. 10.1016/j.watres.2007.10.039. [DOI] [PubMed] [Google Scholar]
  93. Nihemaiti M.; Icker M.; Seiwert B.; Reemtsma T. Revisiting Disinfection Byproducts with Supercritical Fluid Chromatography-High Resolution-Mass Spectrometry: Identification of Novel Halogenated Sulfonic Acids in Disinfected Drinking Water. Environ. Sci. Technol. 2023, 57 (9), 3527–3537. 10.1021/acs.est.2c05536. [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Hermes N.; Jewell K. S.; Falås P.; Lutze H. V.; Wick A.; Ternes T. A. Ozonation of Sitagliptin: Removal Kinetics and Elucidation of Oxidative Transformation Products. Environ. Sci. Technol. 2020, 54 (17), 10588–10598. 10.1021/acs.est.0c01454. [DOI] [PubMed] [Google Scholar]
  95. Mawhinney D. B.; Vanderford B. J.; Snyder S. A. Transformation of 1h-Benzotriazole by Ozone in Aqueous Solution. Environ. Sci. Technol. 2012, 46 (13), 7102–7111. 10.1021/es300338e. [DOI] [PubMed] [Google Scholar]
  96. Li K.; Crittenden J. Computerized Pathway Elucidation for Hydroxyl Radical-Induced Chain Reaction Mechanisms in Aqueous Phase Advanced Oxidation Processes. Environ. Sci. Technol. 2009, 43 (8), 2831–2837. 10.1021/es802039y. [DOI] [PubMed] [Google Scholar]
  97. Zhang W.; Zhou S.; Wu Y.; Zhu S.; Crittenden J. Computerized Pathway Generator for the UV/Free Chlorine Process: Prediction of Byproducts and Reactions. Environ. Sci. Technol. 2021, 55 (4), 2608–2617. 10.1021/acs.est.0c07080. [DOI] [PubMed] [Google Scholar]
  98. Lee M.; Blum L. C.; Schmid E.; Fenner K.; von Gunten U. A Computer-Based Prediction Platform for the Reaction of Ozone with Organic Compounds in Aqueous Solution: Kinetics and Mechanisms. Environ. Sci. Process Impacts 2017, 19 (3), 465–476. 10.1039/C6EM00584E. [DOI] [PubMed] [Google Scholar]
  99. Kamath D.; Mezyk S. P.; Minakata D. Elucidating the Elementary Reaction Pathways and Kinetics of Hydroxyl Radical-Induced Acetone Degradation in Aqueous Phase Advanced Oxidation Processes. Environ. Sci. Technol. 2018, 52 (14), 7763–7774. 10.1021/acs.est.8b00582. [DOI] [PubMed] [Google Scholar]
  100. Stefan M. I.; Bolton J. R. Reinvestigation of the Acetone Degradation Mechanism in Dilute Aqueous Solution by the UV/H2O2 Process. Environ. Sci. Technol. 1999, 33 (6), 870–873. 10.1021/es9808548. [DOI] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

es3c04086_si_001.pdf (309.1KB, pdf)

Articles from Environmental Science & Technology are provided here courtesy of American Chemical Society

RESOURCES