Stochastic Dynamics Mass Spectrometric Analysis of Ozone Depletion Reactions of Pharmaceutics

Bojidarka Ivanova

doi:10.53941/acpa.2026.100001

Abstract

Pharmaceutical xenobiotics at ng·L⁻¹ concentration levels occur in environmental aquatics as metabolized or unchanged chemicals, due to human and animal excretion. Wastewater plants treat polluted sewage waters. Despite this, transformed pollutants are released into the environmental aquatics. Ozonation is utilized for therapeutic purposes. It is based on ion-radicals from O₃ and organic matter. Details on the mechanistic aspects of degradation reactions of pharmaceuticals due to ozonation are crucial in elaborating precise kinetic models for real process monitoring and optimizing cleanup technologies used in sludge and wastewater plants. It is relevant to environmental chemistry, chemical engineering, and process control; thus, improving real industrial-scale technologies. This study first applies an innovative stochastic dynamics mass spectrometric model formula to ozonation processes of metronidazole and carbamazepine. It uses ultra-high resolution electrospray ionization mass spectrometry, high-accuracy approaches to quantum chemistry, and chemometrics. The quantification of metronidazole via the innovative formula, thus, assessing relation D″_SD = f(conc.), yields excellent performances |r| = 0.99979. The mass spectrometric-based structural analysis shows |r| = 0.93957. These excellent performances of examining the very complex case of ozonation reaction of metronidazole highlight that the novel stochastic dynamics tool is the only currently available method for processing of mass spectrometric data on fluctuations of low intensity analyte peaks; thus, producing not only exact quantitative, but also 3D molecular structural analysis of species. The reported performances underline the innovative formula as a prospective novel approach to both the fundamental analytical sciences and industry.

References

1.
Beltran, F.; Jimenez-Lopez, M.; Alvarez, P.; et al. Kinetic modelling of ozonation and photolytic ozonation of metronidazole removal from water. J. Ind. Eng. Chem. 2025, 144, 654–662. https://doi.org/10.1016/j.jiec.2024.10.010.
2.
Perez, T.; Garcia-Segura, S.; El-Ghenymy, A.; et al. Solar photoelectro-Fenton degradation of the antibiotic metronidazole using a flow plant with a Pt/air-diffusion cell and a CPC photoreactor. Electrochim. Acta 2015, 165, 173–181. https://doi.org/10.1016/j.electacta.2015.02.243.
3.
Moslehi, M.; Zadeh, M.; Nateq, K.; et al. Statistical computational optimization approach for photocatalytic-ozonation decontamination of metronidazole in aqueous media using CuFe2O4/SiO2/ZnO nanocomposite. Environm. Res. 2024, 242, 117747. https://doi.org/10.1016/j.envres.2023.117747.
4.
Gahrouei, G.; Vakili, S.; Zandifar, A.; et al. From wastewater to clean water: Recent advances on the removal of metronidazole, ciprofloxacin, and sulfamethoxazole antibiotics from water through adsorption and advanced oxidation processes (AOPs). Environm. Res. 2024, 252, 119029. https://doi.org/10.1016/j.envres.2024.119029.
5.
Skalska-Tuomi, K.; Kaijanen, L.; Monteagudo, J.; et al. Efficient removal of pharmaceuticals from wastewater: Comparative study of three advanced oxidation processes. J. Environ. Manag. 2025, 375, 124276. https://doi.org/10.1016/j.jenvman.2025.124276.
6.
Serna-Carrizales, J.; Zarate-Guzman, A.; Flores-Ramirez, R.; et al. Application of artificial intelligence for the optimization of advanced oxidation processes to improve the water quality polluted with pharmaceutical compounds. Chemosphere 2024, 351, 141216. https://doi.org/10.1016/j.chemosphere.2024.141216.
7.
Available online: https://eur-lex.europa.eu/eli/dir/2024/3019/oj/eng (accessed on 27 November 2024).
8.
Zhang, T.; Zheng, L.; Yang, X.; et al. Integrated spectral based monitoring, optimization and control of the combined ozonation and powdered activated carbon adsorption process to remove organic micropollutants from secondary effluent. Water Res. 2025, 268, 122588. https://doi.org/10.1016/j.watres.2024.122588.
9.
Alhuwaymil, Z. Development, analysis, and effectiveness of an F-C-MgO/rGOP catalyst for the degradation of atrazine using ozonation process: Synergistic effect, mechanism, and toxicity assessment. J. Environ. Manag. 2025, 374, 123990. https://doi.org/10.1016/j.jenvman.2024.123990.
10.
Ivanova, B. Special Issue with Research Topics on “Recent Analysis and Applications of Mass Spectra on Biochemistry”. Int. J. Mol. Sci. 2024, 25, 1995. https://doi.org/10.3390/ijms25041995.
11.
Prada-Vasquez, M.; Simarro-Gimeno, C.; Vidal-Barreiro, I.; et al. Application of catalytic ozonation using Y zeolite in the elimination of pharmaceuticals in effluents from municipal wastewater treatment plants. Sci. Total Environ. 2024, 925, 171625. https://doi.org/10.1016/j.scitotenv.2024.171625.
12.
Janda, M.; Seah, B.; Jakob, D.; et al. Determination of abundant metabolite matrix adducts illuminates the dark metabolome of MALDI-mass spectrometry imaging datasets. Anal. Chem. 2021, 93, 8399−8407. https://doi.org/10.1021/acs.analchem.0c04720.
13.
Ivanova, B. Stochastic dynamics mass spectrometric quantification and 3D molecular structural analysis of tricyclic antidepressant in marine dissolved organic matter. Environ. Geochem. Health 2025, 47, 211. https://doi.org/10.1007/s10653-025-02519-4.
14.
Ivanova, B.; Spiteller, M. Exact quantifying of mass spectrometric variable intensity of analyte peaks with respect to experimental conditions of measurements—A stochastic dynamic approach. Anal. Chem. Lett. 2022, 12, 542–561. https://doi.org/10.1080/22297928.2022.2086822.
15.
Ivanova, B. Stochastic dynamic mass spectrometric quantitative and structural analyses of pharmaceutics and biocides in biota and sewage sludge. Int. J. Mol. Sci. 2023, 24, 6306.
16.
Ivanova, B.; Spiteller, M. A stochastic dynamic mass spectrometric diffusion method and its application to 3D structural analysis of the analytes. Rev. Anal. Chem. 2019, 38, 1. https://doi.org/10.1515/revac-2019-0003.
17.
Ivanova, B.; Spiteller, M. Stochastic dynamic mass spectrometric approach to quantify reserpine in solution. Anal. Chem. Letts. 2020, 10, 703–721.
18.
Ivanova, B.; Spiteller, M. Mass spectrometric stochastic dynamic 3D structural analysis of mixture of steroids in solution—Experimental and theoretical study. Steroids 2022, 181, 109001.
19.
Ivanova, B.; Spiteller, M. Chapter 1: Mass spectrometric and quantum chemical treatments of molecular and ionic interactions of a flavonoid-O-glycoside—A stochastic dynamic approach. In Advances in Chemistry Research; Taylor, J., Ed.; NOVA Science Publishers: New York, NY, USA, 2022; Volume 74, pp. 1–126.
20.
Ivanova, B.; Spiteller, M. Stochastic dynamic ultraviolet photofragmentation and high collision energy dissociation mass spectrometric kinetics of triadimenol and sucralose. Environ. Sci. Pollut. Res. 2023, 30, 32348–33237.
21.
Ivanova, B.; Spiteller, M. Stochastic dynamic quantitative and 3D structural matrix assisted laser desorption/ionization mass spectrometric analyses of mixture of nucleosides. J. Mol. Struct. 2022, 1260, 132701.
22.
Ivanova, B.; Spiteller, M. Electrospray ionization stochastic dynamic mass spectrometric 3D structural analysis of ZnII-ion containing complexes in solution. Inorg. Nano-Met. Chem. 2022, 52, 1407–1429.
23.
Ivanova, B. Structural analysis of polylactic acid in composite starch biopolymers—A stochastic dynamics mass spectrometric approach. Innov. Discov. 2024, 1, 26. https://doi.org/10.53964/id.2024026.
24.
Ivanova, B. Stochastic dynamics mass spectrometric and Fourier transform infrared spectroscopic structural analyses of composite biodegradable plastics. Pollut. Study 2024, 5, 2741. https://doi.org/10.54517/ps.v5i2.2741.
25.
Ivanova, B. Stochastic dynamics mass spectrometric structural analysis of nucleotides, Adv. Anal. Sci. 2024, 5, 3043. https://doi.org/10.54517/aas.v5i2.3043.
26.
Ammar, H.; Brahim, M.; Abdelhédi, R.; et al. Enhanced degradation of metronidazole by sunlight via photo-Fenton process under gradual addition of hydrogen peroxide. J. Mol. Catal. A Chem. 2016, 420, 222–227. http://doi.org/10.1016/j.molcata.2016.04.029.
27.
Höhn, D.; Renner, G. Small HRMS (Orbitrap) raw Files in Profile Mode for Fast Testruns of Processing Software [Data set]. Zenodo 2025. https://doi.org/10.5281/zenodo.15477095.
28.
Frisch, M.; Trucks, G.W.; Schlegel, H.B. et al. Gaussian 09; Gaussian, Inc.: Pittsburgh, PA, USA, 2009. Available online: https://gaussian.com/gaussian16/ (accessed on 22 October 2025).
29.
Dalton2011 Program Package. Available online: https://gitlab.com/dalton/dalton/tree/release/2020 (accessed on 22 October 2025).
30.
Gordon, M.; Schmidt, M. Advances in electronic structure theory: GAMESS a decade later. In Theory and Applications of Computational Chemistry: The First Forty Years; Dykstra, C., Frenking, G., Kim, K., et al., Eds.; Elsevier: Amsterdam, The Netherlands, 2005; pp. 1167–1189.
31.
Zhao, Y.; Truhlar, D. Density functionals with broad applicability in chemistry. Acc. Chem. Res. 2008, 41, 157–167. https://doi.org/10.1021/ar700111a.
32.
Zhao, Y.; Truhlar, D. The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: Two new functionals and systematic testing of four M06–class functionals and 12 other functionals. Theor. Chem. Acc. 2008, 120, 215–241. https://doi.org/10.1007/s00214-007-0310-x.
33.
Hay, P.; Wadt, W. Ab initio effective core potentials for molecular calculations. Potentials for K to Au including the outermost core orbitals. J. Chem. Phys. 1985, 82, 299. https://doi.org/10.1063/1.448975.
34.
Truhlar, D. A simple approximation for the vibrational partition function of a hindered internal rotation. J. Comput. Chem. 1991, 12, 266–270. https://doi.org/10.1002/jcc.540120217.
35.
Wang, H.; Castillo, A.; Bozzelli, J. Thermochemical properties enthalpy, entropy, and heat capacity of C1–C4 fluorinated hydrocarbons: Fluorocarbon group additivity. J. Phys. Chem. A 2015, 119, 8202–8215. https://doi.org/10.1021/acs.jpca.5b03912.
36.
Schleyer, P.; Schreiner, P.; Allinger, N.; et al. (Eds.) Encyclopedia of Computational Chemistry; Wiley: West Sussex, UK, 1998; Volumes 2 and 3, pp. 812–2300.
37.
Schreiner, P.; Allen, W.; Orozco, M.; et al. Computational Molecular Science; Wiley: West Sussex, UK, 2014; Volumes 2 and 4, pp. 513–1257+1631–2523.
38.
Allinger, L. Conformational analysis. 130. MM2. A hydrocarbon force field utilizing V1 and V2 torsional terms. J. Am. Chem. Soc. 1977, 99, 8127–8134. https://doi.org/10.1021/ja00467a001.
39.
Burkert, U.; Allinger, N. Molecular Mechanics in ACS Monograph 177; American Chemical Society: Washington, DC, USA, 1982; pp. 1–339.
40.
Miller, J.; Miller, M. Statistics and Chemometrics for Analytical Chemistry; Pentice Hall: London, UK, 1988; pp. 1–271.
41.
Gao, S.; Zhao, Z.; Xu, Y.; et al. Oxidation of sulfamethoxazole (SMX) by chlorine, ozone and permanganate—A comparative study. J. Hazard. Mater. 2014, 274, 258–269. https://doi.org/10.1016/j.jhazmat.2014.04.024.
42.
Magana, A.; Kamimura, N.; Soumyanath, A.; et al. Caffeoylquinic acids: Chemistry, biosynthesis, occurrence, analytical challenges, and bioactivity. Plant J. 2021, 107, 1299–1319. https://doi.org/10.1111/tpj.15390.
43.
Can, Z.; Gurol, M. Formaldehyde formation during ozonation of drinking water. Ozone Sci. Eng. 2003, 25, 41–51. https://doi.org/10.1080/713610649.
44.
Manasfi, T.; Houska, J.; Gebhardt, I.; et al. 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. https://doi.org/10.1016/j.watres.2023.119751.
45.
Burkholder, J.; Cox, R.; Ravishankara, R. Atmospheric degradation of ozone depleting substances, their substitutes, and related species. Chem. Rev. 2015, 115, 3704−3759. https://doi.org/10.1021/cr5006759.
46.
Lim, S.; Shi, J.; Von Gunten, U.; et al. Ozonation of organic compounds in water and wastewater: A critical review. Water Res. 2022, 213, 118053. https://doi.org/10.1016/j.watres.2022.118053.
47.
Voukides, A.; Konrad, K.; Johnson, R. Competing mechanistic channels in the oxidation of aldehydes by ozone. J. Org. Chem. 2009, 74, 2108–2113. https://doi.org/10.1021/jo8026593.
48.
Li, Z.; Xiang, L.; Pan, S.; et al. The degradation of aqueous oxytetracycline by an O3/CaO2 system in the presence of HCO3−: Performance, mechanism, degradation pathways, and toxicity evaluation. Molecules 2024, 29, 659. https://doi.org/10.3390/molecules29030659.
49.
Ruffinoa, M.; Zanetti, M. Experimental study on the abatement of ammonia and organic carbon with ozone. Desalination Water Treat. 2012, 37, 130–138. https://doi.org/10/5004/dwt.2012.2780.
50.
Hiserodt, R.; Pope, B.; Cossette, M.; et al. Proposed mechanisms for the fragmentation of doubly allylic alkenamides (tingle compounds) by low energy collisional activation in a triple quadrupole mass spectrometer. J. Am. Soc. Mass Spectrom. 2004, 15, 1462–1470. https://doi.org/10.1016/j.jasms.2004.06.009.
51.
Adams, H.; Fanourakis, A.; Dahiya, A.; et al. Allylic amination of alkenyl alcohols: Simultaneous control of chemoselectivity and enantioselectivity in nitrene transfer using ion-paired catalysts. ACS Catal. 2025, 15, 14639–14646. https://doi.org/10.1021/acscatal.5c04140.
52.
Goodson, D. Mathematical Methods for Physical and Analytical Chemistry; J. Wiley and Sons Inc.: Hoboken, NJ, USA, 2011; pp. 1–382.
53.
Otto, M. Chemometrics, 3rd ed.; Wiley: Weinheim, Germany, 2017; pp. 1–383.
54.
Taylor, J. Quality Assurance of Chemical Measurements; Lewis Publishers, Inc.: Chelsea, MI, USA, 1987; pp. 1–328.
55.
Zeleny, R.; Harbeck, S.; Schimmel, H. Validation of a liquid chromatography-tandem mass spectrometry method for the identification and quantification of 5-nitroimidazole drugs and their corresponding hydroxy metabolites in lyophilised pork meat. J. Chromatogr. A 2009, 1216, 249–256. https://doi.org/10.1016/j.chroma.2008.11.061.
56.
Council Directive 96/23/EC of 29 April 1996 on Measures to Monitor Certain Substances and Residues Thereof in Live Animals and Animal Products and Repealing Directives 85/358/EEC and 86/469/EEC and Decisions 89/187/EEC and 91/664/EEC. Available online: https://eur-lex.europa.eu/eli/dir/1996/23/oj/eng (accessed on 13 Decembre 2019).
57.
Pandeti, S.; Feketeová, L.; Reddy, T.; et al. Nitroimidazolic radiosensitizers investigated by electrospray ionization time-of-flight mass spectrometry and density functional theory. RSC Adv. 2017, 7, 45211–45221. https://doi.org/10.1039/c7ra08312b.
58.
Upadyshev, M.; Ivanova, B.; Motyleva, S. Mass spectrometric identification of metabolites after magnetic-pulse treatment of infected Pyrus communis L. Microplants. Int. J. Mol. Sci. 2023, 24, 16776. https://doi.org/10.3390/ijms242316776.
59.
Motyleva, S.; Ivanova, B.; Vinokur, M.; et al. Gas chromatography–mass spectrometry analysis of carbohydrates and linoleic acid in tomato leaves depending on growing conditions: An experimental and theoretical study. In Advances in Biology; Grant, C., Ed.; Nova Science Publishers: New York, NY, USA, 2025; Volume 9. https://doi.org/10.52305/JDWC2175.

Scilight Press

Author Information

Abstract

Keywords

References

About Scilight

Journals

Publishing Policies

Contact Us