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Frontiers of Environmental Science & Engineering

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Front. Environ. Sci. Eng.    2021, Vol. 15 Issue (5) : 106    https://doi.org/10.1007/s11783-021-1394-6
RESEARCH ARTICLE
Identification of resistant pharmaceuticals in ozonation using QSAR modeling and their fate in electro-peroxone process
Majid Mustafa1(), Huijiao Wang2, Richard H. Lindberg1, Jerker Fick1, Yujue Wang2(), Mats Tysklind1
1. Department of Chemistry, Umeå University, Umeå S-90187, Sweden
2. School of Environment, Beijing Key Laboratory for Emerging Organic Contaminants Control, State Key Joint Laboratory of Environmental Simulation and Pollution Control (Ministry of Education), Tsinghua University, Beijing 100084, China
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Abstract

• Effect of converting ozonation to E-peroxone was studied on pharmaceutical removal.

• A QSAR model was developed for selected 89 pharmaceuticals of special concern.

• Both processes abated the pharmaceuticals of moderate and high kO3 quickly.

• E-peroxone process accelerated the elimination of pharmaceuticals with low k O3.

• Developed QSAR model reliably predicted kO3 of 418 out of 491 pharmaceuticals.

The abatements of 89 pharmaceuticals in secondary effluent by ozonation and the electro-peroxone (E-peroxone) process were investigated. Based on the results, a quantitative structure-activity relationship (QSAR) model was developed to explore relationship between chemical structure of pharmaceuticals and their oxidation rates by ozone. The orthogonal projection to latent structure (OPLS) method was used to identify relevant chemical descriptors of the pharmaceuticals, from large number of descriptors, for model development. The resulting QSAR model, based on 44 molecular descriptors related to the ozone reactivity of the pharmaceuticals, showed high goodness of fit (R2 = 0.963) and predictive power (Q2 = 0.84). After validation, the model was used to predict second-order rate constants of 491 pharmaceuticals of special concern ( kO3) including the 89 studied experimentally. The predicted k O3 values and experimentally determined pseudo-first order rate constants of the pharmaceuticals’ abatement during ozonation (kOZ) and the E-peroxone process (kEP) were then used to assess effects of switching from ozonation to the E-peroxone process on removal of these pharmaceuticals. The results indicate that the E-peroxone process could accelerate the abatement of pharmaceuticals with relatively low ozone reactivity ( kO3<~102 M-1·s−1) than ozonation (3–10 min versus 5–20 min). The validated QSAR model predicted 66 pharmaceuticals to be highly O3-resistant. The developed QSAR model may be used to estimate the ozone reactivity of pharmaceuticals of diverse chemistry and thus predict their fate in ozone-based processes.
Keywords Ozone      Electro-peroxone      Wastewater      Quantitative structure activity relationship      Advanced oxidation processes     
Corresponding Author(s): Majid Mustafa,Yujue Wang   
Issue Date: 03 February 2021
 Cite this article:   
Majid Mustafa,Huijiao Wang,Richard H. Lindberg, et al. Identification of resistant pharmaceuticals in ozonation using QSAR modeling and their fate in electro-peroxone process[J]. Front. Environ. Sci. Eng., 2021, 15(5): 106.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-021-1394-6
https://academic.hep.com.cn/fese/EN/Y2021/V15/I5/106
Fig.1  Abatement of the selected set of 89 pharmaceuticals during (a) ozonation and (b) E-peroxone treatment of the selected wastewater. The pharmaceuticals are ordered from left to right along the x-axis in accordance with rapidity of their removal (highest to lowest). (Reaction conditions: volume= 250 mL, concentration of each pharmaceutical= ~1 mg/L, inlet O3 gas phase concentration= 4.8 mg/L, gas flow rate= 0.35 L/min, current= 35 mA).
Compound pKa kO3 (M−1·s−1) k•OHb
(M−1· s–1)		 kOZ
(min–1)		 kEP
(min–1)		 kEP/kOZ
Reported a Predicted
Alfuzosin 1.11 × 105
Alprazolam 2.79 × 10-1 0.458 0.662 1.45
Amitriptyline 2.53 × 102 1.554 1.657 1.07
Atenolol 9.6 c 1.7 × 103 c 2.24 × 103 8 × 109 c 1.004 0.865 0.86
Atorvastatin 1.63 × 104 1.19 × 1010 d
*Atracurium 1.63 × 1011 3.028 3.291 1.09
Azelastine 5.62 × 101 1.499 2.214 1.48
Azithromycin 8.7, 9.5 e 1.1 × 105 e 1.24 × 105 2.9 × 109 e
Beclomethasone 2.49 × 103
Biperiden 2.14 × 103 1.304 0.963 0.74
Bisoprolol 1.83 × 104 1.694 1.189 0.70
Bromocriptine 6.90 × 101
Budesonide 5.10 × 102 0.508 0.583 1.15
Buprenorphine 1.70 × 105
Bupropion 2.16 × 102 3.3 × 109 f 0.779 0.808 1.04
Caffeine 6.5 × 102 g 5.9 × 102 5.9 × 109 h 0.490 0.709 1.45
Carbamazepine 3 × 105 i 8.8 × 109 i 2.959 2.828 0.96
Cilazapril 5.14 × 102 2.056
Ciprofloxacin 6.2, 8.8 e 1.9 × 104 e 3.59 × 104 4.1 × 109 e 1.633 1.626 1.00
Citalopram 1.11 × 103 0.868 1.301 1.50
Clarithromycin 9.0 c 4.0 × 104 c 6.07 × 104 5 × 109 c 2.458 1.842 0.75
Clemastine 6.51 × 102 1.558 2.463 1.58
Clindamycin 7.6 c 4.3 × 106 c 4.97 × 105 1010 c
Clomipramine 1.32 × 102
Clonazepam 2.35 0.362 0.690 1.91
Clotrimazole 8.36 × 101 0.728 1.065 1.46
Codeine 4.82 × 104
Cyproheptadine 1.87 × 102
Desloratadine 4.07 × 101 0.904 1.033 1.14
Diclofenac 4.2 i 1 × 106 i 7.5 × 109 i
Dicycloverine 5.06 × 103
Dihydroergotamine 1.46 × 102
Diltiazem 8.2, 12.9 j 5.65 × 105 8.3 × 109
Diphenhydramine 3.27 × 103 5.42 × 109 d 1.993 1.942 0.97
*Dipyridamole 3.28 × 103
Donepezil 1.67 × 106 1.590 2.523 1.59
Duloxetine 9.7 3.04 × 105 9.72 × 109 f
Eprosartan 4.9 × 105 k 1.00 × 106
Erythromycin 8.4 c 7.9 × 104 c 5.21 × 104 5 × 109 c
Fexofenadine 9 l 9.0 × 103 l 2.26 × 104 0.860 1.016 1.18
Finasteride 1.97 × 102 1.334 1.185 0.89
Flecainide 1.17 × 104 1.714 1.261 0.74
Fluconazole <1 c 0.69 4.6 × 109 c 0.207 0.501 2.43
Flunitrazepam 1.70 0.300 0.487 1.62
Fluoxetine 8.7 m 2.5 × 104 6.14 × 103 8.4 × 109 n 0.974 1.439 1.48
*Flupentixol 1.68 × 107
*Fluphenazine 3.24 × 107
*Glibenclamide 5.77 × 103 0.625
*Glimepiride 1.53 × 103 0.474 0.693 1.46
Haloperidol 6.39 × 103 2.440 2.190 0.90
*Hydroxyzine 2.87 × 103 2.376
Irbesartan 2.4 × 101 k 6.99 1010 0.326 0.631 1.93
Levomepromazine 2.07 × 107
Loperamide 1.29 × 102 2.650 2.391 0.90
Maprotiline 4.03 × 102 0.765 0.525 0.69
Memantine 7.75 0.474 0.843 1.78
Metoprolol 9.7 o 2.0 × 103 c 1.37 × 104 7.3 × 109 p 1.268 0.980 0.77
Mianserin 6.31 × 102
Mirtazapine 1.25 × 103
Naloxone 7.06 × 104
*Nefazodone 1.01 × 103
Norfloxacin 8.8 c 1.9 × 104 c 3.14 × 104 5 × 109 c 1.900 2.007 1.06
Ofloxacin 7.9 q 1.95 × 106 r 4.70 × 105 4.2 × 109 r 3.480 4.103 1.18
Orphenadrine 2.84 × 103 2.272 1.759 0.77
Oxazepam ~1 c 1.55 9.1 × 109 c 0.518 0.797 1.54
*Oxytetracycline 1.48 × 106 6.96 × 109 s 2.606
Paracetamol 2.57 × 106 t 4.94 × 109 t 4.472 4.635 1.04
Paroxetine 6.91 × 104 9.6 × 109 f
*Perphenazine 3.75 × 106
Pizotifen 6.44 × 103 2.545
*Promethazine 1.67 × 106
Propranolol 9.5 c 1 × 105 c 1.95 × 104 1010 p
Ranitidine 8.2 c 4.1 × 106 c 2.01 × 107 1010 c
Repaglinide 6.31 × 103
Risperidone 2.25 × 103 2.828
Rosuvastatin 5.02 × 104 0.785 0.918 1.17
Roxithromycin 9.2 e 6.3 × 104 e 3.88 × 104 5.4 × 109 e
Sertraline 1.60 × 101
Sotalol 9.4 c 1.9 × 104 c 6.06 × 104 ~1010 c
Sulfamethoxazole 5.6 o 5.5 × 105 e 1.72 × 105 5.5 × 109 e
Telmisartan 1.2 × 105 k 4.29 × 104 0.702 0.823 1.17
Terbutaline 8.6 u 1.23 × 105 6.87 × 109 j
*Tetracycline 3.3,7.7,9.7 e 1.9 × 106 e 1.63 × 106 7.7 × 109 e 3.205
Tramadol 9.4 c 4.0 × 103 c 1.57 × 104 6.3 × 109 v 0.856 0.981 1.15
Trihexyphenidyl 2.02 × 103 1.192 1.008 0.85
Trimethoprim 3.2, 7.1 o 4.1 × 105 c 4.57 × 105 6.9 × 109 e 3.398
Venlafaxine 9.4 c 8.5 × 103 c 1.60 ×104 1010 c 0.842 0.977 1.16
Verapamil 9.7 c 2.7 × 106 c 3.74 × 106 1010 c
Zolpidem 1.01 × 103
Tab.1  pKa values, previously published and QSAR model-predicted ozone rate constants ( kO3), pseudo-first order rate constants obtained for abatement during ozonation (kOZ) and the E-peroxone process (kEP), and kEP/kOZ ratios for the tested pharmaceuticals
Fig.2  Correlation between the literature-reported and QSAR model-predicted second-order rate constants for the reaction of O3 with compounds used for model training. The solid line represents the linear regression line obtained, while the lower and upper dashed lines represent the prediction error ranges of factors of 1/3 and 3, respectively.
Fig.3  VIP plot showing predictive descriptors (Table S6) in order from highest to lowest importance to kO3. Descriptors shown as white column bars were positively and descriptors as gray column bars were negatively correlated to k O3.
Fig.4  The ratio of pseudo-first order rate constants for pharmaceutical abatement during the E-peroxone process and ozonation treatment (kEP/kOZ) in relation to the second-order rate constant for pharmaceutical reaction with ozone ( k O3) predicted by the QSAR model.
Pharmaceutical kO3 (M−1·s−1) Pharmaceutical kO3 (M−1·s−1) Pharmaceutical kO3 (M−1·s−1)
Ciclosporin 7.5 × 10-5* Ribavirin 6.58 Lomustine 42.1
Triazolam 0.0574 Praziquantel 6.60 Loratadine 47.6
Lorazepam 0.28 Irbesartan 6.99** Dihydroergotamine 52.0
Alprazolam 0.28** Ethosuximide 7.13 Guanethidine 55.0
Clobazam 0.67 Memantine 7.75** Azelastine 56.2**
Fluconazole 0.69** Felbamate 10.4 Itraconazole 60.8*
Anastrozole 1.23 Nitrazepam 13.0 Lisinopril 67.1
Oxazepam 1.55** Zidovudine 15.2 Piperacillin 68.0
Letrozole 1.69 Sertraline 16.0** Bromocriptine 69.0**
Flunitrazepam 1.70** Methohexital 16.2 Mercaptopurine 80.7
Methenamine 2.0* Levosimendan 16.9* Domperidone 81.5
Clonazepam 2.35** Phenobarbital 19.2 Clotrimazole 83.6**
Clonidine 2.52 Mitotane 20.7 Flucytosine 84.2
Carisoprodol 2.86 Moxonidine 20.9 Efavirenz 84.9
Amiloride 3.01 Dihydralazine 24.6 Baclofen 91.0
Amantadine 3.45 Chlordiazepoxide 29.3 Caspofungin 93.1*
Voriconazole 3.56 Streptomycin 29.3* Gemcitabine 93.1
Proguanil 3.88 Melagatran 33.4 Nortriptyline 93.8
Artemether 4.75 Pyrimethamine 36.4 Oxcarbazepine 94.4
Pentobarbital 4.88 Ketamine 39.2 Quinaprilate 94.4
Midazolam 5.46 Desloratadine 40.7** Alfentanil 96.2
Anagrelide 6.08 Isoflurane 41.9 Loracarbef 96.9
Tab.2  The 66 O3-resistant pharmaceuticals with k O3<100 M−1·s−1 predicted by the QSAR model
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