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The role of microorganisms in the removal of pharmaceutical and personal care products
The role of microorganisms in the removal of pharmaceutical and personal care products
15
Dong Zhang Hangzhou Dianzi University, Hangzhou, P.R. China
Pharmaceutical and personal care products (PPCPs) have been a worldwide concern over the past two decades due to their extensive applications (including medicine, industry, livestock farming, aquaculture, and people’s daily life) and adverse impacts to wildlife and people (Pan et al., 2009; Oulton et al., 2010; Evgenidou et al., 2015; Paredes et al., 2016). Based on their various purposes, PPCPs can be classified into several classes (Wang and Wang, 2016), including hormones (e.g., estriol, estrone, and 17β-estradiol), antibiotics [e.g., sulfamethoxazole (SMX)], lipid regulators, nonsteroidal antiinflammatory drugs, β-blockers, antidepressants (e.g., diazepam, and amitriptyline), anticonvulsants, antineoplastic, diagnostic contrast media, fragrances, preservatives, disinfectants (e.g., triclosan), and sunscreen agents (e.g., oxybenzone). The worldwide production of these PPCPs can reach up to 2 3 107 tons per year. These micropollutants can be detected ubiquitously in soil, wastewater, surface water, and drinking water as well as having similar environmental behaviors with persistent organic pollutants (POPs) and are also called pseudo-POPs. Wastewater is often recognized as one of the important sinks of PPCPs, and various treatment technologies such as activated sludge treatment, fungal biodegradation, nanofiltration, reverse osmosis, and advance oxidation methods have been focused on for the removal of PPCPs from wastewater (Joss et al., 2004; Jelic et al., 2011; Dialynas and Diamadopoulos, 2012). Moreover, the type of biological treatment plays a vital role in the removal, transformation, and transportation of the PPCPs’ environmental fate and behavior. Activated sludge systems are adopted as the main conventional biological treatment unit to remove or degrade organic pollutants (including PPCPs) from wastewater in wastewater treatment plants (WWTPs). It has the capacity to remove PPCPs from wastewater, but the removal efficiency of PPCPs has changed greatly, depending on the physiochemical properties of the targeted compounds as well as the environmental conditions such as the biological reactor configuration and operational parameters [hydraulic retention time (HRT), sludge retention time, and pH] (Dialynas and Diamadopoulos, 2012; Wang and Wang, 2016).
Pharmaceuticals and Personal Care Products: Waste Management and Treatment Technology. DOI: https://doi.org/10.1016/B978-0-12-816189-0.00015-9 © 2019 Elsevier Inc. All rights reserved.
342
Pharmaceuticals and Personal Care Products: Waste Management and Treatment Technology
Several literature studies have reviewed the removal efficiency of PPCPs by biological treatment (Miege et al., 2009; Verlicchi et al., 2012; Wang and Wang, 2016). However, the specific roles of biological processes such as adsorption, transmembrane process, and intercellular enzymatic degradation have not been discussed. During the removal and biotransformation of PPCPs in wastewaters, several interfacial processes can be recognized (Zhang and Zhu, 2014): (1) sorption of PPCP molecules onto biomass surface from the wastewater aqueous phase (Wells, 2006; Dialynas and Diamadopoulos, 2012; Vasiliadou et al., 2013; Blair et al., 2015); (2) transmembrane process from the outer surface of biological cells into the internal matrix (Song et al., 2010; Zhang et al., 2013) which is a ratelimited process in the biodegradation kinetics of organic compounds, especially for hydrophobic compounds, due to the cytoplasmic membrane lipids acting as a “barrier” (Hearn et al., 2009; Wiener and Horanyi, 2011); and (3) biotransformation or mineralization of PPCPs in biomass cells facilitating with intercellular enzymes (Kagle et al., 2009; Onesios et al., 2009; Alvarino et al., 2016). In this chapter, we mainly discuss the biological sorption of PPCPs by biosolids, transmembrane transport, and underlying mechanisms of PPCPs, typical intercellular catalytic degradation pathways of typical PPCPs, as well as the role of each biological process or interfacial behavior in the bioremoval and biotransformation of PPCPs in wastewater.
15.1
Biological sorption of typical pharmaceutical and personal care products
15.1.1 Biological sorption of pharmaceutical and personal care products During the passage through the WWTPs, PPCPs in influents can be significantly reduced by two processes: sorption and biodegradation (Dialynas and Diamadopoulos, 2012; Jelic et al., 2011; Blair et al., 2015; Fernandez-Fontaina, et al., 2013). The sorption of organic pollutants including PPCPs by biosolids or biomass can be recognized as a branch of biotechnology that effectively reduces chemical concentrations in wastewater influents, and as a crucial step for the subsequent transmembrane transport and intercellular biodegradation (Bokbolet et al., 1999; Zhang et al., 2018). Furthermore, the high frequency of PPCPs and their metabolites in biosolids have been measured (Miao et al., 2005; McClellan and Halden, 2010). Sorption of these micropollutants onto sludge/biosolids may also represent their significant route into surface water or soil, or when improper treatment of sludge occurs or is used as fertilizer on agricultural land (Ternes et al., 2004). Sorption is designated as a removal mechanism for many compounds, and knowledge of the sorption properties of PPCPs on biosolids will allow for a better understanding of their fate and impact to the environment. However, the little information on the sorption of PPCPs during wastewater treatment available is also
The role of microorganisms in the removal of pharmaceutical and personal care products
343
confusing. Furthermore, not much is known about the occurrence and behavior of PPCPs in biosolids during WWTPs and its impact on the fate of these micropollutants. The sorption of hydrophobic organic compounds—especially nonpolar organic compounds such as polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and organic pesticides—has been extensively studied for soil, sewage sludge, microorganisms, and nanoparticles (Steen and Karickhoff, 1984; Tsezos and Bell, 1989; Chung et al., 2007; Li et al., 2007; Carballa et al., 2008; Chen et al., 2010; Lin and Gan, 2011; Zhuang et al., 2011; Zhang and Zhu, 2012; Yu et al., 2013; Zhang et al., 2018). The partition/distribution coefficient (Kd) and carbon normalized distribution coefficient (Koc 5 Kd/foc, where foc is the carbon content of the sorbent) are considered as good parameters to evaluate the sorption property of hydrophobic organic compounds (HOCs) from aqueous solutions onto solid phase or matrix. Linear correlations can be often observed between log Kd (or log Koc) with log Kow for tested organic pollutants on particular sorbents, where Kow is the octanol water distribution coefficient and represents the hydrophobicity of organic pollutants. Using these correlations, we can evaluate the sorption and fate of various pollutants in wastewater during their treatment. For example, based on reasonable assumptions for nonspecific lipophilic interactions, several relationships between log Kd (or log Koc) and log Kow were obtained and could be used to predict the sorption behavior for various hydrophobic organic compounds and microorganism-derived biosorbents (Zhang et al., 2018): G
G
G
G
Bacterial biomass for PAHs: log Kd 5 1.01 log Kow 2 0.74 (Zhang et al., 2018). Bacterial suspensions for unsubstituted and methylated PAHs: log Kd 5 0.98( 6 0.36) log Kow 2 2.71( 6 1.55) (Dimitriou-Christidis et al., 2007). White-rot fungi for PAHs: log Koc 5 1.13 log Kow 2 0.84 (Chen et al., 2010). River sediments for PAHs and their derivatives: log Koc 5 1.00 log Kow 2 0.21 (Karickhoff et al., 1979).
Furthermore, the sorption process could facilitate the subsequent biodegradation efficiency. Previous work has indicated that the improvement of sorption Kd of pyrene resulted in an improvement of its biodegradation efficiency (B ) by a Klebsiella oxytoca strain, as shown in Fig. 15.1 (Zhang et al., 2013; Zhang, 2013). The sorption process controls the removal, biotransformation, fate, and eco-risk of PPCPs in sewage treatment plants and other aquatic ecosystems. If we can predict the sorption behavior simply by Kd values or other sorption parameters, this would lower the economic and time costs for analyses of sludge and other biosolid samples. However, for pseudo-hydrophobic organic compounds (the log Kow of PPCPs ranges from 22.64 to 7.1), sorption data collected from recent works with available Kd or Koc parameters on PPCPs was insufficient (only 273 valid data were obtained) and showed significant deviations from their Kow, as shown in Fig. 15.2 and Table 15.1. The plots of log Kd with log Kow do not show a good relationship, which is consistent with previous studies (Wells, 2006). Moreover, the values of sorption parameter for a certain compound would be totally different and cover a large range in different studies. For example, the sorption parameter (log Kd) of
344
Pharmaceuticals and Personal Care Products: Waste Management and Treatment Technology
Figure 15.1 The relationship between apparent biodegradation efficiency (B ) with apparent sorption affinity parameter Kd by a Klebsiella oxytoca strain.
Figure 15.2 The relationship of log Kd with log Kow of target PPCPs (data directly obtained and indirectly calculated from the literature is shown in Table 15.1). PPCP, Pharmaceutical and personal care product.
carbamazepine (CBZ) onto different sludges in different bioreactors ranged from 22.00 to 2.44 L/kg (Jones et al., 2002; Ternes et al., 2004; Miao et al., 2005; Carballa et al., 2008; Jelic et al., 2011; Conkle et al., 2012; Dialynas and Diamadopoulos, 2012; Fernandez-Fontaina et al., 2013; Yu et al., 2013; Vasiliadou
Table 15.1 Sorption properties of pharmaceutical and personal care products onto biosolids Compounds
MWa
log Kow
pKa
10,11-Dihydroxycarbamazepine 17α-Ethinylestradiol
270.3 296.4
0.13 4.20
10.4
17α-Ethinylestradiol
296.4
4.20
10.4
17α-Ethinylestradiol
296.4
4.20
10.4
17α-Ethinylestradiol 17α-Ethinylestradiol 17α-Ethinylestradiol 17α-Ethinylestradiol 17β-Estradiol
296.4 296.4 296.4 296.4 272.4
4.20 4.20 4.20 4.20 3.90
10.4 10.4 10.4 10.4 10.4
17β-Estradiol
272.4
3.90
10.4
17β-Estradiol
272.4
3.90
10.4
17β-Estradiol
272.4
3.90
10.4
17β-Estradiol 17β-Estradiol 2-Hydroxycarbamazepine 3-Hydroxycarbamazepine 4-Androstene-3,17-dione 4-Tert-octylphenol 4-Tert-octylphenol 4-Tert-octylphenol Acetaminophen
272.4 272.4 252.3 252.3 286.4 206.3 206.3 206.3 151.2
3.90 3.90 2.25 2.41 2.75e 5.00 5.00 5.00 0.46f
10.4 10.4
9.4g
Bioreactor
log Kd (L/kg)
log Koc (L/kg)
Primary sludge Mesophilic digested sludge Thermophilic digested sludge Immersed membrane bioreactor Primary sludge Secondary sludge CASb UASBc Mesophilic digested sludge Thermophilic digested sludge Immersed membrane bioreactor Sludge
0.84 2.53
3.40
Miao et al. (2005) Carballa et al. (2008)
2.13
2.95
Carballa et al. (2008)
CAS UASB Primary sludge Primary sludge CAS Soil IVC Soil PDS Soil WPL CAS
pH
log Dow
kbiol (L/g/day)
2.99 2.44 2.54 2.30 2.48 2.66
2.90 2.93 7.00 0.040 3.53
Reference
Dialynas and Diamadopoulos (2012) Ternes et al. (2004) Ternes et al. (2004) Alvarino et al. (2014) Alvarino et al. (2014) Carballa et al. (2008)
n.d.d
Carballa et al. (2008)
2.34
Dialynas and Diamadopoulos (2012) Stuer-Lauridsen et al. (2000) Alvarino et al. (2014) Alvarino et al. (2014) Miao et al. (2005) Miao et al. (2005) Blair et al. (2015) Yu et al. (2013) Yu et al. (2013) Yu et al. (2013) Blair et al. (2015)
3.17 2.90 2.40 1.43 1.36 3.26 1.28 1.07 1.63 1.92
19 0.070
3.15 3.46 3.15
(Continued)
Table 15.1 (Continued) Compounds
MWa
log Kow
pKa
Bioreactor
log Kd (L/kg)
Acetylsalicylic acid
180.2
1.19
3.5
Sludge
0.34
Acetylsalicylic acid Allopurinol Amlodipine
180.2 136.1 408.9
1.19 2 0.55 3.00
3.5 9.3 9.4h
Primary sludge Primary sludge Sludge
0.35 2 1.40 2.16
1.00 1.29
Amoxycillin
365.4
0.87
Primary sludge
0.03
2.94
Ampicillin
349.4
1.35g
CAS
1.48
Atenolol Atenolol Atorvastatin
266.3 266.3 588.7
0.16 0.16 6.36j
Azithromycin Azithromycin Azithromycin Bendroflumethiazide
749.0 749.0 749.0 421.4
4.02g 4.02g 4.02g 1.19
3.2 11.7h 2.5 7.3g 9.2 9.2 4.3 14.9h 8.7g 8.7g 8.7g 8.5g
Bezafibrate Bisphenol A Bisphenol A Bisphenol A Budesonide
361.8 228.3 228.3 228.3 430.5
3.80 3.32e 3.32e 3.32e 1.36
9.6k 9.6k 9.6k
Caffeine Caffeine
194.2 194.2
2 0.07e 2 0.07e
10.4e 10.4e
log Koc (L/kg)
pH
log Dow
kbiol (L/g/day)
Reference Stuer-Lauridsen et al. (2000) Jones et al. (2002) Jones et al. (2002) Stuer-Lauridsen et al. (2000) Jones et al. (2002)
2 0.552
Blair et al. (2015)
Primary sludge WWTP1 WWTP1
2 0.68 1.67 3.15
2.17 1.79 3.27
CAS CAS Primary sludge Sludge
2.58 2.11 2.56 0.34
2.97
WWTP1 Soil IVC Soil PDS Soil WPL Sludge
2.23 0.83 0.70 0.94 0.52
2.36 2.70 3.09 2.46
CAS Suspended growth reactor
2.15 2 1.22
7.3 7.3
0.157 2 1.75i 2 1.24i
Jones et al. (2002) Jelic et al. (2011) Jelic et al. (2011)
7.3
0.100
Go¨bel et al. (2005) Blair et al. (2015) Ivanova´ et al. (2017) Stuer-Lauridsen et al. (2000) Jelic et al. (2011) Yu et al. (2013) Yu et al. (2013) Yu et al. (2013) Stuer-Lauridsen et al. (2000) Blair et al. (2015) Vasiliadou et al. (2013)
Caffeine Carbamazepine Carbamazepine Carbamazepine
194.2 236.3 236.3 236.3
2 0.07e 2.45 2.45 2.45
10.4e 13.9 13.9 13.9
Carbamazepine
236.3
2.45
13.9
Carbamazepine Carbamazepine Carbamazepine
236.3 236.3 236.3
2.45 2.45 2.45
13.9 13.9 13.9
Carbamazepine
236.3
2.45
13.9
Carbamazepine Carbamazepine
236.3 236.3
2.45 2.45
13.9 13.9
Carbamazepine
236.3
2.45
13.9
Carbamazepine Carbamazepine Carbamazepine Carbamazepine Carbamazepine Carbamazepine Celestolide
236.3 236.3 236.3 236.3 236.3 236.3 244.4
2.45 2.45 2.67l 2.45 2.45 2.45 5.00
13.9 13.9 13.9 13.9 13.9 13.9
Celestolide
244.4
Celestolide Celestolide Chlortetracycline Cimetidine Ciprofloxacin
244.4 244.4 478.9 252.3 331.3
A2/O reactor Primary sludge Secondary sludge Mesophilic digested sludge Thermophilic digested sludge Primary sludge WWTP1 Continuous membrane reactor Batch reactor
2.90 n.d. 0.08 1.55
n.d. 0.54 2.42
Ashfaq et al. (2017) Ternes et al. (2004) Ternes et al. (2004) Carballa et al. (2008)
1.31
2.12
Carballa et al. (2008)
1.41 1.67 0.43
3.59 1.79 1.24
7.3 7.0
2.25 2 4.15i 2 3.05i
0.00
0.44
8.4
2 4.45i
1.00 2.25
5.00
CAS Immersed membrane reactor Suspended growth reactor Aerobic wetland Anaerobic wetland Primary sludge Soil IVC Soil PDS Soil WPL Continuous membrane reactor Batch reactor
5.00 5.00 2 1.30 0.40 2 1.74
CAS UASB A2/O reactor Primary sludge Sludge
6.8 6.1
2 2.00
0.01 0.01
0.09024
1.07 0.99 2.44 0.40 0.16 0.50 3.77
2.27 2.55 2.02 4.58
7.0
6.13
3.71
4.15
8.4
9.11
3.48 3.08 5.59 2 0.44 2 2.59
1.56 1.48
63 0.05 2.84
2 0.0124
Jones et al. (2002) Jelic et al. (2011) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013) Blair et al. (2015) Dialynas and Diamadopoulos (2012) Vasiliadou et al. (2013) Conkle et al. (2012) Conkle et al. (2012) Miao et al. (2005) Yu et al. (2013) Yu et al. (2013) Yu et al. (2013) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013) Alvarino et al. (2014) Alvarino et al. (2014) Ashfaq et al. (2017) Jones et al. (2002) Stuer-Lauridsen et al. (2000)
(Continued)
Table 15.1 (Continued) Compounds
MWa
log Kow
pKa
Bioreactor
log Kd (L/kg)
Ciprofloxacin Ciprofloxacin Ciprofloxacin Citalopram
331.3 331.3 331.3 324.4
2 1.74 2 1.74 2 1.74 2.86
6.1 6.1 6.1
CAS Primary sludge A2/O reactor Sludge
2.70 4.40 2.02 2.02
Citalopram Clarithromycin Clarithromycin Clarithromycin Clarithromycin Clofibric acid Clofibric acid Clofibric acid
324.4 748.0 748.0 748.0 748.0 214.6 214.6 214.6
2.86 3.16m 3.16m 3.16m 3.16m 2.57 2.57 2.57
3.20 2.42 2.14 2.11 2.85 n.d. 0.68 2.30
Codeine Cotinine Cyclophosphamide Cyclophosphamide Danofloxacin Diazepam Diazepam Diazepam
176.2 176.2 261.1 261.1 357.4 284.7 284.7 284.7
1.19g 0.07g 0.63 0.63 2 0.30 2.82 2.82 2.85
10.6n
Primary sludge CAS WWTP1 CAS Primary sludge Primary sludge Secondary sludge Immersed membrane bioreactor CAS CAS Primary sludge Secondary sludge A2/O reactor Primary sludge Secondary sludge Sludge
Diazepam Diazepam
284.7 284.7
2.85 2.85
3.3 3.3
Diazepam
284.7
2.85
3.3
9.0m 9.0m 9.0m 9.0m
3.3 3.3 3.3
WWTP1 Continuous membrane reactor Batch reactor
1.15 1.53 1.74 0.38 3.59 1.64 1.32 2.01
log Koc (L/kg)
2.82 2.26
pH
log Dow
7.3
1.45i
kbiol (L/g/day)
1.15
2.20 0.85
3.15 1.51
3.27 2.32
7.3 7.0
2 1.15i 2 0.85i
0
1.70
2.14
8.4
2 2.25i
0.19
Reference Blair et al. (2015) Ivanova´ et al. (2017) Ashfaq et al. (2017) Stuer-Lauridsen et al. (2000) Ivanova´ et al. (2017) Go¨bel et al. (2005) Jelic et al. (2011) Blair et al. (2015) Ivanova´ et al. (2017) Ternes et al. (2004) Ternes et al. (2004) Dialynas and Diamadopoulos (2012) Blair et al. (2015) Blair et al. (2015) Ternes et al. (2004) Ternes et al. (2004) Ashfaq et al. (2017) Ternes et al. (2004) Ternes et al. (2004) Stuer-Lauridsen et al. (2000) Jelic et al. (2011) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013)
Diazepam Diazepam Diclofenac Diclofenac Diclofenac
284.7 284.7 296.1 296.1 296.1
2.85 2.85 4.60 4.60 4.50
3.3 3.3 4.2 4.2 4.2
CAS UASB Primary sludge Secondary sludge Mesophilic digested sludge Thermophilic digested sludge Primary sludge WWTP1 Continuous membrane reactor Batch reactor
1.48 2.60 2.66 1.20 1.82
Diclofenac
296.1
4.50
4.2
Diclofenac Diclofenac Diclofenac
296.1 296.1 296.1
0.70 4.50 4.50
4.2 4.2 4.2
Diclofenac
296.1
4.50
4.2
Diclofenac Digoxigenin Digoxin
296.1 390.5 780.9
4.50 1.10e 1.26
Diltiazem Diltiazem hydrochloride Diphenhydramine Enalapril
414.5 451.0 255.4 376.5
2.70e 2.70 3.27e 4.22
Erythromycin Erythromycin
733.9 733.9
Erythromycin Erythromycin Erythromycin Erythromycin Erythromycin
0.4 0.004 3.12 1.67 2.68
Alvarino et al. (2014) Alvarino et al. (2014) Ternes et al. (2004) Ternes et al. (2004) Carballa et al. (2008)
1.79
2.61
Carballa et al. (2008)
2 0.14 2.09 1.89
2.92 2.22 2.71
7.3 7.0
2 2.12 1.40i 1.70i
0.1
1.51
1.95
8.4
0.30i
0.02
4.2
A2/O reactor CAS Sludge
3.06 2.18 0.41
CAS Primary sludge CAS Sludge
1.34 1.86 2.23 3.38
3.06 3.06
8.1o 7.7 9.0f 3.0 5.4p 8.9 8.9
2.22 1.01
1.00 1.82
733.9
3.06
8.9
Primary sludge Continuous membrane reactor Batch reactor
1.45
1.89
733.9 733.9 733.9 733.9
3.06 3.06 3.06 3.06
8.9 8.9 8.9 8.9
CAS UASB Primary sludge A Primary sludge B
1.70 1.48 2.01 1.98
3.98
2.62
7.0 8.4
2.13 2.44i
0.31 0.52 3 0.001
Jones et al. (2002) Jelic et al. (2011) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013) Ashfaq et al. (2017) Blair et al. (2015) Stuer-Lauridsen et al. (2000) Blair et al. (2015) Jones et al. (2002) Blair et al. (2015) Stuer-Lauridsen et al. (2000) Jones et al. (2002) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013) Alvarino et al. (2014) Alvarino et al. (2014) Xu et al. (2007) Xu et al. (2007)
(Continued)
Table 15.1 (Continued) Compounds
MWa
log Kow
pKa
Bioreactor
log Kd (L/kg)
Erythromycin Erythromycin
733.9 733.9
3.06 3.06
8.9 8.9
Primary sludge C Primary sludge D
2.23 2.38
Xu et al. (2007) Xu et al. (2007)
Estrogen
272.4
5.07
5.7
Sludge
4.23
Estrone
270.4
4.10
10.4
2.48
3.35
Estrone
270.4
4.10
10.4
2.18
3.00
Carballa et al. (2008)
Estrone Estrone Fenoprofen Ferrous sulfate Fluoxetine
270.4 270.4 242.3 151.9 309.3
4.10 4.10 3.10g 2 0.37 4.05g
10.4 10.4 7.3p
Fluoxetine
309.3
4.05g
Mesophilic digested sludge Thermophilic digested sludge CAS UASB A2/O reactor Primary sludge Continuous membrane reactor Batch reactor
Stuer-Lauridsen et al. (2000) Carballa et al. (2008)
n.d.
Fluoxetine Fluoxetine Fluoxetine Furosemide
309.3 309.3 309.3 330.7
4.05g 4.05g 4.05g 2.03
CAS CAS UASB Sludge
3.08 3.40 2.85 1.19
Furosemide Galaxolide Galaxolide Galaxolide
330.7 254.8 254.8 254.8
2.03 5.90 5.90 5.90
2.24 3.69 3.26 4.12
2.37 4.15 3.72 4.99
Galaxolide
254.8
5.90
WWTP1 Primary sludge Secondary sludge Mesophilic digested sludge Thermophilic digested sludge
3.83
4.64
3.9 3.9
2.18 2.48 3.91 2 1.22 2.55
log Koc (L/kg)
1.16 3.36
pH
log Dow
kbiol (L/g/day)
Reference
2 0.04
Alvarino et al. (2014) Alvarino et al. (2014) Ashfaq et al. (2017)
7.0
1.98
8.4
n.d.
Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013)
10 0.003
7.3
2 1.37i
Alvarino et al. (2014) Alvarino et al. (2014) Stuer-Lauridsen et al. (2000) Jelic et al. (2011) Ternes et al. (2004) Ternes et al. (2004) Carballa et al. (2008) Carballa et al. (2008)
Galaxolide
254.8
5.90
Continuous membrane reactor
3.27
4.08
7.0
0.9
Fernandez-Fontaina et al. (2013)
Galaxolide
254.8
5.90
Batch reactor
3.21
6.65
8.4
1.71
Galaxolide Galaxolide Galaxolide
254.8 254.8 254.8
5.90 5.90 5.90
CAS UASB Secondary sludge
3.41 3.57 3.20
Gemfibrozil Gemfibrozil Gemfibrozil Gemfibrozil Gemfibrozil Gemfibrozil Glibenclamide Glibenclamide Gliclazide Hydrochlorthiazide
250.3 250.3 250.3 250.3 250.3 250.3 494.0 494.0 323.4 297.7
4.77j 4.77j 4.77j 4.77j 4.77j 4.77j 4.70j 4.70j 2.60g 2 0.07g
0.95 0.32 0.42 0.41 0.19 0.60 3.15 3.58 1.25 2 1.00
Ibuprofen Ibuprofen Ibuprofen
206.3 206.3 206.3
3.50 3.50 4.00
7.9 9.2p 4.5 4.5 4.5
CAS Aerobic wetland Anaerobic wetland Soil IVC Soil PDS Soil WPL WWTP1 A2/O reactor Primary sludge Sludge
n.d. 0.85 1.58
1.32 2.44
Ibuprofen
206.3
4.00
4.5
1.32
2.14
Carballa et al. (2008)
Ibuprofen Ibuprofen
206.3 206.3
3.50 3.50
4.9 4.5
Primary sludge Secondary sludge Mesophilic digested sludge Thermophilic digested sludge Primary sludge Sludge
Fernandez-Fontaina et al. (2013) Alvarino et al. (2014) Alvarino et al. (2014) Artola-Garicano et al. (2003) Blair et al. (2015) Conkle et al. (2012) Conkle et al. (2012) Yu et al. (2013) Yu et al. (2013) Yu et al. (2013) Jelic et al. (2011) Ashfaq et al. (2017) Jones et al. (2002) Stuer-Lauridsen et al. (2000) Ternes et al. (2004) Ternes et al. (2004) Carballa et al. (2008)
2.66 2.66
2.60
Ibuprofen
206.3
3.50
4.5
2.05
2.86
7.0
0.85i
38.07
Ibuprofen
206.3
3.50
4.5
Continuous membrane reactor Batch reactor
1.38
1.82
8.4
2 0.55i
6.03
Jones et al. (2002) Stuer-Lauridsen et al. (2000) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013)
4.5q 4.5q 4.5q 4.5q 4.5q 4.5q
41 0.01 0.071
0.8 2.28 2.58 2.12 3.27
7.3
1.67i
4.10
(Continued)
Table 15.1 (Continued) Compounds
MWa
log Kow
pKa
Bioreactor
log Kd (L/kg)
Ibuprofen Ibuprofen Ibuprofen Ibuprofen Ibuprofen Ibuprofen Ifosfamide Ifosfamide Iopromide
206.3 206.3 206.3 206.3 206.3 206.3 261.1 261.1 791.1
3.50 3.50 3.50 3.50 3.50 3.50 0.86 0.86 2 2.30
4.5 4.5 4.5 4.5 4.5 4.5
2.23 2.38 2.00 0.39 2 0.34 2.43 1.34 0.15 0.84
1.79 0.61 1.71
Blair et al. (2015) Alvarino et al. (2014) Alvarino et al. (2014) Conkle et al. (2012) Conkle et al. (2012) Ashfaq et al. (2017) Ternes et al. (2004) Ternes et al. (2004) Carballa et al. (2008)
Iopromide
791.1
2 2.30
9.9
0.46
1.28
Carballa et al. (2008)
Ketoprofen Lorazepam Mebeverine hydrochloride Mefenamic acid Mefenamic acid Mefenamic acid Mesalazine
254.3 321.2 466.0 241.3 241.3 241.3 153.1
3.12g 2.39g 3.82 5.12 5.12 5.12 0.98
4.45g 13.0g
CAS CAS UASB Aerobic wetland Anaerobic wetland A2/O reactor Primary sludge Secondary sludge Mesophilic digested sludge Thermophilic digested sludge A2/O reactor WWTP1 Primary sludge Primary sludge WWTP1 A2/O reactor Primary sludge
Metformin Metformin hydrochloride Methyl paraben N,N-diethyl-meta-toluamide N,N-diethyl-meta-toluamide Naproxen
129.2 165.6 155.2 191.3 191.3 230.3
2 2.64j 2 2.64 2.02e 2.02 e 3.18
4.2
0.48 2 3.52 3.85 1.28 0.32 1.03
1.76 0.80 1.90
Blair et al. (2015) Jones et al. (2002) Ashfaq et al. (2017) Conkle et al. (2012) Conkle et al. (2012) Carballa et al. (2008)
Naproxen
230.3
3.18
4.2
CAS Primary sludge A2/O reactor Aerobic wetland Anaerobic wetland Mesophilic digested sludge Thermophilic digested sludge
1.09
1.91
Carballa et al. (2008)
9.9
4.2g 4.2g 4.2g 2.09 5.26g 12.4q 12.4
3.80 2.34 2.98 4.28 2.71 3.12 0.14
log Koc (L/kg)
pH
log Dow
kbiol (L/g/day) 2 0.007
0.88 0.15
2.47 5.82 2.66 2.84
7.3
2 3.31i
7.3
2.02i
1.00
2.02
2 2.64
Reference
Ashfaq et al. (2017) Jelic et al. (2011) Jones et al. (2002) Jones et al. (2002) Jelic et al. (2011) Ashfaq et al. (2017) Jones et al. (2002)
Naproxen Naproxen
230.3 230.3
3.18 3.18
4.2 4.2
Naproxen
230.3
3.18
4.2
Naproxen Naproxen Naproxen Nitrazepam
230.3 230.3 230.3 281.3
3.18 3.18 3.18 2.25
4.2 4.2 4.2
Norfloxacin
319.3
0.46p
Norfloxacin
319.3
0.46p
Norfloxacin
319.3
0.46p
Norfloxacin
319.3
0.46p
Norfloxacin
319.3
0.46p
Norfloxacin
319.3
0.46p
Octocrylene Ofloxacin
361.5 361.4
7.10 2 0.39e
Ofloxacin
361.4
2 0.39e
Ofloxacin
361.4
2 0.39e
Ofloxacin
361.4
2 0.39e
Ofloxacin
361.4
2 0.39e
6.3 8.8p 6.3 8.8p 6.3 8.8p 6.3 8.8p 6.3 8.8p 6.3 8.8p 6.0 9.3r 6.0 9.3r 6.0 9.3r 6.0 9.3r 6.0 9.3r
Primary sludge Continuous membrane reactor Batch reactor
2.34 1.55
2.54 2.36
7.0
0.33 0.38i
4.23
CAS
2.28
Jones et al. (2002) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013) Blair et al. (2015) Alvarino et al. (2014) Alvarino et al. (2014) Stuer-Lauridsen et al. (2000) Blair et al. (2015)
1.22
1.67
8.4
2 1.02i
0.5
CAS CAS UASB Sludge
2.08 2.00
Primary sludge A
3.84
Xu et al. (2007)
Primary sludge B
2.81
Xu et al. (2007)
Primary sludge C
3.75
Xu et al. (2007)
Primary sludge D
3.64
Xu et al. (2007)
A2/O reactor
4.92
Ashfaq et al. (2017)
A2/O reactor CAS
3.13 2.40
Ashfaq et al. (2017) Blair et al. (2015)
1.41
1 1.11
Primary sludge A
3.54
Xu et al. (2007)
Primary sludge B
3.81
Xu et al. (2007)
Primary sludge C
3.33
Xu et al. (2007)
Primary sludge D
3.70
Xu et al. (2007)
(Continued)
Table 15.1 (Continued) Compounds
MWa
log Kow
pKa
Bioreactor
Ofloxacin
361.4
2 0.39e
A2/O reactor
Oxytetracycline
460.4
2 0.90
Oxytetracycline
460.4
2 0.90
Oxytetracycline
460.4
2 0.90
Paracetamol Paracetamol
151.2 151.2
0.46 0.49
6.0 9.3r 3.3 7.3 3.3 7.3 3.3 7.3 9.4 9.5
Paraxanthine Penicillin G Phenoxymethylpenicillin Progesterone Propyl paraben Quinine sulfate Radiolabeled iopromide Ranitidine Ranitidine Ranitidine
180.2 334.4 350.4 314.5 180.2 746.9 791.1 314.4 314.4 314.4
2 0.20 1.83e 2.09 3.87e 2.40 5.40 2 2.33 0.27g 0.27g 0.27g
Roxithromycin
837.1
2.40
9.2
Roxithromycin
837.1
2.40
9.2
Roxithromycin
837.1
2.40
9.2
2.7g 2.7g
log Kd (L/kg)
pH
log Dow
5.56
Primary sludge
2 1.70
Sludge
2 1.74
A2/O reactor
log Koc (L/kg)
2 0.38 2 0.40
CAS CAS Primary sludge CAS A2/O reactor Primary sludge Secondary sludge WWTP1 CAS Suspended growth reactor Mesophilic digested sludge Thermophilic digested sludge Continuous membrane reactor
1.93 2.49 1.25 3.28 3.55 4.56 1.04 1.90 2.63 2 0.38
Reference Ashfaq et al. (2017)
2 4.63
1.99
Jones et al. (2002) Stuer-Lauridsen et al. (2000) Ashfaq et al. (2017)
5.01
Primary sludge Sludge
kbiol (L/g/day)
1.79
0.46
1.89
8.27 1.51 2.03
7.3
Jones et al. (2002) Stuer-Lauridsen et al. (2000) Blair et al. (2015) Blair et al. (2015) Jones et al. (2002) Blair et al. (2015) Ashfaq et al. (2017) Jones et al. (2002) Ternes et al. (2004) Jelic et al. (2011) Blair et al. (2015) Vasiliadou et al. (2013)
1.92
2.79
Carballa et al. (2008)
1.14
1.95
Carballa et al. (2008)
1.34
2.15
7.0
0.20i
Fernandez-Fontaina et al. (2013)
2.15
8.4
1.54i
1.38 2.12 2.17
7.3 7.0
2 3.37i
Roxithromycin
837.1
2.40
9.2
Batch reactor
1.71
Roxithromycin Roxithromycin Roxithromycin Roxithromycin Roxithromycin Roxithromycin Salbutamol
837.1 837.1 837.1 837.1 837.1 837.1 239.3
2.40 2.40 2.40 2.40 2.40 2.40 0.01
9.2 9.2 9.2 9.2 9.2 9.2 10.3g
CAS UASB Primary sludge A Primary sludge B Primary sludge C Primary sludge D Sludge
2.00 1.60 2.95 2.36 2.90 3.08 2 1.00
Sildenafil Sodium valproate Sotalol Sulfadimethoxine Sulfadimidine Sulfamethoxazole
474.6 166.2 272.4 310.3 278.3 253.3
2.75f 2 0.85 0.24g 1.63 0.89g 0.89
6.0s 4.6p
3.09 2 1.70 2.00 2.04 1.95 1.36
Sulfamethoxazole
253.3
0.89
1.8
Sulfamethoxazole Sulfamethoxazole
253.3 253.3
0.89 0.89
1.8 1.8
Sulfamethoxazole
253.3
0.89
1.8
A2/O reactor Primary sludge WWTP1 Sludge Primary sludge B Mesophilic digested sludge Thermophilic digested sludge CAS Continuous membrane reactor Batch reactor
Sulfamethoxazole Sulfamethoxazole Sulfamethoxazole
253.3 253.3 253.3
0.89 0.89 0.89
1.8 1.8 1.8
1.00 1.60 1.53
Sulfamethoxazole
253.3
0.89
1.8
1.60
Alvarino et al. (2016)
Sulfamethoxazole
253.3
0.89
1.8
1.51
Alvarino et al. (2016)
Sulfamethoxazole
253.3
0.89
1.8
CAS Anaerobic degradation Heterotrophic aerobic degradation Heterotrophic denitrification Autotrophic denitrification Autotrophic nitrification
Go¨bel et al. (2005) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013) Blair et al. (2015) Alvarino et al. (2016) Alvarino et al. (2016)
0.85
Alvarino et al. (2016)
2.1 7.6g 1.8
2.23
Fernandez-Fontaina et al. (2013) Alvarino et al. (2014) Alvarino et al. (2014) Xu et al. (2007) Xu et al. (2007) Xu et al. (2007) Xu et al. (2007) Stuer-Lauridsen et al. (2000) Ashfaq et al. (2017) Jones et al. (2002) Jelic et al. (2011) Yang et al. (2011) Xu et al. (2007) Carballa et al. (2008)
1.18
2.00
Carballa et al. (2008)
2.41 0.93
2.81 1.75
7.0
2 4.31i
1.04
1.48
8.4
2 5.71i
(Continued)
Table 15.1 (Continued) Compounds
MWa
log Kow
pKa
Bioreactor
log Kd (L/kg)
Sulfamethoxazole Sulfamethoxazole Sulfamethoxazole Sulfamethoxazole Sulfamethoxazole Sulfamonomethoxine Sulfapyidine Sulfasalazine
253.3 253.3 253.3 253.3 253.3 280.3 249.3 398.4
0.89 0.89 0.89 0.89 0.89 0.70 0.35g 3.81
1.8 1.8 1.8 1.8 1.9 2.0 8.4g 2.3 6.5q
CAS UASB Primary sludge B A2/O reactor Sludge Sludge CAS Primary sludge
1.90 1.65 2.41 1.86 1.46 1.75 2.47 2.97
Terbutaline
255.3
0.48
Sludge
2 0.40
Testosterone Tetracycline Thiabendazole Thiabendazole Tonalide Tonalide Tonalide
288.4 444.4 201.2 201.2 258.3 258.3 258.3
3.32e 2 1.37e 2.47g 2.47g 5.70 5.70 5.80
3.15 4.86 2.18 1.81 3.72 3.38 4.18
4.18 3.85 5.05
Tonalide
258.3
5.80
4.00
4.82
Carballa et al. (2008)
Tonalide
258.3
5.70
3.53
4.34
7.0
0.9
Tonalide
258.3
5.70
CAS A2/O reactor CAS A2/O reactor Primary sludge Secondary sludge Mesophilic digested sludge Thermophilic digested sludge Continuous membrane reactor Batch reactor
Stuer-Lauridsen et al. (2000) Blair et al. (2015) Ashfaq et al. (2017) Blair et al. (2015) Ashfaq et al. (2017) Ternes et al. (2004) Ternes et al. (2004) Carballa et al. (2008)
3.43
3.88
8.4
3.87
Tonalide Tonalide Tonalide
258.3 258.3 258.3
5.70 5.70 5.70
CAS UASB Secondary sludge
3.78 3.48 3.10
3.3g 4.6g 4.6g
log Koc (L/kg)
1.58 1.87 2.87 3.26
pH
7.0 7.0
log Dow
kbiol (L/g/day)
Reference Alvarino et al. (2014) Alvarino et al. (2014) Xu et al. (2007) Ashfaq et al. (2017) Yang et al. (2011) Yang et al. (2011) Go¨bel et al. (2005) Jones et al. (2002)
2 4.12i 2 4.30i
38 0.04 0.023
Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013) Alvarino et al. (2014) Alvarino et al. (2014) Artola-Garicano et al. (2003)
Triclocarbon Triclosan Triclosan Triclosan Triclosan Trimethoprim Trimethoprim Trimethoprim Trimethoprim
315.6 289.5 289.5 289.5 289.5 290.3 290.3 290.3 290.3
4.90j 4.76g 4.76g 4.76g 4.76g 0.91 0.91 0.91 0.91
7.9g 7.9g 7.9g 7.9g 7.1 7.1 7.1 7.1
Trimethoprim
290.3
0.91
7.1
Trimethoprim Trimethoprim Trimethoprim Venlafaxine Xylometazolin
290.3 290.3 290.3 277.4 280.8
0.91 0.91 0.91 3.20g 4.91
7.1 7.1 7.1 10.1g
Zopiclone
388.8
0.25
CAS CAS Soil IVC Soil PDS Soil WPL CAS Soil WWTP1 Continuous membrane reactor Batch reactor
4.08 3.40 1.99 1.83 2.29 2.32 0.87 1.7 1.40
3.86 4.21 3.81 2.72 3.66 1.83 2.22
9.2 7.3 7.0
1.40
1.85
8.4
CAS CAS UASB Primary sludge Sludge
1.15 1.90 1.08 2.69 4.07
Sludge
1.41
WWTP, Wastewater treatment plant; IVC, Imperial Valley caly; PDS, Palmdale sand; WPL, Washington Palouse loam. a MW: molecular weight, g/mol. b CAS: conventional activated sludge. c UASB: upflow anaerobic sludge bed. d n.d.: no data. e Obtained from Hansch et al. (1995). f Obtained from Sangster (2014). g Obtained from DrugBank, http://www.drugbank.ca. h Obtained from ACE (2016). i Calculated values following the Eqs. (15.1) (15.3). j Obtained from US EPA. k Obtained from Zeng et al. (2006). l Use the log Kow without modification with respecting the original data. m Obtained from McFarland et al. (1997). n Obtained from Haynes (2012). o Obtained from Gerhartz (1985). p Obtained from O’Neil (2001). q Obtained from SPARC (2008). r Obtained from Tolls (2001). s Obtained from Royal Society of Chemistry.
Blair et al. (2015) Blair et al. (2015) Yu et al. (2013) Yu et al. (2013) Yu et al. (2013) Go¨bel et al. (2005) Lin and Gan (2011) Jelic et al. (2011) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013) Blair et al. (2015) Alvarino et al. (2014) Alvarino et al. (2014) Ivanova´ et al. (2017) Stuer-Lauridsen et al. (2000) Stuer-Lauridsen et al. (2000)
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Pharmaceuticals and Personal Care Products: Waste Management and Treatment Technology
et al., 2013; Blair et al., 2015). When normalized by organic carbon contents, large deviations of log Koc values with log Kow were obtained (as shown in Fig. 15.3). Taking CBZ as an example, log Koc values of 0.44 L/kg (Fernandez-Fontaina et al., 2013) and 3.59 L/kg (Jones et al., 2002) were obtained. Several studies even reported that the contribution of adsorption in conventional biological wastewater treatment for PPCPs could be neglected (Alvarino et al., 2016; Deng et al., 2016), while others found strong sorption affinities between biosolids and PPCPs such as galaxolide, tonalide, celestolide, and 17α-ethynil estradiol (Carballa et al., 2008; Jelic et al., 2011; Reif, 2011; Dialynas and Diamadopoulos, 2012). Similar trends have been observed in the sorption ability for PPCPs onto soil and sediments, the obtained sorption parameter Kd ranged from 0.4 to 269,097 L/kg for 11 PPCPs including bisphenol A, 17β-estradiol (E2), oxytetracycline, sulfathiazole, and sulfamethazine (Pan et al., 2009). There are two main sorption mechanisms during biological wastewater treatment, namely absorption and adsorption. 1. Absorption (or partition): This entails the hydrophobic interactions of the aliphatic and aromatic groups of a compound with the hydrophobic sites of biomass including biolipid chains and lipophilic cell membranes of the microorganisms, and the lipid fractions of the sludge (Ternes et al., 2004; Carballa et al., 2008; Chen et al., 2010; Zhang and Zhu, 2012; Zhang et al., 2013). 2. Adsorption: This is mainly the electrostatic interactions of positively charged groups of chemicals with the negatively charged surfaces of the microorganisms caused by the presence of carboxyl, hydroxyl, and phosphate groups (Golet et al., 2003; Siegrist et al., 2003; Zhang and Zhu, 2014).
Figure 15.3 The relationship of log Koc with log Kow of target PPCPs (data directly obtained and indirectly calculated from the literature is shown in Table 15.1). PPCP, Pharmaceutical and personal care product.
The role of microorganisms in the removal of pharmaceutical and personal care products
359
For nonpolar HOCs (such as PAHs, PCBs, and insecticides) the sorption affinity to biosolids and other carbon-containing solids can mainly be attributed to absorption and good relationships can be established between log Kd (or log Koc) with the octanol water partitioning coefficient (log Kow) (Karickhoff et al., 1979; Chen et al., 2010; Zhang et al., 2018). However, as many PPCPs are moderately polar substances, these compounds might only interact with parts of organic matter or with the minerals of biosolids. In addition to the partition mechanism, specific electrostatic interactions may be relevant for some PPCPs. Unlike the nonpolar compounds, the sorption mechanism and behavior of polar molecules on biomass highly depend on the chemical form(s) in particular solution conditions. Therefore the pH of wastewater (usually between 7 and 9) or aqueous solutions will directly impact the ratio of the nonionized to ionized chemical form(s) of these PPCPs, which in turn impacts the success rate of PPCPs’ removal and sorption (Wells, 2006). In this situation, Kow values should not be the only parameter used to determine the sorption behavior. Recent studies revealed that the sorption parameters are dependent on the hydrophobicity ionogenicity profile of the chemical (Wells, 2006), and the pH of the wastewater undergoing treatment as well as the pKa and log Kow of targeted compounds are needed to fully evaluate the sorption process. A pHdependent octanol water distribution coefficient (Dow) was proposed to evaluate and predict the sorption of PPCPs. To calculate the log Dow value, three types of PPCPs were classified, namely neutral PPCPs (e.g., musk fragrances), acidic PPCPs (e.g., ibuprofen, naproxen, estrogens), and basic PPCPs (e.g., roxithromycin, iopromide, and clarithromycin). The value of log Dow can be calculated using the following equations (Stuer-Lauridsen et al., 2000; Jones et al., 2002; Ternes et al., 2004; Wells, 2006; Carballa et al., 2008). For PPCPs with only neutral moieties: log Dow 5 log Kow
(15.1)
For PPCPs with acidic moieties (e.g., log Dow 5 log Kow 1 log
COOH):
1 1 1 10pH2pKa
For PPCPs with basic moieties (e.g., log Dow 5 log Kow 1 log
OH,
RNH2,
1 1 1 10pKa2pH
(15.2) R2NH,
R3N): (15.3)
Similar to the log Kd 2 log Kow (or log Kd 2 log Kow) relationship, a log Kd 2 log Dow relationship was established by Carballa et al. (2008) for PPCPs and digested sludge, namely: log Koc 5 0:74 log Dow 1 0:15
360
Pharmaceuticals and Personal Care Products: Waste Management and Treatment Technology
Figure 15.4 The relationship of log Kd with log Dow of target PPCPs (data directly obtained from the literature). PPCP, Pharmaceutical and personal care product.
In the literature, only 12 Kd 2 Dow data have been collected and plotted and are shown in Fig. 15.4 revealing the weak correlation found between log Kd values and log Dow values (R2 5 0.5638, n 5 12). Based on Eqs. (15.1) (15.3), 27 more Kd 2 Dow data were calculated considering the available pKa and the ambient pH. These data (39 Kd 2 Dow data) were then plotted and illustrated in Fig. 15.5. Unfortunately, no significant correlation was found, probably due to the limited valid data, different bioreactors, and other practical conditions such as HRT and sludge retention time. However, there seems to be a glimmer of hope. By reviewing the plots of log Kd and log Kow (total 273 data) collected from the literature (see Fig. 15.6), significant correlations between log Kd and log Kow were found (log Kd 5 0.998 log Kow 2 0.858, R2 5 0.9952; black open circle, n 5 72) which divides the disorderly data into three clear zones. These well-fitted data include neutral PPCPs (e.g., ifosfamide, diazepam), acidic PPCPs (e.g., ibuprofen, naproxen, and diclofenac), and basic PPCPs (e.g., erythromycin, roxithromycin, and clarithromycin). Further studies are required to discover the hidden information that can be derived from the data. Moreover, it would be appreciated if researchers and authors provide more condition parameters (including pH of wastewater, pKa of target compounds, Kd values or Koc values, specific organic carbon content of biosolids, and so on) in their studies and published papers.
15.1.2 Factors affecting sorption of pharmaceutical and personal care products In general, the sorption affinity of PPCPs with biosolids depends on the chemical nature of PPCPs (e.g., log Kow, pKa, and structure), coexisting substances,
The role of microorganisms in the removal of pharmaceutical and personal care products
361
Figure 15.5 The relationship of log Kd with log Dow of target PPCPs (data directly obtained and indirectly calculated from the literature as listed in Table 15.1). PPCP, Pharmaceutical and personal care product.
Figure 15.6 Distinguishing the well-fitted data from the total collected data of log Kd versus log Kow.
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Pharmaceuticals and Personal Care Products: Waste Management and Treatment Technology
properties of the biosolids, and practical conditions such as pH, redox potentials, and contact time. These factors could also indirectly influence the subsequent biodegradation and advanced oxidation of PPCPs and the disposal of PPCPs-sorbed sludge through the changing of (bio)available PPCPs in the aqueous phase of wastewater. The chemical nature of the PPCPs plays a crucial role in their sorption (including adsorption and absorption) during the biological treatment of PPCP-bearing wastewater. Chemical properties of PPCPs, such as hydrophobicity and polarity, are considered to be the driving force of their environmental transport (Wells, 2006). Here we will consider polar properties and the octanol water coefficient: 1. Polar properties: Polar moieties are recognized as one of the key properties affecting the sorption efficiency and mechanisms by (bio)sorbents. Nonpolar PPCPs sorb onto biosolids mainly via hydrophobic interactions (e.g., π π interaction), which are usually resistant to a change of wastewater pH. However, the properties of the wastewater has a marked impact on the sorption of PPCPs with polar functional group(s) onto biosolids, owing to the interactions with biosolids via both partitioning and adsorption mechanisms. The polar substituent group(s) could also impact the sorption of PPCPs. For example, the Kd value of CBZ was about 277 L/kg (calculated value based original data) (Miao et al., 2005). Potential metabolites which have one or two hydroxyl group(s) (2-hydroxycarbamazepine, 3-hydroxycarbamazepine, and 10,11-dihydroxycarbamazepine) have much lower Kd values of 26.9, 23.1, and 6.94 L/kg, respectively (calculated values based on original data) (Miao et al., 2005). 2. The octanol water coefficient (log Kow) and pH-dependent octanol water distribution coefficient (log Dow): log Kow (some researchers use log Dow) is widely considered as the most important and simple parameter to evaluate and predict the sorption behavior of (hydrophobic) organic contaminants onto carbon-rich sorbents. Theoretically, the higher log Kow a given substance has, the larger the sorption affinity parameter (e.g., log Kd) onto a particular (bio)sorbent is. As illustrated in Fig. 15.6, there is a good correlation between the log Kd and log Kow values (the black open circle representing data). Even out of the range of the black open circle area, the same trend of log Kd values being larger for PPCPs with larger log Kow values is found.
The type of bioreactors, redox potentials, and coexisting substances (primary substance effects) are also important parameters that make a difference in the sorption behavior of PPCPs during wastewater treatment. Various studies have demonstrated that PPCPs removal, sorption, and biotransformation are highly dependent on the retention time, biomass concentration, bioreactor type, and the type of primary substance (Gartiser et al., 2007; Mohring et al., 2009; Conkle et al., 2012; Vasiliadou et al., 2013). Alvarino et al. (2014) found high variability of the relative amount of PPCPs sorbed onto sludge in different bioreactors under various aerobic conditions. For example, the constant sorption coefficient (Kd) for ibuprofen, fluoxetine, tonalide, and celestolide in conventional activated sludge (CAS) and upflow anaerobic sludge blanket were 240 and 100, 2500 and 700 L/kgvss, 6000 and 3000 L/kgvss, 3050 and 1200 L/kgvss, respectively. Alvarino et al. (2016) determined the sorption coefficient (Kd) of SMX under five different redox potentials and found significant differences among the Kd values of SMX. For example, the
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Kd values of SMX (L/kgtss) were 40, 32, and 7 under anaerobic degradation, autotrophic denitrification, and autotrophic nitrification conditions, respectively. Vasiliadou et al. (2013) demonstrated that treatment of coexisting substances led to increased sorption distribution coefficients for PPCP due to synergistic effects. Taking caffeine and SMX as examples under individual sorption conditions the Kd values were 60 and 0 L/kg, respectively, whereas under simultaneous conditions these values were 130 and 140 L/kg, respectively.
15.2
Transmembrane mechanisms of pharmaceutical and personal care products
The amount of material (target chemical molecules) crossing a surface (cell membrane) in a unit of time is known as flux. One of the first physiological demonstrations of the existence of membranes is the semipermeable property of cell envelopes (Al-Awqati, 1995). This means that not all substances can cross such membranes easily. Therefore chemical molecules, especially hydrophobic and pseudo-hydrophobic organic compounds, transport across the cell membrane (as a “barrier”) with different one-way flux through several mechanisms. These mechanisms mainly include diffusion through the lipid bilayer and various protein channels. Assuming that a given chemical contaminant has some degree of water solubility with a corresponding concentration in the wastewater phase or other aqueous environment, the obstacle to biological treatment would be the accessibility of the pollutants to the enzymatic machinery of the cell (which might be called the “real” bioavailability) (Bressleer and Gray, 2003). For organic pollutants, their transmembrane transport is the rate-limiting process during the biodegradation. The membrane-crossing flux depends on the target contaminant concentration outside the cell membrane (Vander et al., 2001). In addition, the type and structure of protein channels are also important for the transmembrane transport of HOCs and pseudo-HOCs.
15.2.1 Theoretical transmembrane flux The membrane-crossing flux can be simply written as (Bressleer and Gray, 2003): J 5 km CLout 2 CLin
(15.4)
where J is the flux of the components across the membrane; km is the permeability of the membrane, which has a positive correlation with the oil water partition coefficient (typically one-tenth of the octanol water distribution coefficient) and a negative correlation with the square root of the molecular weight of the target compound; and CLin and CLout are the intracellular and external concentrations of the target compound, respectively.
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Considering the active intracellular enzyme, the intracellular concentration of the target compound is usually significantly reduced to a relatively low level (CLin 0). The external concentration (CLout ) should be the compound concentration outside of the membrane and equal to water solubility. However, for these (pseudo) hydrophobic organic compounds, considerable amount of the compounds are accumulated (sorbed) on the surface of biosolids or microorganisms (see Section 15.1.1). Therefore the external concentration (CLout ) should be equal to the surface-sorbed concentration (Cad), which can be written as (Zhang, 2013): CLout 5 Cad 5 kKd Sw
(15.5)
where Kd is the sorption affinity coefficient, Sw is the water solubility, and k is an empirical parameter. The maximum membrane flux can be estimated through (Zhang, 2013): Jmax 5 0:002994 KowUKdUSwUMW0:5
(15.6)
where Kow is the octanol water distribution coefficient of the target compound, MW is the molecular weight of the target chemical, and 0.002994 is the empirical constant. If we do not consider the effect of sorption, the maximum membrane flux can be estimated as (Bressleer and Gray, 2003): Jmax 5 0:003 KowUSwUMW20:5
(15.7)
where 0.003 is the empirical constant. Bressleer and Gray (2003) provided a correlation between the maximum aerobic degradation rate and maximum membrane flux (calculated using Eq. 15.7) of 16 chemicals including aromatics, chlorine compounds, and oxygenated compounds. They suggested that a broad trend of maximum possible bioremediation rates could be correlated with an estimation of membrane flux. However, we evaluated the relationship between biodegradation rate and estimated maximum membrane flux (J) of 17 PPCPs (illustrated in Fig. 15.7) and found no significant correlation (R2 5 0.1169, n 5 50). The maximum membrane flux was calculated using Eq. 15.7 and the biodegradation rates were directly obtained from the literature as listed in Table 15.1. There are three possible reasons why the data did not fit the equation well: (1) the biodegradation rates of selected PPCPs were obtained by activated sludge in WWTPs, which might be different than those obtained from pure cultural biodegradation; (2) the selected PPCPs contained various negatively and/or positively charged polar groups; and (3) the sorption of PPCPs on the surface of cell membranes resulted in an increase of available concentrations of PPCPs being transported across the cell membranes. As we can see from Eq. 15.6, for a given contaminant in a certain wastewater treatment system, the transmembrane flux mainly depends on the sorption property
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Figure 15.7 Correlation between biodegradation rate (kb) and maximum membrane flux (J). The biodegradation rate was obtained from the literature. The maximum flux was estimated from Eq. (15.7) using the property data listed in Table 15.1.
of active biosolids (or biomass) which, subsequently, determines the efficiency of the biological degradation of the contaminant (Bressleer and Gray, 2003). We evaluated the relationship between sorption-modified transmembrane flux (J ) and sorption parameter (Kd), as illustrated in Fig. 15.8. A significant correlation of tested PPCPs (excluding galaxolide) was found and the results indicate that transmembrane flux partly depends on the sorption process. Furthermore, the relationship between biodegradation rate (kb) and sorption modified maximum membrane flux (J ) was evaluated, and is illustrated in Fig. 15.9. The correlation between kb and J (R2 5 0.66, n 5 50) is much better than that of kb and J. The positive correlation and the mechanism beyond the estimation of J (based on sorption-modified concentration) indicate that surface sorption plays a vital role in the transmembrane process and enzymatic degradation of PPCPs by biosolids. In addition, transmembrane flux is a good parameter to predict the intracellular biotransformation of PPCPs.
15.2.2 Transmembrane route There mainly are two transport routes for organic compounds across the cell membrane and into the cytoplasmic environment, namely diffusion through the lipid bilayer and protein channels. The cytoplasmic membrane lipids of bacteria cells play an essential role in the function of transmembrane processes (Green et al., 1980; Carrie`re and Legrimellec, 1986). Integral membrane proteins can span the lipid bilayer, some of which can form channels allowing ions as well as organic compounds to diffuse across the membrane (Vander et al., 2001). The property of
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Figure 15.8 Correlation between sorption modified maximum membrane flux (J ) with sorption constant (Kd). The sorption-modified maximum flux was estimated from Eq. (15.6) using the property data listed in Table 15.1.
Figure 15.9 Correlation between biodegradation rate (kb) and sorption-modified maximum membrane flux (J ). The biodegradation rate was obtained from the literature. The sorptionmodified maximum flux was estimated from Eq. (15.6) using the property data listed in Table 15.1.
organic contaminants (e.g., log Kow) and the type of protein are important factors that can effectively affect the transmembrane process. Diffusion through the lipid bilayer. The cell membrane is composed of a phospholipid bilayer. The amphiphilic property of phospholipids, which have a polar
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head group and two nonpolar “hydrophobic tails”, divides the lipid bilayer into three unique polarity domains. The polar heads orientate toward the outside of both sides of the membrane and form two fairly polar domains. The central region of the bilayer is nonpolar and hydrophobic in nature due to the fatty acid moieties of the phospholipids. Liposomes and lipid bilayers prepared by lecithin (phosphatidylcholine) are frequently used as experimental models to simulate biological membranes due to simple and good mimic of biological membranes. The lipid bilayer poses a significant barrier to the mass transport of certain organic contaminants. In general, nonpolar organic compounds with high octane water distribution coefficient (log Kow) readily partition into regular membrane lipid bilayers and easily enter into the internal matrix. In contrast, most polar organic molecules diffuse into cells very slowly, or not at all, although some of these contaminants could sorb on the membrane surface without entering the cell. For example, the released concentration of pyrene (log Kow 5 4.88) captured in liposome could eventually reach around 0.1 mg/L, although it is a time-consuming process (Zhang et al., 2013). Song et al. (2010) investigated the sorption and transmembrane transport of two antibacterial drugs (kanamycin and chloramphenicol) using lecithin liposome. Relatively strong sorption of both drugs onto the liposome was found; however, different mechanisms for the two drugs were used. The sorption of hydrophilic kanamycin (log Kow 5 26.70) occurred via electrostatic attraction, whereas the liposome sorbed the weak hydrophobic chloramphenicol (log Kow 5 1.14) through lipid water partition. More than 89% of the adsorbed kanamycin was located on the outer surface of the liposome, while over 80% of the adsorbed chloramphenicol entered the internal matrix. Diffusion through the transmembrane protein channels. Bacteria dominate the biological system during the removal and biodegradation of organic contaminants in wastewater. The outer membrane associated with the cytoplasmic membrane of Gram-negative bacteria provides an effective barrier for the passage of hydrophobic molecules due to the presence of the polar and hydrophilic lipopolysaccharide (LPS) layer on the outside of the bacterial cells (Hearn et al., 2009; Martı´nez et al., 2013). Therefore the transport of hydrophobic contaminants through the LPS layer is energetically unfavorable. Evidence shows that the removal of LPS from the cell surface is one of the important reasons for increasing the uptake of phenanthrene by a Citrobacter strain (Li and Zhu, 2012). Therefore hydrophobic organic contaminants cannot be easily transported from the outside of the cell into the internal matrix, even though the lipid bilayer provides ready passage for HOCs. Thus uptake of hydrophobic molecules must proceed through specific transporter pathways. Integral proteins have been recognized as offering a possible solution. There are two possible transport mechanisms that can be envisioned, and both are based on the diffusion process. a. “Classical” integral protein transport: The hatch undergoes conformational changes to create a transient channel, where hydrophobic molecules can transport transverse from the extracellular medium directly to the aqueous periplasm (van den Berg et al., 2004; Karuppiah et al., 2011).
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Figure 15.10 Schematic diagram of outer membrane protein with lateral opening. b. Alternative “lateral diffusion” transport (Hearn et al., 2009; Martı´nez et al., 2013). In the outer membrane protein with a lateral opening, a detergent molecule-bounded lateral opening can be trigged and an uninterrupted, hydrophobic passageway for lateral diffusion can be generated. Through the lateral opening, hydrophobic molecules can exit the barrel wall to move into the outer membrane from where they could diffuse into the periplasm. The lateral opening of these proteins is located in the region of the polar nonpolar interface of the outer leaflet of the outer membrane. The brief schematic diagram is provided in Fig. 15.10. To date, several outer membrane proteins containing lateral openings have been observed and characterized, including OprG (Touw et al., 2010), AlkL (Jusling et al., 2012), NapQ (Eaton, 1994), and FadL families (Hearn et al., 2009; Martı´nez et al., 2013).
15.3
Biotransformation of pharmaceutical and personal care products
15.3.1 Typical degrading strains, related enzymes and pathways of typical pharmaceutical and personal care products The use of PPCPs as a growth substrate (both sole carbon source and cosubstance) has been demonstrated for microorganisms (mainly bacteria and fungi) from wastewater and surface water. Microbes capable of growth on, and biodegradation of, certain PPCPs have been isolated and characterized. Examples of microbes capable of degrading four typical PPCPs (i.e., ibuprofen, SMX, CBZ, and triclosan) as well as related enzymes are summarized in Table 15.2. Three typical fungal enzymes— that is, manganese-peroxide (MnP), laccases, and lignin-peroxide (LiP)—are commonly studied. In sharp contrast, bacterial enzymes are barely mentioned.
Table 15.2 A Summary of strains and related enzymes capable of the biodegradation of four typical pharmaceutical and personal care products Compound
Strain
Enzyme
Condition
Reference
Ibuprofen
Phanerochaete chrysosporium Trametes versicolor Ganoderma lucidum Firmicutes sp. Aeromonas sp. Nitrospira sp. Betaproteobacteria Sphingomonas sp. Ibu-2 Patulibacter sp. I11 Trametes versicolor Ganoderma lucidum Rhodococcus rhodochrous
MnPa Lacb MnP
Fed batch stirred reactor Batch stirred reactor Batch stirred reactor Sludge Sludge Sludge Sludge Batch stirred reactor Batch stirred reactor Batch stirred reactor Batch stirred reactor Membrane bioreactor Batch stirred reactor Batch stirred reactor Batch stirred reactor Batch stirred reactor Sludge Sludge Sludge Sludge Membrane bioreactor Batch stirred reactor
Rodarte-Morales et al. (2012) Vasiliadou et al. (2016)
Sulfamethoxazole
Pseudomonas aeruginosa Acinetobacter sp. W1 Dyella sp. WW1 Firmicutes sp. Aeromonas sp. Nitrospira sp. Betaproteobacteria Microbacterium sp. BR1 Bacillus subtilis
ipfABDEF Lac MnP
Zhao et al. (2015)
Murdoch and Hay (2005, 2006) Almeida et al. (2013) Vasiliadou et al. (2016) Bouju et al. (2012) Gauthier et al. (2010) Bouju et al. (2012) Wang and Wang (2018a) Wang et al. (2018) Zhao et al. (2015)
Fenu et al. (2015) Larcher and Yargeau (2011) (Continued)
Table 15.2 (Continued) Compound
Strain
Triclosan
Pseudomonas putida TriRY Alcaligenes xylosoxidans TR1 Dyella sp. WW1 Rhodotorula mucilaginosa Penicillium sp. Aspergillus versicolor Nannochloris sp. Sphingopyxis sp. KCY1 Nitromonas europaea Burkholderia xenovorans LB400 Sphingomonas sp. YL-JM2C Sphingomonas sp. PH-07 Sphingomonas wittichii RW1 Pseudomonas sp. CBZ-4 Acinetobacter sp. HY-7 Trametes versicolor Ganoderma lucidum Starkeya sp. C11 Rizobium sp. C12 Dyella sp. WW1 Unidentified basidiomycete BNI Phodococcus rhodochrous
Carbamazepine
CBZ, Carbamazepine. a MnP: manganese-peroxidase. b Lac: laccase. c LiP: lignin-peroxidase.
Enzyme
Lac MnP
Lac, LiP,c MnP
Condition
Reference
Tryptic soy broth Tryptic soy broth Batch stirred reactor Original wastewater Original wastewater Batch stirred reactor Batch stirred reactor Batch stirred reactor Batch stirred reactor Batch stirred reactor Batch stirred reactor Batch stirred reactor Batch stirred reactor Low temperature (10 C) Batch stirred reactor Batch stirred reactor Batch stirred reactor Activated sludge Activated sludge Batch stirred reactor Batch stirred reactor Batch stirred reactor
Meade et al. (2001) Wang et al. (2018) Ta¸stan et al. (2016) Ta¸stan and Do¨nmez (2015) Bai and Acharya (2016) Lee et al. (2012) Roh et al. (2009) Kim et al. (2011) Mulla et al. (2015) Kim et al. (2011) Li et al. (2013) Cui et al. (2009) Vasiliadou et al. (2016) Bessa et al. (2017) Wang et al. (2018) Santosa et al. (2012) Gauthier et al. (2010)
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Murdoch and Hay (2005, 2006) identified a cluster of five putative genes (ipfABDEF) from a Sphingomonsa strain, which could be assigned to aromatic dioxygenases (ipfA and ipfB), enzymes for the addition or removal of acyl groups (ipfD), and coenzyme A ligases (ipfF). Microbiological transformation of PPCPs can result in complete mineralization to CO2 and H2O, or degradation to lower molecular weight mediates, or minor structural modification. These biotransformations happen through either catabolism (PPCPs act as a carbon and energy source) or cometabolism (coincidental transformation of target PPCPs with other compounds) (Kagle et al., 2009). Many studies have reported that PPCP metabolites (including lower molecular weight mediates and minor chemical modified products) were widely determined (Miao et al., 2005; Kosjek et al., 2007). These metabolites might still pose high risks to ecosystems and humans such as estrone (E1, transformed from 17β-estradio, E2) and nonylphenol (metabolite of nonionic surfactant). These results emphasize that the disappearance of the parent PPCP compound is not equivalent to the complete removal of PPCPs and studies on the detection of PPCPs in aqueous environments and metabolic pathways are critical to understand their fate in environment (Kagle et al., 2009). The metabolic pathways of PPCPs differ between microbial strains, related enzymes, and environmental conditions (e.g., pH, temperature). Next we briefly discuss three catabolic pathways of selected PPCPs (i.e., ibuprofen, triclosan, and SMX). 1. Ibuprofen. The biotransformation of ibuprofen usually occurs via the meta cleavage pathway as suggested by Murdoch and Hay (2005, 2006) using Sphingomonas sp. Ibu-2, and is illustrated in Fig. 15.11. The biodegradation of ibuprofen initiates the formation of ibuprofenCoA with the help of coenzyme A ligase encoded by ipfF. Two oxygen molecules are added to replace the propionic acid side chain catalyzed by the dioxygenase (encoded by ipfAB), forming 4-isobutylcatechol. This is followed by a meta cleavage to form 2-hydroxy-5-isobutylhexa-2,4-dienedioic acid. 2. Triclosan. The main degradation pathway of triclosan involves initial ether bond cleavage catalyzed by the chlorohydroquinone dehydrogenase to form 2,4-dichlorophenol (Lee et al., 2012; Mulla et al., 2015). Several intermediates (shown in the brackets in Fig. 15.12) are proposed to form compounds containing quinone bonds. 2,4-Dichlorophenol is further degraded through dechlorination to hydroquinone which can be easily mineralized to CO2 and H2O. 3. SMX. There are three regions where different enzymes attack to and, thus, results in six different degradation pathways, as illustrated in Fig. 15.13. These regions include an amino group linked to a benzene ring, heterocyclic ring, and S N bonds. The ammonia oxidizing bacteria (e.g., Rhodoccocus strain) initially attack the amino group via cometabolism and form desamino-SMX (Mu¨ller et al., 2013). Wang and Wang (2018a) proposed a degradation pathway initialized by simultaneously attacking the amino group and N O bonds in the heterocyclic ring, resulting in the metabolites of 4-hydroxybenzenesulfonic acid and 3-hydroxylamine-amino-5-carboxyl. Sulfate-reducing bacteria have the potential to cleave the heterocyclic ring through three pathways, namely isomerism, hydrogenation,
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Figure 15.11 Possible pathways of ibuprofen. Source: Modified based on Murdoch, R.W., Hay, A.G., 2005. Formation of catechols via removal acid side chains of ibuprofen and related aromatic acids. Appl. Environ. Microbiol. 71 (10), 6121 6125. and dioxygenation (Jia et al., 2017). SMX can also be attacked through breaking the S N bond to form 3-amino-5-methylisoxazole and 4-aminobenzenesulfonic acid through cometabolic pathways. This is followed by the metabolic degradation by bacteria such as Microbacterium sp. to form 4-aminophenol and further trihydroxybenzene (Jiang et al., 2014; Ricken et al., 2013).
15.3.2 Factors Once the PPCPs enter the wastewater treatment systems, they can be removed by several mechanisms including sorption and biotransformation. Sorption to biosolids plays an essential role in the removal and transmembrane process of PPCPs (Fig. 15.8), and transmembrane transport can be considered as a rate-limited parameter for their biotransformation (Fig. 15.9). Factors having an impact on sorption and transmembrane affect the biotransformation efficiency and degree. In addition, incubation conditions influencing microorganisms’ growth and activities are also important parameters for PPCPs’ biotransformation. 1. Chemical structure and properties. Functional groups including esters, nitriles, and aromatic alcohols usually increase the bioavailability of target compounds, whereas aromatic amines, iodide, nitro, and azo groups tend to render a compound more recalcitrant (Tunker et al., 2000). With similar initial concentrations (4.5 μg/L for triclosan and 4.2 μg/L for BPA), the specific degradation rates of the two PPCPs were 0.15 and 1.0 L/ mg/h, respectively, mainly owing to structure differences (Zhou et al., 2014).
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Figure 15.12 Possible pathways of triclosan. Source: Modified based on Lee, D.G., Zhao, F., Rezenom, Y.H., Russell, D.H., Chu, K.H., 2012. Biodegradation of triclosan by a wastewater microorganism. Water Res. 46 (13), 4226 4234; Mulla, S.I., Wang, H., Sun, Q., Hu, A., Yu, C.P., 2015. Characterization of triclosan metabolism in Sphingomonas sp. YL-JM2C. Sci. Rep. 6, 21965 21966.
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Figure 15.13 Possible pathways of SMX. SMX, Sulfamethoxazole. Source: Modified based on Mu¨ller et al. (2013); Jiang et al. (2014); Jia et al. (2017); Ricken et al. (2013); Wang, J.L., Wang, S.Z., 2018a. Microbial degradation of sulfamethoxazole in the environment. Appl. Microbiol. Biotechnol. 102 (8), 3573 3582; Wang, S.Z., Wang, J.L., 2018b. Biodegradation and metabolic pathway of sulfamethoxazole by a novel strain Acinetobacter sp. Appl. Microbiol. Biotechnol. 102, 425 432 (Wang and Wang, 2018b).
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2. Sorption behaviors. The sorption of PPCPs onto the cell surface facilitates the transmembrane process and subsequently increases the substance concentration for intercellular enzymatic degradation. For example, the sorption affinity constant (Kd), sorptionmodified transmembrane flux (J ), and biodegradation rate (kb) of SMX under a continuous membrane bioreactor and CAS were 8.6 and 80 L/kg, 7.66 and 71.3 mg/cm2/s, and 0.3 and 9 L/g/day, respectively (Fernandez-Fontaina et al., 2013; Alvarino et al., 2014). Similar tends were found for the treatment of tonalide, of which the parameters were 2714 and 6000 L/kg for Kd, 316,745 and 700,246 mg/cm2/s for J , and 3.87 and 38 L/g/ day for kb, respectively (Fernandez-Fontaina et al., 2013; Alvarino et al., 2014). 3. Microbial community structure or degrading strains. The mineralization of 0.5 mM 14Clabeled SMX could reach around 60% by enrichment cultures, whereas the Ralstonia microcosm (Ralstonia sp. HB1 and Ralstonia sp. HB2) could mineralize only 40% of SMX under the same conditions (Bouju et al., 2012). Different strains might secrete different dominant enzymes which could lead to different biotransformation results. For example, two white-rot fungi had different dominant enzyme systems (laccase for Trametes versicolor, and manganese-peroxidase for Ganoderma lucidum), and demonstrated different biodegradation ability of PPCPs (Vasiliadou et al., 2016). 4. Bioreactor conditions (substance concentration, temperature, and retention time). Initial concentration is another factor that can influence the biotransformation of PPCPs in WWTPs and other aqueous environments. In general, the biotransformation rate of PPCPs usually presents as a fast degradation process followed by a slow degradation process (Wang and Wang, 2018a). At low concentrations, the degradation rates of PPCPs increase with increasing substrate concentration. For example, the specific removal rates of CBZ were about 0.0013 and 0.0063 hour21 by Pseudomonas sp. CBZ-4 at the initial concentrations of 10 and 60 mg/L, respectively (Li et al., 2013). Since the detected concentration in wastewater and aqueous environments ranges from ng/L to mg/L (Wang and Wang, 2016), the bioavailability of PPCPs in the aqueous phase might be low. Therefore the degrading strains for PPCPs should be capable of utilizing PPCPs at very low concentrations.
Coexisting substances (usually an easily degradable carbon source, e.g., glucose, acetate) can accelerate the biodegradation process. For example, the biodegradation rate was much higher for diclofenac degradation with periodic feeding of carbon acetate (5.9 mM) (0.078 day21) than those with an addition of carbon acetate (5.9 mM) only at the beginning (0.028 day21) or those without any carbon source addition (0.017 day21) (Bessa et al., 2017). Many studies have noted improved biological transformation of certain PPCPs with increased retention time, both HRT and solids retention time (SRT). Sua´rez et al. (2008) summarized that shorter HRT reduced the elimination of ibuprofen and ketoprofen, and higher removal of most PPCPs was observed for longer SRT. A much greater increase in the removal of trimethoprim and macrolides (two to three times) was observed when SRT increased from 16 33 to 60 80 days in a membrane bioreactor (Go¨bel et al., 2007). Another factor affecting the biotransformation of PPCPs is temperature due to the influence of the enzyme activities of microorganisms. The optimum temperature of most organisms is above 20 C. However, in some cases, cold-adapted bacteria preferring low temperature are also reported. For example, Li et al. (2013) reported a cold-adapted CBZ-degrading bacterium (Pseudomonas sp. CBZ-4), for which the
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optimum temperature was 10 C. The average removal efficiency of CBZ could reach 26% at 10 C, whereas the removal efficiency dropped to only 9% when the temperature increased to 37 C.
15.4
Conclusion
The sorption, transmembrane process, and intercellular enzymatic degradation of PPCPs are successive processes, with each providing more available substance for the subsequent process. The sorption of PPCPs onto cell surface or biosolids surface could facilitate the transmembrane transport to the intracellular cytoplasm, providing sufficient substance for the intercellular catalytic enzymes. For a given contaminant in a certain wastewater treatment system, the transmembrane flux mainly depends on the sorption property of active biosolids (or biomass) and subsequently determines the efficiency of the biological degradation of the contaminant. However, less is known on the sorption and transmembrane process of PPCPs during wastewater treatment. Therefore further research is urgently needed to gain a better understanding of the behavior, removal efficiency, and fate of PPCPs after disposal into wastewater.
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Further reading Aronson, D., Citra, M., Shuler, K., Printup, H., Howard, P.H., 1999. Aerobic Biodegradation of Organic Chemicals in Environmental Media: A Summary of Field and Laboratory Studies. EPA Reports, Office of Research and Development Athens GA, 30605, New York.
15
Dong Zhang Hangzhou Dianzi University, Hangzhou, P.R. China
Pharmaceutical and personal care products (PPCPs) have been a worldwide concern over the past two decades due to their extensive applications (including medicine, industry, livestock farming, aquaculture, and people’s daily life) and adverse impacts to wildlife and people (Pan et al., 2009; Oulton et al., 2010; Evgenidou et al., 2015; Paredes et al., 2016). Based on their various purposes, PPCPs can be classified into several classes (Wang and Wang, 2016), including hormones (e.g., estriol, estrone, and 17β-estradiol), antibiotics [e.g., sulfamethoxazole (SMX)], lipid regulators, nonsteroidal antiinflammatory drugs, β-blockers, antidepressants (e.g., diazepam, and amitriptyline), anticonvulsants, antineoplastic, diagnostic contrast media, fragrances, preservatives, disinfectants (e.g., triclosan), and sunscreen agents (e.g., oxybenzone). The worldwide production of these PPCPs can reach up to 2 3 107 tons per year. These micropollutants can be detected ubiquitously in soil, wastewater, surface water, and drinking water as well as having similar environmental behaviors with persistent organic pollutants (POPs) and are also called pseudo-POPs. Wastewater is often recognized as one of the important sinks of PPCPs, and various treatment technologies such as activated sludge treatment, fungal biodegradation, nanofiltration, reverse osmosis, and advance oxidation methods have been focused on for the removal of PPCPs from wastewater (Joss et al., 2004; Jelic et al., 2011; Dialynas and Diamadopoulos, 2012). Moreover, the type of biological treatment plays a vital role in the removal, transformation, and transportation of the PPCPs’ environmental fate and behavior. Activated sludge systems are adopted as the main conventional biological treatment unit to remove or degrade organic pollutants (including PPCPs) from wastewater in wastewater treatment plants (WWTPs). It has the capacity to remove PPCPs from wastewater, but the removal efficiency of PPCPs has changed greatly, depending on the physiochemical properties of the targeted compounds as well as the environmental conditions such as the biological reactor configuration and operational parameters [hydraulic retention time (HRT), sludge retention time, and pH] (Dialynas and Diamadopoulos, 2012; Wang and Wang, 2016).
Pharmaceuticals and Personal Care Products: Waste Management and Treatment Technology. DOI: https://doi.org/10.1016/B978-0-12-816189-0.00015-9 © 2019 Elsevier Inc. All rights reserved.
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Several literature studies have reviewed the removal efficiency of PPCPs by biological treatment (Miege et al., 2009; Verlicchi et al., 2012; Wang and Wang, 2016). However, the specific roles of biological processes such as adsorption, transmembrane process, and intercellular enzymatic degradation have not been discussed. During the removal and biotransformation of PPCPs in wastewaters, several interfacial processes can be recognized (Zhang and Zhu, 2014): (1) sorption of PPCP molecules onto biomass surface from the wastewater aqueous phase (Wells, 2006; Dialynas and Diamadopoulos, 2012; Vasiliadou et al., 2013; Blair et al., 2015); (2) transmembrane process from the outer surface of biological cells into the internal matrix (Song et al., 2010; Zhang et al., 2013) which is a ratelimited process in the biodegradation kinetics of organic compounds, especially for hydrophobic compounds, due to the cytoplasmic membrane lipids acting as a “barrier” (Hearn et al., 2009; Wiener and Horanyi, 2011); and (3) biotransformation or mineralization of PPCPs in biomass cells facilitating with intercellular enzymes (Kagle et al., 2009; Onesios et al., 2009; Alvarino et al., 2016). In this chapter, we mainly discuss the biological sorption of PPCPs by biosolids, transmembrane transport, and underlying mechanisms of PPCPs, typical intercellular catalytic degradation pathways of typical PPCPs, as well as the role of each biological process or interfacial behavior in the bioremoval and biotransformation of PPCPs in wastewater.
15.1
Biological sorption of typical pharmaceutical and personal care products
15.1.1 Biological sorption of pharmaceutical and personal care products During the passage through the WWTPs, PPCPs in influents can be significantly reduced by two processes: sorption and biodegradation (Dialynas and Diamadopoulos, 2012; Jelic et al., 2011; Blair et al., 2015; Fernandez-Fontaina, et al., 2013). The sorption of organic pollutants including PPCPs by biosolids or biomass can be recognized as a branch of biotechnology that effectively reduces chemical concentrations in wastewater influents, and as a crucial step for the subsequent transmembrane transport and intercellular biodegradation (Bokbolet et al., 1999; Zhang et al., 2018). Furthermore, the high frequency of PPCPs and their metabolites in biosolids have been measured (Miao et al., 2005; McClellan and Halden, 2010). Sorption of these micropollutants onto sludge/biosolids may also represent their significant route into surface water or soil, or when improper treatment of sludge occurs or is used as fertilizer on agricultural land (Ternes et al., 2004). Sorption is designated as a removal mechanism for many compounds, and knowledge of the sorption properties of PPCPs on biosolids will allow for a better understanding of their fate and impact to the environment. However, the little information on the sorption of PPCPs during wastewater treatment available is also
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confusing. Furthermore, not much is known about the occurrence and behavior of PPCPs in biosolids during WWTPs and its impact on the fate of these micropollutants. The sorption of hydrophobic organic compounds—especially nonpolar organic compounds such as polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and organic pesticides—has been extensively studied for soil, sewage sludge, microorganisms, and nanoparticles (Steen and Karickhoff, 1984; Tsezos and Bell, 1989; Chung et al., 2007; Li et al., 2007; Carballa et al., 2008; Chen et al., 2010; Lin and Gan, 2011; Zhuang et al., 2011; Zhang and Zhu, 2012; Yu et al., 2013; Zhang et al., 2018). The partition/distribution coefficient (Kd) and carbon normalized distribution coefficient (Koc 5 Kd/foc, where foc is the carbon content of the sorbent) are considered as good parameters to evaluate the sorption property of hydrophobic organic compounds (HOCs) from aqueous solutions onto solid phase or matrix. Linear correlations can be often observed between log Kd (or log Koc) with log Kow for tested organic pollutants on particular sorbents, where Kow is the octanol water distribution coefficient and represents the hydrophobicity of organic pollutants. Using these correlations, we can evaluate the sorption and fate of various pollutants in wastewater during their treatment. For example, based on reasonable assumptions for nonspecific lipophilic interactions, several relationships between log Kd (or log Koc) and log Kow were obtained and could be used to predict the sorption behavior for various hydrophobic organic compounds and microorganism-derived biosorbents (Zhang et al., 2018): G
G
G
G
Bacterial biomass for PAHs: log Kd 5 1.01 log Kow 2 0.74 (Zhang et al., 2018). Bacterial suspensions for unsubstituted and methylated PAHs: log Kd 5 0.98( 6 0.36) log Kow 2 2.71( 6 1.55) (Dimitriou-Christidis et al., 2007). White-rot fungi for PAHs: log Koc 5 1.13 log Kow 2 0.84 (Chen et al., 2010). River sediments for PAHs and their derivatives: log Koc 5 1.00 log Kow 2 0.21 (Karickhoff et al., 1979).
Furthermore, the sorption process could facilitate the subsequent biodegradation efficiency. Previous work has indicated that the improvement of sorption Kd of pyrene resulted in an improvement of its biodegradation efficiency (B ) by a Klebsiella oxytoca strain, as shown in Fig. 15.1 (Zhang et al., 2013; Zhang, 2013). The sorption process controls the removal, biotransformation, fate, and eco-risk of PPCPs in sewage treatment plants and other aquatic ecosystems. If we can predict the sorption behavior simply by Kd values or other sorption parameters, this would lower the economic and time costs for analyses of sludge and other biosolid samples. However, for pseudo-hydrophobic organic compounds (the log Kow of PPCPs ranges from 22.64 to 7.1), sorption data collected from recent works with available Kd or Koc parameters on PPCPs was insufficient (only 273 valid data were obtained) and showed significant deviations from their Kow, as shown in Fig. 15.2 and Table 15.1. The plots of log Kd with log Kow do not show a good relationship, which is consistent with previous studies (Wells, 2006). Moreover, the values of sorption parameter for a certain compound would be totally different and cover a large range in different studies. For example, the sorption parameter (log Kd) of
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Figure 15.1 The relationship between apparent biodegradation efficiency (B ) with apparent sorption affinity parameter Kd by a Klebsiella oxytoca strain.
Figure 15.2 The relationship of log Kd with log Kow of target PPCPs (data directly obtained and indirectly calculated from the literature is shown in Table 15.1). PPCP, Pharmaceutical and personal care product.
carbamazepine (CBZ) onto different sludges in different bioreactors ranged from 22.00 to 2.44 L/kg (Jones et al., 2002; Ternes et al., 2004; Miao et al., 2005; Carballa et al., 2008; Jelic et al., 2011; Conkle et al., 2012; Dialynas and Diamadopoulos, 2012; Fernandez-Fontaina et al., 2013; Yu et al., 2013; Vasiliadou
Table 15.1 Sorption properties of pharmaceutical and personal care products onto biosolids Compounds
MWa
log Kow
pKa
10,11-Dihydroxycarbamazepine 17α-Ethinylestradiol
270.3 296.4
0.13 4.20
10.4
17α-Ethinylestradiol
296.4
4.20
10.4
17α-Ethinylestradiol
296.4
4.20
10.4
17α-Ethinylestradiol 17α-Ethinylestradiol 17α-Ethinylestradiol 17α-Ethinylestradiol 17β-Estradiol
296.4 296.4 296.4 296.4 272.4
4.20 4.20 4.20 4.20 3.90
10.4 10.4 10.4 10.4 10.4
17β-Estradiol
272.4
3.90
10.4
17β-Estradiol
272.4
3.90
10.4
17β-Estradiol
272.4
3.90
10.4
17β-Estradiol 17β-Estradiol 2-Hydroxycarbamazepine 3-Hydroxycarbamazepine 4-Androstene-3,17-dione 4-Tert-octylphenol 4-Tert-octylphenol 4-Tert-octylphenol Acetaminophen
272.4 272.4 252.3 252.3 286.4 206.3 206.3 206.3 151.2
3.90 3.90 2.25 2.41 2.75e 5.00 5.00 5.00 0.46f
10.4 10.4
9.4g
Bioreactor
log Kd (L/kg)
log Koc (L/kg)
Primary sludge Mesophilic digested sludge Thermophilic digested sludge Immersed membrane bioreactor Primary sludge Secondary sludge CASb UASBc Mesophilic digested sludge Thermophilic digested sludge Immersed membrane bioreactor Sludge
0.84 2.53
3.40
Miao et al. (2005) Carballa et al. (2008)
2.13
2.95
Carballa et al. (2008)
CAS UASB Primary sludge Primary sludge CAS Soil IVC Soil PDS Soil WPL CAS
pH
log Dow
kbiol (L/g/day)
2.99 2.44 2.54 2.30 2.48 2.66
2.90 2.93 7.00 0.040 3.53
Reference
Dialynas and Diamadopoulos (2012) Ternes et al. (2004) Ternes et al. (2004) Alvarino et al. (2014) Alvarino et al. (2014) Carballa et al. (2008)
n.d.d
Carballa et al. (2008)
2.34
Dialynas and Diamadopoulos (2012) Stuer-Lauridsen et al. (2000) Alvarino et al. (2014) Alvarino et al. (2014) Miao et al. (2005) Miao et al. (2005) Blair et al. (2015) Yu et al. (2013) Yu et al. (2013) Yu et al. (2013) Blair et al. (2015)
3.17 2.90 2.40 1.43 1.36 3.26 1.28 1.07 1.63 1.92
19 0.070
3.15 3.46 3.15
(Continued)
Table 15.1 (Continued) Compounds
MWa
log Kow
pKa
Bioreactor
log Kd (L/kg)
Acetylsalicylic acid
180.2
1.19
3.5
Sludge
0.34
Acetylsalicylic acid Allopurinol Amlodipine
180.2 136.1 408.9
1.19 2 0.55 3.00
3.5 9.3 9.4h
Primary sludge Primary sludge Sludge
0.35 2 1.40 2.16
1.00 1.29
Amoxycillin
365.4
0.87
Primary sludge
0.03
2.94
Ampicillin
349.4
1.35g
CAS
1.48
Atenolol Atenolol Atorvastatin
266.3 266.3 588.7
0.16 0.16 6.36j
Azithromycin Azithromycin Azithromycin Bendroflumethiazide
749.0 749.0 749.0 421.4
4.02g 4.02g 4.02g 1.19
3.2 11.7h 2.5 7.3g 9.2 9.2 4.3 14.9h 8.7g 8.7g 8.7g 8.5g
Bezafibrate Bisphenol A Bisphenol A Bisphenol A Budesonide
361.8 228.3 228.3 228.3 430.5
3.80 3.32e 3.32e 3.32e 1.36
9.6k 9.6k 9.6k
Caffeine Caffeine
194.2 194.2
2 0.07e 2 0.07e
10.4e 10.4e
log Koc (L/kg)
pH
log Dow
kbiol (L/g/day)
Reference Stuer-Lauridsen et al. (2000) Jones et al. (2002) Jones et al. (2002) Stuer-Lauridsen et al. (2000) Jones et al. (2002)
2 0.552
Blair et al. (2015)
Primary sludge WWTP1 WWTP1
2 0.68 1.67 3.15
2.17 1.79 3.27
CAS CAS Primary sludge Sludge
2.58 2.11 2.56 0.34
2.97
WWTP1 Soil IVC Soil PDS Soil WPL Sludge
2.23 0.83 0.70 0.94 0.52
2.36 2.70 3.09 2.46
CAS Suspended growth reactor
2.15 2 1.22
7.3 7.3
0.157 2 1.75i 2 1.24i
Jones et al. (2002) Jelic et al. (2011) Jelic et al. (2011)
7.3
0.100
Go¨bel et al. (2005) Blair et al. (2015) Ivanova´ et al. (2017) Stuer-Lauridsen et al. (2000) Jelic et al. (2011) Yu et al. (2013) Yu et al. (2013) Yu et al. (2013) Stuer-Lauridsen et al. (2000) Blair et al. (2015) Vasiliadou et al. (2013)
Caffeine Carbamazepine Carbamazepine Carbamazepine
194.2 236.3 236.3 236.3
2 0.07e 2.45 2.45 2.45
10.4e 13.9 13.9 13.9
Carbamazepine
236.3
2.45
13.9
Carbamazepine Carbamazepine Carbamazepine
236.3 236.3 236.3
2.45 2.45 2.45
13.9 13.9 13.9
Carbamazepine
236.3
2.45
13.9
Carbamazepine Carbamazepine
236.3 236.3
2.45 2.45
13.9 13.9
Carbamazepine
236.3
2.45
13.9
Carbamazepine Carbamazepine Carbamazepine Carbamazepine Carbamazepine Carbamazepine Celestolide
236.3 236.3 236.3 236.3 236.3 236.3 244.4
2.45 2.45 2.67l 2.45 2.45 2.45 5.00
13.9 13.9 13.9 13.9 13.9 13.9
Celestolide
244.4
Celestolide Celestolide Chlortetracycline Cimetidine Ciprofloxacin
244.4 244.4 478.9 252.3 331.3
A2/O reactor Primary sludge Secondary sludge Mesophilic digested sludge Thermophilic digested sludge Primary sludge WWTP1 Continuous membrane reactor Batch reactor
2.90 n.d. 0.08 1.55
n.d. 0.54 2.42
Ashfaq et al. (2017) Ternes et al. (2004) Ternes et al. (2004) Carballa et al. (2008)
1.31
2.12
Carballa et al. (2008)
1.41 1.67 0.43
3.59 1.79 1.24
7.3 7.0
2.25 2 4.15i 2 3.05i
0.00
0.44
8.4
2 4.45i
1.00 2.25
5.00
CAS Immersed membrane reactor Suspended growth reactor Aerobic wetland Anaerobic wetland Primary sludge Soil IVC Soil PDS Soil WPL Continuous membrane reactor Batch reactor
5.00 5.00 2 1.30 0.40 2 1.74
CAS UASB A2/O reactor Primary sludge Sludge
6.8 6.1
2 2.00
0.01 0.01
0.09024
1.07 0.99 2.44 0.40 0.16 0.50 3.77
2.27 2.55 2.02 4.58
7.0
6.13
3.71
4.15
8.4
9.11
3.48 3.08 5.59 2 0.44 2 2.59
1.56 1.48
63 0.05 2.84
2 0.0124
Jones et al. (2002) Jelic et al. (2011) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013) Blair et al. (2015) Dialynas and Diamadopoulos (2012) Vasiliadou et al. (2013) Conkle et al. (2012) Conkle et al. (2012) Miao et al. (2005) Yu et al. (2013) Yu et al. (2013) Yu et al. (2013) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013) Alvarino et al. (2014) Alvarino et al. (2014) Ashfaq et al. (2017) Jones et al. (2002) Stuer-Lauridsen et al. (2000)
(Continued)
Table 15.1 (Continued) Compounds
MWa
log Kow
pKa
Bioreactor
log Kd (L/kg)
Ciprofloxacin Ciprofloxacin Ciprofloxacin Citalopram
331.3 331.3 331.3 324.4
2 1.74 2 1.74 2 1.74 2.86
6.1 6.1 6.1
CAS Primary sludge A2/O reactor Sludge
2.70 4.40 2.02 2.02
Citalopram Clarithromycin Clarithromycin Clarithromycin Clarithromycin Clofibric acid Clofibric acid Clofibric acid
324.4 748.0 748.0 748.0 748.0 214.6 214.6 214.6
2.86 3.16m 3.16m 3.16m 3.16m 2.57 2.57 2.57
3.20 2.42 2.14 2.11 2.85 n.d. 0.68 2.30
Codeine Cotinine Cyclophosphamide Cyclophosphamide Danofloxacin Diazepam Diazepam Diazepam
176.2 176.2 261.1 261.1 357.4 284.7 284.7 284.7
1.19g 0.07g 0.63 0.63 2 0.30 2.82 2.82 2.85
10.6n
Primary sludge CAS WWTP1 CAS Primary sludge Primary sludge Secondary sludge Immersed membrane bioreactor CAS CAS Primary sludge Secondary sludge A2/O reactor Primary sludge Secondary sludge Sludge
Diazepam Diazepam
284.7 284.7
2.85 2.85
3.3 3.3
Diazepam
284.7
2.85
3.3
9.0m 9.0m 9.0m 9.0m
3.3 3.3 3.3
WWTP1 Continuous membrane reactor Batch reactor
1.15 1.53 1.74 0.38 3.59 1.64 1.32 2.01
log Koc (L/kg)
2.82 2.26
pH
log Dow
7.3
1.45i
kbiol (L/g/day)
1.15
2.20 0.85
3.15 1.51
3.27 2.32
7.3 7.0
2 1.15i 2 0.85i
0
1.70
2.14
8.4
2 2.25i
0.19
Reference Blair et al. (2015) Ivanova´ et al. (2017) Ashfaq et al. (2017) Stuer-Lauridsen et al. (2000) Ivanova´ et al. (2017) Go¨bel et al. (2005) Jelic et al. (2011) Blair et al. (2015) Ivanova´ et al. (2017) Ternes et al. (2004) Ternes et al. (2004) Dialynas and Diamadopoulos (2012) Blair et al. (2015) Blair et al. (2015) Ternes et al. (2004) Ternes et al. (2004) Ashfaq et al. (2017) Ternes et al. (2004) Ternes et al. (2004) Stuer-Lauridsen et al. (2000) Jelic et al. (2011) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013)
Diazepam Diazepam Diclofenac Diclofenac Diclofenac
284.7 284.7 296.1 296.1 296.1
2.85 2.85 4.60 4.60 4.50
3.3 3.3 4.2 4.2 4.2
CAS UASB Primary sludge Secondary sludge Mesophilic digested sludge Thermophilic digested sludge Primary sludge WWTP1 Continuous membrane reactor Batch reactor
1.48 2.60 2.66 1.20 1.82
Diclofenac
296.1
4.50
4.2
Diclofenac Diclofenac Diclofenac
296.1 296.1 296.1
0.70 4.50 4.50
4.2 4.2 4.2
Diclofenac
296.1
4.50
4.2
Diclofenac Digoxigenin Digoxin
296.1 390.5 780.9
4.50 1.10e 1.26
Diltiazem Diltiazem hydrochloride Diphenhydramine Enalapril
414.5 451.0 255.4 376.5
2.70e 2.70 3.27e 4.22
Erythromycin Erythromycin
733.9 733.9
Erythromycin Erythromycin Erythromycin Erythromycin Erythromycin
0.4 0.004 3.12 1.67 2.68
Alvarino et al. (2014) Alvarino et al. (2014) Ternes et al. (2004) Ternes et al. (2004) Carballa et al. (2008)
1.79
2.61
Carballa et al. (2008)
2 0.14 2.09 1.89
2.92 2.22 2.71
7.3 7.0
2 2.12 1.40i 1.70i
0.1
1.51
1.95
8.4
0.30i
0.02
4.2
A2/O reactor CAS Sludge
3.06 2.18 0.41
CAS Primary sludge CAS Sludge
1.34 1.86 2.23 3.38
3.06 3.06
8.1o 7.7 9.0f 3.0 5.4p 8.9 8.9
2.22 1.01
1.00 1.82
733.9
3.06
8.9
Primary sludge Continuous membrane reactor Batch reactor
1.45
1.89
733.9 733.9 733.9 733.9
3.06 3.06 3.06 3.06
8.9 8.9 8.9 8.9
CAS UASB Primary sludge A Primary sludge B
1.70 1.48 2.01 1.98
3.98
2.62
7.0 8.4
2.13 2.44i
0.31 0.52 3 0.001
Jones et al. (2002) Jelic et al. (2011) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013) Ashfaq et al. (2017) Blair et al. (2015) Stuer-Lauridsen et al. (2000) Blair et al. (2015) Jones et al. (2002) Blair et al. (2015) Stuer-Lauridsen et al. (2000) Jones et al. (2002) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013) Alvarino et al. (2014) Alvarino et al. (2014) Xu et al. (2007) Xu et al. (2007)
(Continued)
Table 15.1 (Continued) Compounds
MWa
log Kow
pKa
Bioreactor
log Kd (L/kg)
Erythromycin Erythromycin
733.9 733.9
3.06 3.06
8.9 8.9
Primary sludge C Primary sludge D
2.23 2.38
Xu et al. (2007) Xu et al. (2007)
Estrogen
272.4
5.07
5.7
Sludge
4.23
Estrone
270.4
4.10
10.4
2.48
3.35
Estrone
270.4
4.10
10.4
2.18
3.00
Carballa et al. (2008)
Estrone Estrone Fenoprofen Ferrous sulfate Fluoxetine
270.4 270.4 242.3 151.9 309.3
4.10 4.10 3.10g 2 0.37 4.05g
10.4 10.4 7.3p
Fluoxetine
309.3
4.05g
Mesophilic digested sludge Thermophilic digested sludge CAS UASB A2/O reactor Primary sludge Continuous membrane reactor Batch reactor
Stuer-Lauridsen et al. (2000) Carballa et al. (2008)
n.d.
Fluoxetine Fluoxetine Fluoxetine Furosemide
309.3 309.3 309.3 330.7
4.05g 4.05g 4.05g 2.03
CAS CAS UASB Sludge
3.08 3.40 2.85 1.19
Furosemide Galaxolide Galaxolide Galaxolide
330.7 254.8 254.8 254.8
2.03 5.90 5.90 5.90
2.24 3.69 3.26 4.12
2.37 4.15 3.72 4.99
Galaxolide
254.8
5.90
WWTP1 Primary sludge Secondary sludge Mesophilic digested sludge Thermophilic digested sludge
3.83
4.64
3.9 3.9
2.18 2.48 3.91 2 1.22 2.55
log Koc (L/kg)
1.16 3.36
pH
log Dow
kbiol (L/g/day)
Reference
2 0.04
Alvarino et al. (2014) Alvarino et al. (2014) Ashfaq et al. (2017)
7.0
1.98
8.4
n.d.
Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013)
10 0.003
7.3
2 1.37i
Alvarino et al. (2014) Alvarino et al. (2014) Stuer-Lauridsen et al. (2000) Jelic et al. (2011) Ternes et al. (2004) Ternes et al. (2004) Carballa et al. (2008) Carballa et al. (2008)
Galaxolide
254.8
5.90
Continuous membrane reactor
3.27
4.08
7.0
0.9
Fernandez-Fontaina et al. (2013)
Galaxolide
254.8
5.90
Batch reactor
3.21
6.65
8.4
1.71
Galaxolide Galaxolide Galaxolide
254.8 254.8 254.8
5.90 5.90 5.90
CAS UASB Secondary sludge
3.41 3.57 3.20
Gemfibrozil Gemfibrozil Gemfibrozil Gemfibrozil Gemfibrozil Gemfibrozil Glibenclamide Glibenclamide Gliclazide Hydrochlorthiazide
250.3 250.3 250.3 250.3 250.3 250.3 494.0 494.0 323.4 297.7
4.77j 4.77j 4.77j 4.77j 4.77j 4.77j 4.70j 4.70j 2.60g 2 0.07g
0.95 0.32 0.42 0.41 0.19 0.60 3.15 3.58 1.25 2 1.00
Ibuprofen Ibuprofen Ibuprofen
206.3 206.3 206.3
3.50 3.50 4.00
7.9 9.2p 4.5 4.5 4.5
CAS Aerobic wetland Anaerobic wetland Soil IVC Soil PDS Soil WPL WWTP1 A2/O reactor Primary sludge Sludge
n.d. 0.85 1.58
1.32 2.44
Ibuprofen
206.3
4.00
4.5
1.32
2.14
Carballa et al. (2008)
Ibuprofen Ibuprofen
206.3 206.3
3.50 3.50
4.9 4.5
Primary sludge Secondary sludge Mesophilic digested sludge Thermophilic digested sludge Primary sludge Sludge
Fernandez-Fontaina et al. (2013) Alvarino et al. (2014) Alvarino et al. (2014) Artola-Garicano et al. (2003) Blair et al. (2015) Conkle et al. (2012) Conkle et al. (2012) Yu et al. (2013) Yu et al. (2013) Yu et al. (2013) Jelic et al. (2011) Ashfaq et al. (2017) Jones et al. (2002) Stuer-Lauridsen et al. (2000) Ternes et al. (2004) Ternes et al. (2004) Carballa et al. (2008)
2.66 2.66
2.60
Ibuprofen
206.3
3.50
4.5
2.05
2.86
7.0
0.85i
38.07
Ibuprofen
206.3
3.50
4.5
Continuous membrane reactor Batch reactor
1.38
1.82
8.4
2 0.55i
6.03
Jones et al. (2002) Stuer-Lauridsen et al. (2000) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013)
4.5q 4.5q 4.5q 4.5q 4.5q 4.5q
41 0.01 0.071
0.8 2.28 2.58 2.12 3.27
7.3
1.67i
4.10
(Continued)
Table 15.1 (Continued) Compounds
MWa
log Kow
pKa
Bioreactor
log Kd (L/kg)
Ibuprofen Ibuprofen Ibuprofen Ibuprofen Ibuprofen Ibuprofen Ifosfamide Ifosfamide Iopromide
206.3 206.3 206.3 206.3 206.3 206.3 261.1 261.1 791.1
3.50 3.50 3.50 3.50 3.50 3.50 0.86 0.86 2 2.30
4.5 4.5 4.5 4.5 4.5 4.5
2.23 2.38 2.00 0.39 2 0.34 2.43 1.34 0.15 0.84
1.79 0.61 1.71
Blair et al. (2015) Alvarino et al. (2014) Alvarino et al. (2014) Conkle et al. (2012) Conkle et al. (2012) Ashfaq et al. (2017) Ternes et al. (2004) Ternes et al. (2004) Carballa et al. (2008)
Iopromide
791.1
2 2.30
9.9
0.46
1.28
Carballa et al. (2008)
Ketoprofen Lorazepam Mebeverine hydrochloride Mefenamic acid Mefenamic acid Mefenamic acid Mesalazine
254.3 321.2 466.0 241.3 241.3 241.3 153.1
3.12g 2.39g 3.82 5.12 5.12 5.12 0.98
4.45g 13.0g
CAS CAS UASB Aerobic wetland Anaerobic wetland A2/O reactor Primary sludge Secondary sludge Mesophilic digested sludge Thermophilic digested sludge A2/O reactor WWTP1 Primary sludge Primary sludge WWTP1 A2/O reactor Primary sludge
Metformin Metformin hydrochloride Methyl paraben N,N-diethyl-meta-toluamide N,N-diethyl-meta-toluamide Naproxen
129.2 165.6 155.2 191.3 191.3 230.3
2 2.64j 2 2.64 2.02e 2.02 e 3.18
4.2
0.48 2 3.52 3.85 1.28 0.32 1.03
1.76 0.80 1.90
Blair et al. (2015) Jones et al. (2002) Ashfaq et al. (2017) Conkle et al. (2012) Conkle et al. (2012) Carballa et al. (2008)
Naproxen
230.3
3.18
4.2
CAS Primary sludge A2/O reactor Aerobic wetland Anaerobic wetland Mesophilic digested sludge Thermophilic digested sludge
1.09
1.91
Carballa et al. (2008)
9.9
4.2g 4.2g 4.2g 2.09 5.26g 12.4q 12.4
3.80 2.34 2.98 4.28 2.71 3.12 0.14
log Koc (L/kg)
pH
log Dow
kbiol (L/g/day) 2 0.007
0.88 0.15
2.47 5.82 2.66 2.84
7.3
2 3.31i
7.3
2.02i
1.00
2.02
2 2.64
Reference
Ashfaq et al. (2017) Jelic et al. (2011) Jones et al. (2002) Jones et al. (2002) Jelic et al. (2011) Ashfaq et al. (2017) Jones et al. (2002)
Naproxen Naproxen
230.3 230.3
3.18 3.18
4.2 4.2
Naproxen
230.3
3.18
4.2
Naproxen Naproxen Naproxen Nitrazepam
230.3 230.3 230.3 281.3
3.18 3.18 3.18 2.25
4.2 4.2 4.2
Norfloxacin
319.3
0.46p
Norfloxacin
319.3
0.46p
Norfloxacin
319.3
0.46p
Norfloxacin
319.3
0.46p
Norfloxacin
319.3
0.46p
Norfloxacin
319.3
0.46p
Octocrylene Ofloxacin
361.5 361.4
7.10 2 0.39e
Ofloxacin
361.4
2 0.39e
Ofloxacin
361.4
2 0.39e
Ofloxacin
361.4
2 0.39e
Ofloxacin
361.4
2 0.39e
6.3 8.8p 6.3 8.8p 6.3 8.8p 6.3 8.8p 6.3 8.8p 6.3 8.8p 6.0 9.3r 6.0 9.3r 6.0 9.3r 6.0 9.3r 6.0 9.3r
Primary sludge Continuous membrane reactor Batch reactor
2.34 1.55
2.54 2.36
7.0
0.33 0.38i
4.23
CAS
2.28
Jones et al. (2002) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013) Blair et al. (2015) Alvarino et al. (2014) Alvarino et al. (2014) Stuer-Lauridsen et al. (2000) Blair et al. (2015)
1.22
1.67
8.4
2 1.02i
0.5
CAS CAS UASB Sludge
2.08 2.00
Primary sludge A
3.84
Xu et al. (2007)
Primary sludge B
2.81
Xu et al. (2007)
Primary sludge C
3.75
Xu et al. (2007)
Primary sludge D
3.64
Xu et al. (2007)
A2/O reactor
4.92
Ashfaq et al. (2017)
A2/O reactor CAS
3.13 2.40
Ashfaq et al. (2017) Blair et al. (2015)
1.41
1 1.11
Primary sludge A
3.54
Xu et al. (2007)
Primary sludge B
3.81
Xu et al. (2007)
Primary sludge C
3.33
Xu et al. (2007)
Primary sludge D
3.70
Xu et al. (2007)
(Continued)
Table 15.1 (Continued) Compounds
MWa
log Kow
pKa
Bioreactor
Ofloxacin
361.4
2 0.39e
A2/O reactor
Oxytetracycline
460.4
2 0.90
Oxytetracycline
460.4
2 0.90
Oxytetracycline
460.4
2 0.90
Paracetamol Paracetamol
151.2 151.2
0.46 0.49
6.0 9.3r 3.3 7.3 3.3 7.3 3.3 7.3 9.4 9.5
Paraxanthine Penicillin G Phenoxymethylpenicillin Progesterone Propyl paraben Quinine sulfate Radiolabeled iopromide Ranitidine Ranitidine Ranitidine
180.2 334.4 350.4 314.5 180.2 746.9 791.1 314.4 314.4 314.4
2 0.20 1.83e 2.09 3.87e 2.40 5.40 2 2.33 0.27g 0.27g 0.27g
Roxithromycin
837.1
2.40
9.2
Roxithromycin
837.1
2.40
9.2
Roxithromycin
837.1
2.40
9.2
2.7g 2.7g
log Kd (L/kg)
pH
log Dow
5.56
Primary sludge
2 1.70
Sludge
2 1.74
A2/O reactor
log Koc (L/kg)
2 0.38 2 0.40
CAS CAS Primary sludge CAS A2/O reactor Primary sludge Secondary sludge WWTP1 CAS Suspended growth reactor Mesophilic digested sludge Thermophilic digested sludge Continuous membrane reactor
1.93 2.49 1.25 3.28 3.55 4.56 1.04 1.90 2.63 2 0.38
Reference Ashfaq et al. (2017)
2 4.63
1.99
Jones et al. (2002) Stuer-Lauridsen et al. (2000) Ashfaq et al. (2017)
5.01
Primary sludge Sludge
kbiol (L/g/day)
1.79
0.46
1.89
8.27 1.51 2.03
7.3
Jones et al. (2002) Stuer-Lauridsen et al. (2000) Blair et al. (2015) Blair et al. (2015) Jones et al. (2002) Blair et al. (2015) Ashfaq et al. (2017) Jones et al. (2002) Ternes et al. (2004) Jelic et al. (2011) Blair et al. (2015) Vasiliadou et al. (2013)
1.92
2.79
Carballa et al. (2008)
1.14
1.95
Carballa et al. (2008)
1.34
2.15
7.0
0.20i
Fernandez-Fontaina et al. (2013)
2.15
8.4
1.54i
1.38 2.12 2.17
7.3 7.0
2 3.37i
Roxithromycin
837.1
2.40
9.2
Batch reactor
1.71
Roxithromycin Roxithromycin Roxithromycin Roxithromycin Roxithromycin Roxithromycin Salbutamol
837.1 837.1 837.1 837.1 837.1 837.1 239.3
2.40 2.40 2.40 2.40 2.40 2.40 0.01
9.2 9.2 9.2 9.2 9.2 9.2 10.3g
CAS UASB Primary sludge A Primary sludge B Primary sludge C Primary sludge D Sludge
2.00 1.60 2.95 2.36 2.90 3.08 2 1.00
Sildenafil Sodium valproate Sotalol Sulfadimethoxine Sulfadimidine Sulfamethoxazole
474.6 166.2 272.4 310.3 278.3 253.3
2.75f 2 0.85 0.24g 1.63 0.89g 0.89
6.0s 4.6p
3.09 2 1.70 2.00 2.04 1.95 1.36
Sulfamethoxazole
253.3
0.89
1.8
Sulfamethoxazole Sulfamethoxazole
253.3 253.3
0.89 0.89
1.8 1.8
Sulfamethoxazole
253.3
0.89
1.8
A2/O reactor Primary sludge WWTP1 Sludge Primary sludge B Mesophilic digested sludge Thermophilic digested sludge CAS Continuous membrane reactor Batch reactor
Sulfamethoxazole Sulfamethoxazole Sulfamethoxazole
253.3 253.3 253.3
0.89 0.89 0.89
1.8 1.8 1.8
1.00 1.60 1.53
Sulfamethoxazole
253.3
0.89
1.8
1.60
Alvarino et al. (2016)
Sulfamethoxazole
253.3
0.89
1.8
1.51
Alvarino et al. (2016)
Sulfamethoxazole
253.3
0.89
1.8
CAS Anaerobic degradation Heterotrophic aerobic degradation Heterotrophic denitrification Autotrophic denitrification Autotrophic nitrification
Go¨bel et al. (2005) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013) Blair et al. (2015) Alvarino et al. (2016) Alvarino et al. (2016)
0.85
Alvarino et al. (2016)
2.1 7.6g 1.8
2.23
Fernandez-Fontaina et al. (2013) Alvarino et al. (2014) Alvarino et al. (2014) Xu et al. (2007) Xu et al. (2007) Xu et al. (2007) Xu et al. (2007) Stuer-Lauridsen et al. (2000) Ashfaq et al. (2017) Jones et al. (2002) Jelic et al. (2011) Yang et al. (2011) Xu et al. (2007) Carballa et al. (2008)
1.18
2.00
Carballa et al. (2008)
2.41 0.93
2.81 1.75
7.0
2 4.31i
1.04
1.48
8.4
2 5.71i
(Continued)
Table 15.1 (Continued) Compounds
MWa
log Kow
pKa
Bioreactor
log Kd (L/kg)
Sulfamethoxazole Sulfamethoxazole Sulfamethoxazole Sulfamethoxazole Sulfamethoxazole Sulfamonomethoxine Sulfapyidine Sulfasalazine
253.3 253.3 253.3 253.3 253.3 280.3 249.3 398.4
0.89 0.89 0.89 0.89 0.89 0.70 0.35g 3.81
1.8 1.8 1.8 1.8 1.9 2.0 8.4g 2.3 6.5q
CAS UASB Primary sludge B A2/O reactor Sludge Sludge CAS Primary sludge
1.90 1.65 2.41 1.86 1.46 1.75 2.47 2.97
Terbutaline
255.3
0.48
Sludge
2 0.40
Testosterone Tetracycline Thiabendazole Thiabendazole Tonalide Tonalide Tonalide
288.4 444.4 201.2 201.2 258.3 258.3 258.3
3.32e 2 1.37e 2.47g 2.47g 5.70 5.70 5.80
3.15 4.86 2.18 1.81 3.72 3.38 4.18
4.18 3.85 5.05
Tonalide
258.3
5.80
4.00
4.82
Carballa et al. (2008)
Tonalide
258.3
5.70
3.53
4.34
7.0
0.9
Tonalide
258.3
5.70
CAS A2/O reactor CAS A2/O reactor Primary sludge Secondary sludge Mesophilic digested sludge Thermophilic digested sludge Continuous membrane reactor Batch reactor
Stuer-Lauridsen et al. (2000) Blair et al. (2015) Ashfaq et al. (2017) Blair et al. (2015) Ashfaq et al. (2017) Ternes et al. (2004) Ternes et al. (2004) Carballa et al. (2008)
3.43
3.88
8.4
3.87
Tonalide Tonalide Tonalide
258.3 258.3 258.3
5.70 5.70 5.70
CAS UASB Secondary sludge
3.78 3.48 3.10
3.3g 4.6g 4.6g
log Koc (L/kg)
1.58 1.87 2.87 3.26
pH
7.0 7.0
log Dow
kbiol (L/g/day)
Reference Alvarino et al. (2014) Alvarino et al. (2014) Xu et al. (2007) Ashfaq et al. (2017) Yang et al. (2011) Yang et al. (2011) Go¨bel et al. (2005) Jones et al. (2002)
2 4.12i 2 4.30i
38 0.04 0.023
Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013) Alvarino et al. (2014) Alvarino et al. (2014) Artola-Garicano et al. (2003)
Triclocarbon Triclosan Triclosan Triclosan Triclosan Trimethoprim Trimethoprim Trimethoprim Trimethoprim
315.6 289.5 289.5 289.5 289.5 290.3 290.3 290.3 290.3
4.90j 4.76g 4.76g 4.76g 4.76g 0.91 0.91 0.91 0.91
7.9g 7.9g 7.9g 7.9g 7.1 7.1 7.1 7.1
Trimethoprim
290.3
0.91
7.1
Trimethoprim Trimethoprim Trimethoprim Venlafaxine Xylometazolin
290.3 290.3 290.3 277.4 280.8
0.91 0.91 0.91 3.20g 4.91
7.1 7.1 7.1 10.1g
Zopiclone
388.8
0.25
CAS CAS Soil IVC Soil PDS Soil WPL CAS Soil WWTP1 Continuous membrane reactor Batch reactor
4.08 3.40 1.99 1.83 2.29 2.32 0.87 1.7 1.40
3.86 4.21 3.81 2.72 3.66 1.83 2.22
9.2 7.3 7.0
1.40
1.85
8.4
CAS CAS UASB Primary sludge Sludge
1.15 1.90 1.08 2.69 4.07
Sludge
1.41
WWTP, Wastewater treatment plant; IVC, Imperial Valley caly; PDS, Palmdale sand; WPL, Washington Palouse loam. a MW: molecular weight, g/mol. b CAS: conventional activated sludge. c UASB: upflow anaerobic sludge bed. d n.d.: no data. e Obtained from Hansch et al. (1995). f Obtained from Sangster (2014). g Obtained from DrugBank, http://www.drugbank.ca. h Obtained from ACE (2016). i Calculated values following the Eqs. (15.1) (15.3). j Obtained from US EPA. k Obtained from Zeng et al. (2006). l Use the log Kow without modification with respecting the original data. m Obtained from McFarland et al. (1997). n Obtained from Haynes (2012). o Obtained from Gerhartz (1985). p Obtained from O’Neil (2001). q Obtained from SPARC (2008). r Obtained from Tolls (2001). s Obtained from Royal Society of Chemistry.
Blair et al. (2015) Blair et al. (2015) Yu et al. (2013) Yu et al. (2013) Yu et al. (2013) Go¨bel et al. (2005) Lin and Gan (2011) Jelic et al. (2011) Fernandez-Fontaina et al. (2013) Fernandez-Fontaina et al. (2013) Blair et al. (2015) Alvarino et al. (2014) Alvarino et al. (2014) Ivanova´ et al. (2017) Stuer-Lauridsen et al. (2000) Stuer-Lauridsen et al. (2000)
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et al., 2013; Blair et al., 2015). When normalized by organic carbon contents, large deviations of log Koc values with log Kow were obtained (as shown in Fig. 15.3). Taking CBZ as an example, log Koc values of 0.44 L/kg (Fernandez-Fontaina et al., 2013) and 3.59 L/kg (Jones et al., 2002) were obtained. Several studies even reported that the contribution of adsorption in conventional biological wastewater treatment for PPCPs could be neglected (Alvarino et al., 2016; Deng et al., 2016), while others found strong sorption affinities between biosolids and PPCPs such as galaxolide, tonalide, celestolide, and 17α-ethynil estradiol (Carballa et al., 2008; Jelic et al., 2011; Reif, 2011; Dialynas and Diamadopoulos, 2012). Similar trends have been observed in the sorption ability for PPCPs onto soil and sediments, the obtained sorption parameter Kd ranged from 0.4 to 269,097 L/kg for 11 PPCPs including bisphenol A, 17β-estradiol (E2), oxytetracycline, sulfathiazole, and sulfamethazine (Pan et al., 2009). There are two main sorption mechanisms during biological wastewater treatment, namely absorption and adsorption. 1. Absorption (or partition): This entails the hydrophobic interactions of the aliphatic and aromatic groups of a compound with the hydrophobic sites of biomass including biolipid chains and lipophilic cell membranes of the microorganisms, and the lipid fractions of the sludge (Ternes et al., 2004; Carballa et al., 2008; Chen et al., 2010; Zhang and Zhu, 2012; Zhang et al., 2013). 2. Adsorption: This is mainly the electrostatic interactions of positively charged groups of chemicals with the negatively charged surfaces of the microorganisms caused by the presence of carboxyl, hydroxyl, and phosphate groups (Golet et al., 2003; Siegrist et al., 2003; Zhang and Zhu, 2014).
Figure 15.3 The relationship of log Koc with log Kow of target PPCPs (data directly obtained and indirectly calculated from the literature is shown in Table 15.1). PPCP, Pharmaceutical and personal care product.
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359
For nonpolar HOCs (such as PAHs, PCBs, and insecticides) the sorption affinity to biosolids and other carbon-containing solids can mainly be attributed to absorption and good relationships can be established between log Kd (or log Koc) with the octanol water partitioning coefficient (log Kow) (Karickhoff et al., 1979; Chen et al., 2010; Zhang et al., 2018). However, as many PPCPs are moderately polar substances, these compounds might only interact with parts of organic matter or with the minerals of biosolids. In addition to the partition mechanism, specific electrostatic interactions may be relevant for some PPCPs. Unlike the nonpolar compounds, the sorption mechanism and behavior of polar molecules on biomass highly depend on the chemical form(s) in particular solution conditions. Therefore the pH of wastewater (usually between 7 and 9) or aqueous solutions will directly impact the ratio of the nonionized to ionized chemical form(s) of these PPCPs, which in turn impacts the success rate of PPCPs’ removal and sorption (Wells, 2006). In this situation, Kow values should not be the only parameter used to determine the sorption behavior. Recent studies revealed that the sorption parameters are dependent on the hydrophobicity ionogenicity profile of the chemical (Wells, 2006), and the pH of the wastewater undergoing treatment as well as the pKa and log Kow of targeted compounds are needed to fully evaluate the sorption process. A pHdependent octanol water distribution coefficient (Dow) was proposed to evaluate and predict the sorption of PPCPs. To calculate the log Dow value, three types of PPCPs were classified, namely neutral PPCPs (e.g., musk fragrances), acidic PPCPs (e.g., ibuprofen, naproxen, estrogens), and basic PPCPs (e.g., roxithromycin, iopromide, and clarithromycin). The value of log Dow can be calculated using the following equations (Stuer-Lauridsen et al., 2000; Jones et al., 2002; Ternes et al., 2004; Wells, 2006; Carballa et al., 2008). For PPCPs with only neutral moieties: log Dow 5 log Kow
(15.1)
For PPCPs with acidic moieties (e.g., log Dow 5 log Kow 1 log
COOH):
1 1 1 10pH2pKa
For PPCPs with basic moieties (e.g., log Dow 5 log Kow 1 log
OH,
RNH2,
1 1 1 10pKa2pH
(15.2) R2NH,
R3N): (15.3)
Similar to the log Kd 2 log Kow (or log Kd 2 log Kow) relationship, a log Kd 2 log Dow relationship was established by Carballa et al. (2008) for PPCPs and digested sludge, namely: log Koc 5 0:74 log Dow 1 0:15
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Pharmaceuticals and Personal Care Products: Waste Management and Treatment Technology
Figure 15.4 The relationship of log Kd with log Dow of target PPCPs (data directly obtained from the literature). PPCP, Pharmaceutical and personal care product.
In the literature, only 12 Kd 2 Dow data have been collected and plotted and are shown in Fig. 15.4 revealing the weak correlation found between log Kd values and log Dow values (R2 5 0.5638, n 5 12). Based on Eqs. (15.1) (15.3), 27 more Kd 2 Dow data were calculated considering the available pKa and the ambient pH. These data (39 Kd 2 Dow data) were then plotted and illustrated in Fig. 15.5. Unfortunately, no significant correlation was found, probably due to the limited valid data, different bioreactors, and other practical conditions such as HRT and sludge retention time. However, there seems to be a glimmer of hope. By reviewing the plots of log Kd and log Kow (total 273 data) collected from the literature (see Fig. 15.6), significant correlations between log Kd and log Kow were found (log Kd 5 0.998 log Kow 2 0.858, R2 5 0.9952; black open circle, n 5 72) which divides the disorderly data into three clear zones. These well-fitted data include neutral PPCPs (e.g., ifosfamide, diazepam), acidic PPCPs (e.g., ibuprofen, naproxen, and diclofenac), and basic PPCPs (e.g., erythromycin, roxithromycin, and clarithromycin). Further studies are required to discover the hidden information that can be derived from the data. Moreover, it would be appreciated if researchers and authors provide more condition parameters (including pH of wastewater, pKa of target compounds, Kd values or Koc values, specific organic carbon content of biosolids, and so on) in their studies and published papers.
15.1.2 Factors affecting sorption of pharmaceutical and personal care products In general, the sorption affinity of PPCPs with biosolids depends on the chemical nature of PPCPs (e.g., log Kow, pKa, and structure), coexisting substances,
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Figure 15.5 The relationship of log Kd with log Dow of target PPCPs (data directly obtained and indirectly calculated from the literature as listed in Table 15.1). PPCP, Pharmaceutical and personal care product.
Figure 15.6 Distinguishing the well-fitted data from the total collected data of log Kd versus log Kow.
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Pharmaceuticals and Personal Care Products: Waste Management and Treatment Technology
properties of the biosolids, and practical conditions such as pH, redox potentials, and contact time. These factors could also indirectly influence the subsequent biodegradation and advanced oxidation of PPCPs and the disposal of PPCPs-sorbed sludge through the changing of (bio)available PPCPs in the aqueous phase of wastewater. The chemical nature of the PPCPs plays a crucial role in their sorption (including adsorption and absorption) during the biological treatment of PPCP-bearing wastewater. Chemical properties of PPCPs, such as hydrophobicity and polarity, are considered to be the driving force of their environmental transport (Wells, 2006). Here we will consider polar properties and the octanol water coefficient: 1. Polar properties: Polar moieties are recognized as one of the key properties affecting the sorption efficiency and mechanisms by (bio)sorbents. Nonpolar PPCPs sorb onto biosolids mainly via hydrophobic interactions (e.g., π π interaction), which are usually resistant to a change of wastewater pH. However, the properties of the wastewater has a marked impact on the sorption of PPCPs with polar functional group(s) onto biosolids, owing to the interactions with biosolids via both partitioning and adsorption mechanisms. The polar substituent group(s) could also impact the sorption of PPCPs. For example, the Kd value of CBZ was about 277 L/kg (calculated value based original data) (Miao et al., 2005). Potential metabolites which have one or two hydroxyl group(s) (2-hydroxycarbamazepine, 3-hydroxycarbamazepine, and 10,11-dihydroxycarbamazepine) have much lower Kd values of 26.9, 23.1, and 6.94 L/kg, respectively (calculated values based on original data) (Miao et al., 2005). 2. The octanol water coefficient (log Kow) and pH-dependent octanol water distribution coefficient (log Dow): log Kow (some researchers use log Dow) is widely considered as the most important and simple parameter to evaluate and predict the sorption behavior of (hydrophobic) organic contaminants onto carbon-rich sorbents. Theoretically, the higher log Kow a given substance has, the larger the sorption affinity parameter (e.g., log Kd) onto a particular (bio)sorbent is. As illustrated in Fig. 15.6, there is a good correlation between the log Kd and log Kow values (the black open circle representing data). Even out of the range of the black open circle area, the same trend of log Kd values being larger for PPCPs with larger log Kow values is found.
The type of bioreactors, redox potentials, and coexisting substances (primary substance effects) are also important parameters that make a difference in the sorption behavior of PPCPs during wastewater treatment. Various studies have demonstrated that PPCPs removal, sorption, and biotransformation are highly dependent on the retention time, biomass concentration, bioreactor type, and the type of primary substance (Gartiser et al., 2007; Mohring et al., 2009; Conkle et al., 2012; Vasiliadou et al., 2013). Alvarino et al. (2014) found high variability of the relative amount of PPCPs sorbed onto sludge in different bioreactors under various aerobic conditions. For example, the constant sorption coefficient (Kd) for ibuprofen, fluoxetine, tonalide, and celestolide in conventional activated sludge (CAS) and upflow anaerobic sludge blanket were 240 and 100, 2500 and 700 L/kgvss, 6000 and 3000 L/kgvss, 3050 and 1200 L/kgvss, respectively. Alvarino et al. (2016) determined the sorption coefficient (Kd) of SMX under five different redox potentials and found significant differences among the Kd values of SMX. For example, the
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363
Kd values of SMX (L/kgtss) were 40, 32, and 7 under anaerobic degradation, autotrophic denitrification, and autotrophic nitrification conditions, respectively. Vasiliadou et al. (2013) demonstrated that treatment of coexisting substances led to increased sorption distribution coefficients for PPCP due to synergistic effects. Taking caffeine and SMX as examples under individual sorption conditions the Kd values were 60 and 0 L/kg, respectively, whereas under simultaneous conditions these values were 130 and 140 L/kg, respectively.
15.2
Transmembrane mechanisms of pharmaceutical and personal care products
The amount of material (target chemical molecules) crossing a surface (cell membrane) in a unit of time is known as flux. One of the first physiological demonstrations of the existence of membranes is the semipermeable property of cell envelopes (Al-Awqati, 1995). This means that not all substances can cross such membranes easily. Therefore chemical molecules, especially hydrophobic and pseudo-hydrophobic organic compounds, transport across the cell membrane (as a “barrier”) with different one-way flux through several mechanisms. These mechanisms mainly include diffusion through the lipid bilayer and various protein channels. Assuming that a given chemical contaminant has some degree of water solubility with a corresponding concentration in the wastewater phase or other aqueous environment, the obstacle to biological treatment would be the accessibility of the pollutants to the enzymatic machinery of the cell (which might be called the “real” bioavailability) (Bressleer and Gray, 2003). For organic pollutants, their transmembrane transport is the rate-limiting process during the biodegradation. The membrane-crossing flux depends on the target contaminant concentration outside the cell membrane (Vander et al., 2001). In addition, the type and structure of protein channels are also important for the transmembrane transport of HOCs and pseudo-HOCs.
15.2.1 Theoretical transmembrane flux The membrane-crossing flux can be simply written as (Bressleer and Gray, 2003): J 5 km CLout 2 CLin
(15.4)
where J is the flux of the components across the membrane; km is the permeability of the membrane, which has a positive correlation with the oil water partition coefficient (typically one-tenth of the octanol water distribution coefficient) and a negative correlation with the square root of the molecular weight of the target compound; and CLin and CLout are the intracellular and external concentrations of the target compound, respectively.
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Pharmaceuticals and Personal Care Products: Waste Management and Treatment Technology
Considering the active intracellular enzyme, the intracellular concentration of the target compound is usually significantly reduced to a relatively low level (CLin 0). The external concentration (CLout ) should be the compound concentration outside of the membrane and equal to water solubility. However, for these (pseudo) hydrophobic organic compounds, considerable amount of the compounds are accumulated (sorbed) on the surface of biosolids or microorganisms (see Section 15.1.1). Therefore the external concentration (CLout ) should be equal to the surface-sorbed concentration (Cad), which can be written as (Zhang, 2013): CLout 5 Cad 5 kKd Sw
(15.5)
where Kd is the sorption affinity coefficient, Sw is the water solubility, and k is an empirical parameter. The maximum membrane flux can be estimated through (Zhang, 2013): Jmax 5 0:002994 KowUKdUSwUMW0:5
(15.6)
where Kow is the octanol water distribution coefficient of the target compound, MW is the molecular weight of the target chemical, and 0.002994 is the empirical constant. If we do not consider the effect of sorption, the maximum membrane flux can be estimated as (Bressleer and Gray, 2003): Jmax 5 0:003 KowUSwUMW20:5
(15.7)
where 0.003 is the empirical constant. Bressleer and Gray (2003) provided a correlation between the maximum aerobic degradation rate and maximum membrane flux (calculated using Eq. 15.7) of 16 chemicals including aromatics, chlorine compounds, and oxygenated compounds. They suggested that a broad trend of maximum possible bioremediation rates could be correlated with an estimation of membrane flux. However, we evaluated the relationship between biodegradation rate and estimated maximum membrane flux (J) of 17 PPCPs (illustrated in Fig. 15.7) and found no significant correlation (R2 5 0.1169, n 5 50). The maximum membrane flux was calculated using Eq. 15.7 and the biodegradation rates were directly obtained from the literature as listed in Table 15.1. There are three possible reasons why the data did not fit the equation well: (1) the biodegradation rates of selected PPCPs were obtained by activated sludge in WWTPs, which might be different than those obtained from pure cultural biodegradation; (2) the selected PPCPs contained various negatively and/or positively charged polar groups; and (3) the sorption of PPCPs on the surface of cell membranes resulted in an increase of available concentrations of PPCPs being transported across the cell membranes. As we can see from Eq. 15.6, for a given contaminant in a certain wastewater treatment system, the transmembrane flux mainly depends on the sorption property
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365
Figure 15.7 Correlation between biodegradation rate (kb) and maximum membrane flux (J). The biodegradation rate was obtained from the literature. The maximum flux was estimated from Eq. (15.7) using the property data listed in Table 15.1.
of active biosolids (or biomass) which, subsequently, determines the efficiency of the biological degradation of the contaminant (Bressleer and Gray, 2003). We evaluated the relationship between sorption-modified transmembrane flux (J ) and sorption parameter (Kd), as illustrated in Fig. 15.8. A significant correlation of tested PPCPs (excluding galaxolide) was found and the results indicate that transmembrane flux partly depends on the sorption process. Furthermore, the relationship between biodegradation rate (kb) and sorption modified maximum membrane flux (J ) was evaluated, and is illustrated in Fig. 15.9. The correlation between kb and J (R2 5 0.66, n 5 50) is much better than that of kb and J. The positive correlation and the mechanism beyond the estimation of J (based on sorption-modified concentration) indicate that surface sorption plays a vital role in the transmembrane process and enzymatic degradation of PPCPs by biosolids. In addition, transmembrane flux is a good parameter to predict the intracellular biotransformation of PPCPs.
15.2.2 Transmembrane route There mainly are two transport routes for organic compounds across the cell membrane and into the cytoplasmic environment, namely diffusion through the lipid bilayer and protein channels. The cytoplasmic membrane lipids of bacteria cells play an essential role in the function of transmembrane processes (Green et al., 1980; Carrie`re and Legrimellec, 1986). Integral membrane proteins can span the lipid bilayer, some of which can form channels allowing ions as well as organic compounds to diffuse across the membrane (Vander et al., 2001). The property of
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Pharmaceuticals and Personal Care Products: Waste Management and Treatment Technology
Figure 15.8 Correlation between sorption modified maximum membrane flux (J ) with sorption constant (Kd). The sorption-modified maximum flux was estimated from Eq. (15.6) using the property data listed in Table 15.1.
Figure 15.9 Correlation between biodegradation rate (kb) and sorption-modified maximum membrane flux (J ). The biodegradation rate was obtained from the literature. The sorptionmodified maximum flux was estimated from Eq. (15.6) using the property data listed in Table 15.1.
organic contaminants (e.g., log Kow) and the type of protein are important factors that can effectively affect the transmembrane process. Diffusion through the lipid bilayer. The cell membrane is composed of a phospholipid bilayer. The amphiphilic property of phospholipids, which have a polar
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367
head group and two nonpolar “hydrophobic tails”, divides the lipid bilayer into three unique polarity domains. The polar heads orientate toward the outside of both sides of the membrane and form two fairly polar domains. The central region of the bilayer is nonpolar and hydrophobic in nature due to the fatty acid moieties of the phospholipids. Liposomes and lipid bilayers prepared by lecithin (phosphatidylcholine) are frequently used as experimental models to simulate biological membranes due to simple and good mimic of biological membranes. The lipid bilayer poses a significant barrier to the mass transport of certain organic contaminants. In general, nonpolar organic compounds with high octane water distribution coefficient (log Kow) readily partition into regular membrane lipid bilayers and easily enter into the internal matrix. In contrast, most polar organic molecules diffuse into cells very slowly, or not at all, although some of these contaminants could sorb on the membrane surface without entering the cell. For example, the released concentration of pyrene (log Kow 5 4.88) captured in liposome could eventually reach around 0.1 mg/L, although it is a time-consuming process (Zhang et al., 2013). Song et al. (2010) investigated the sorption and transmembrane transport of two antibacterial drugs (kanamycin and chloramphenicol) using lecithin liposome. Relatively strong sorption of both drugs onto the liposome was found; however, different mechanisms for the two drugs were used. The sorption of hydrophilic kanamycin (log Kow 5 26.70) occurred via electrostatic attraction, whereas the liposome sorbed the weak hydrophobic chloramphenicol (log Kow 5 1.14) through lipid water partition. More than 89% of the adsorbed kanamycin was located on the outer surface of the liposome, while over 80% of the adsorbed chloramphenicol entered the internal matrix. Diffusion through the transmembrane protein channels. Bacteria dominate the biological system during the removal and biodegradation of organic contaminants in wastewater. The outer membrane associated with the cytoplasmic membrane of Gram-negative bacteria provides an effective barrier for the passage of hydrophobic molecules due to the presence of the polar and hydrophilic lipopolysaccharide (LPS) layer on the outside of the bacterial cells (Hearn et al., 2009; Martı´nez et al., 2013). Therefore the transport of hydrophobic contaminants through the LPS layer is energetically unfavorable. Evidence shows that the removal of LPS from the cell surface is one of the important reasons for increasing the uptake of phenanthrene by a Citrobacter strain (Li and Zhu, 2012). Therefore hydrophobic organic contaminants cannot be easily transported from the outside of the cell into the internal matrix, even though the lipid bilayer provides ready passage for HOCs. Thus uptake of hydrophobic molecules must proceed through specific transporter pathways. Integral proteins have been recognized as offering a possible solution. There are two possible transport mechanisms that can be envisioned, and both are based on the diffusion process. a. “Classical” integral protein transport: The hatch undergoes conformational changes to create a transient channel, where hydrophobic molecules can transport transverse from the extracellular medium directly to the aqueous periplasm (van den Berg et al., 2004; Karuppiah et al., 2011).
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Pharmaceuticals and Personal Care Products: Waste Management and Treatment Technology
Figure 15.10 Schematic diagram of outer membrane protein with lateral opening. b. Alternative “lateral diffusion” transport (Hearn et al., 2009; Martı´nez et al., 2013). In the outer membrane protein with a lateral opening, a detergent molecule-bounded lateral opening can be trigged and an uninterrupted, hydrophobic passageway for lateral diffusion can be generated. Through the lateral opening, hydrophobic molecules can exit the barrel wall to move into the outer membrane from where they could diffuse into the periplasm. The lateral opening of these proteins is located in the region of the polar nonpolar interface of the outer leaflet of the outer membrane. The brief schematic diagram is provided in Fig. 15.10. To date, several outer membrane proteins containing lateral openings have been observed and characterized, including OprG (Touw et al., 2010), AlkL (Jusling et al., 2012), NapQ (Eaton, 1994), and FadL families (Hearn et al., 2009; Martı´nez et al., 2013).
15.3
Biotransformation of pharmaceutical and personal care products
15.3.1 Typical degrading strains, related enzymes and pathways of typical pharmaceutical and personal care products The use of PPCPs as a growth substrate (both sole carbon source and cosubstance) has been demonstrated for microorganisms (mainly bacteria and fungi) from wastewater and surface water. Microbes capable of growth on, and biodegradation of, certain PPCPs have been isolated and characterized. Examples of microbes capable of degrading four typical PPCPs (i.e., ibuprofen, SMX, CBZ, and triclosan) as well as related enzymes are summarized in Table 15.2. Three typical fungal enzymes— that is, manganese-peroxide (MnP), laccases, and lignin-peroxide (LiP)—are commonly studied. In sharp contrast, bacterial enzymes are barely mentioned.
Table 15.2 A Summary of strains and related enzymes capable of the biodegradation of four typical pharmaceutical and personal care products Compound
Strain
Enzyme
Condition
Reference
Ibuprofen
Phanerochaete chrysosporium Trametes versicolor Ganoderma lucidum Firmicutes sp. Aeromonas sp. Nitrospira sp. Betaproteobacteria Sphingomonas sp. Ibu-2 Patulibacter sp. I11 Trametes versicolor Ganoderma lucidum Rhodococcus rhodochrous
MnPa Lacb MnP
Fed batch stirred reactor Batch stirred reactor Batch stirred reactor Sludge Sludge Sludge Sludge Batch stirred reactor Batch stirred reactor Batch stirred reactor Batch stirred reactor Membrane bioreactor Batch stirred reactor Batch stirred reactor Batch stirred reactor Batch stirred reactor Sludge Sludge Sludge Sludge Membrane bioreactor Batch stirred reactor
Rodarte-Morales et al. (2012) Vasiliadou et al. (2016)
Sulfamethoxazole
Pseudomonas aeruginosa Acinetobacter sp. W1 Dyella sp. WW1 Firmicutes sp. Aeromonas sp. Nitrospira sp. Betaproteobacteria Microbacterium sp. BR1 Bacillus subtilis
ipfABDEF Lac MnP
Zhao et al. (2015)
Murdoch and Hay (2005, 2006) Almeida et al. (2013) Vasiliadou et al. (2016) Bouju et al. (2012) Gauthier et al. (2010) Bouju et al. (2012) Wang and Wang (2018a) Wang et al. (2018) Zhao et al. (2015)
Fenu et al. (2015) Larcher and Yargeau (2011) (Continued)
Table 15.2 (Continued) Compound
Strain
Triclosan
Pseudomonas putida TriRY Alcaligenes xylosoxidans TR1 Dyella sp. WW1 Rhodotorula mucilaginosa Penicillium sp. Aspergillus versicolor Nannochloris sp. Sphingopyxis sp. KCY1 Nitromonas europaea Burkholderia xenovorans LB400 Sphingomonas sp. YL-JM2C Sphingomonas sp. PH-07 Sphingomonas wittichii RW1 Pseudomonas sp. CBZ-4 Acinetobacter sp. HY-7 Trametes versicolor Ganoderma lucidum Starkeya sp. C11 Rizobium sp. C12 Dyella sp. WW1 Unidentified basidiomycete BNI Phodococcus rhodochrous
Carbamazepine
CBZ, Carbamazepine. a MnP: manganese-peroxidase. b Lac: laccase. c LiP: lignin-peroxidase.
Enzyme
Lac MnP
Lac, LiP,c MnP
Condition
Reference
Tryptic soy broth Tryptic soy broth Batch stirred reactor Original wastewater Original wastewater Batch stirred reactor Batch stirred reactor Batch stirred reactor Batch stirred reactor Batch stirred reactor Batch stirred reactor Batch stirred reactor Batch stirred reactor Low temperature (10 C) Batch stirred reactor Batch stirred reactor Batch stirred reactor Activated sludge Activated sludge Batch stirred reactor Batch stirred reactor Batch stirred reactor
Meade et al. (2001) Wang et al. (2018) Ta¸stan et al. (2016) Ta¸stan and Do¨nmez (2015) Bai and Acharya (2016) Lee et al. (2012) Roh et al. (2009) Kim et al. (2011) Mulla et al. (2015) Kim et al. (2011) Li et al. (2013) Cui et al. (2009) Vasiliadou et al. (2016) Bessa et al. (2017) Wang et al. (2018) Santosa et al. (2012) Gauthier et al. (2010)
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Murdoch and Hay (2005, 2006) identified a cluster of five putative genes (ipfABDEF) from a Sphingomonsa strain, which could be assigned to aromatic dioxygenases (ipfA and ipfB), enzymes for the addition or removal of acyl groups (ipfD), and coenzyme A ligases (ipfF). Microbiological transformation of PPCPs can result in complete mineralization to CO2 and H2O, or degradation to lower molecular weight mediates, or minor structural modification. These biotransformations happen through either catabolism (PPCPs act as a carbon and energy source) or cometabolism (coincidental transformation of target PPCPs with other compounds) (Kagle et al., 2009). Many studies have reported that PPCP metabolites (including lower molecular weight mediates and minor chemical modified products) were widely determined (Miao et al., 2005; Kosjek et al., 2007). These metabolites might still pose high risks to ecosystems and humans such as estrone (E1, transformed from 17β-estradio, E2) and nonylphenol (metabolite of nonionic surfactant). These results emphasize that the disappearance of the parent PPCP compound is not equivalent to the complete removal of PPCPs and studies on the detection of PPCPs in aqueous environments and metabolic pathways are critical to understand their fate in environment (Kagle et al., 2009). The metabolic pathways of PPCPs differ between microbial strains, related enzymes, and environmental conditions (e.g., pH, temperature). Next we briefly discuss three catabolic pathways of selected PPCPs (i.e., ibuprofen, triclosan, and SMX). 1. Ibuprofen. The biotransformation of ibuprofen usually occurs via the meta cleavage pathway as suggested by Murdoch and Hay (2005, 2006) using Sphingomonas sp. Ibu-2, and is illustrated in Fig. 15.11. The biodegradation of ibuprofen initiates the formation of ibuprofenCoA with the help of coenzyme A ligase encoded by ipfF. Two oxygen molecules are added to replace the propionic acid side chain catalyzed by the dioxygenase (encoded by ipfAB), forming 4-isobutylcatechol. This is followed by a meta cleavage to form 2-hydroxy-5-isobutylhexa-2,4-dienedioic acid. 2. Triclosan. The main degradation pathway of triclosan involves initial ether bond cleavage catalyzed by the chlorohydroquinone dehydrogenase to form 2,4-dichlorophenol (Lee et al., 2012; Mulla et al., 2015). Several intermediates (shown in the brackets in Fig. 15.12) are proposed to form compounds containing quinone bonds. 2,4-Dichlorophenol is further degraded through dechlorination to hydroquinone which can be easily mineralized to CO2 and H2O. 3. SMX. There are three regions where different enzymes attack to and, thus, results in six different degradation pathways, as illustrated in Fig. 15.13. These regions include an amino group linked to a benzene ring, heterocyclic ring, and S N bonds. The ammonia oxidizing bacteria (e.g., Rhodoccocus strain) initially attack the amino group via cometabolism and form desamino-SMX (Mu¨ller et al., 2013). Wang and Wang (2018a) proposed a degradation pathway initialized by simultaneously attacking the amino group and N O bonds in the heterocyclic ring, resulting in the metabolites of 4-hydroxybenzenesulfonic acid and 3-hydroxylamine-amino-5-carboxyl. Sulfate-reducing bacteria have the potential to cleave the heterocyclic ring through three pathways, namely isomerism, hydrogenation,
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Figure 15.11 Possible pathways of ibuprofen. Source: Modified based on Murdoch, R.W., Hay, A.G., 2005. Formation of catechols via removal acid side chains of ibuprofen and related aromatic acids. Appl. Environ. Microbiol. 71 (10), 6121 6125. and dioxygenation (Jia et al., 2017). SMX can also be attacked through breaking the S N bond to form 3-amino-5-methylisoxazole and 4-aminobenzenesulfonic acid through cometabolic pathways. This is followed by the metabolic degradation by bacteria such as Microbacterium sp. to form 4-aminophenol and further trihydroxybenzene (Jiang et al., 2014; Ricken et al., 2013).
15.3.2 Factors Once the PPCPs enter the wastewater treatment systems, they can be removed by several mechanisms including sorption and biotransformation. Sorption to biosolids plays an essential role in the removal and transmembrane process of PPCPs (Fig. 15.8), and transmembrane transport can be considered as a rate-limited parameter for their biotransformation (Fig. 15.9). Factors having an impact on sorption and transmembrane affect the biotransformation efficiency and degree. In addition, incubation conditions influencing microorganisms’ growth and activities are also important parameters for PPCPs’ biotransformation. 1. Chemical structure and properties. Functional groups including esters, nitriles, and aromatic alcohols usually increase the bioavailability of target compounds, whereas aromatic amines, iodide, nitro, and azo groups tend to render a compound more recalcitrant (Tunker et al., 2000). With similar initial concentrations (4.5 μg/L for triclosan and 4.2 μg/L for BPA), the specific degradation rates of the two PPCPs were 0.15 and 1.0 L/ mg/h, respectively, mainly owing to structure differences (Zhou et al., 2014).
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Figure 15.12 Possible pathways of triclosan. Source: Modified based on Lee, D.G., Zhao, F., Rezenom, Y.H., Russell, D.H., Chu, K.H., 2012. Biodegradation of triclosan by a wastewater microorganism. Water Res. 46 (13), 4226 4234; Mulla, S.I., Wang, H., Sun, Q., Hu, A., Yu, C.P., 2015. Characterization of triclosan metabolism in Sphingomonas sp. YL-JM2C. Sci. Rep. 6, 21965 21966.
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Figure 15.13 Possible pathways of SMX. SMX, Sulfamethoxazole. Source: Modified based on Mu¨ller et al. (2013); Jiang et al. (2014); Jia et al. (2017); Ricken et al. (2013); Wang, J.L., Wang, S.Z., 2018a. Microbial degradation of sulfamethoxazole in the environment. Appl. Microbiol. Biotechnol. 102 (8), 3573 3582; Wang, S.Z., Wang, J.L., 2018b. Biodegradation and metabolic pathway of sulfamethoxazole by a novel strain Acinetobacter sp. Appl. Microbiol. Biotechnol. 102, 425 432 (Wang and Wang, 2018b).
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2. Sorption behaviors. The sorption of PPCPs onto the cell surface facilitates the transmembrane process and subsequently increases the substance concentration for intercellular enzymatic degradation. For example, the sorption affinity constant (Kd), sorptionmodified transmembrane flux (J ), and biodegradation rate (kb) of SMX under a continuous membrane bioreactor and CAS were 8.6 and 80 L/kg, 7.66 and 71.3 mg/cm2/s, and 0.3 and 9 L/g/day, respectively (Fernandez-Fontaina et al., 2013; Alvarino et al., 2014). Similar tends were found for the treatment of tonalide, of which the parameters were 2714 and 6000 L/kg for Kd, 316,745 and 700,246 mg/cm2/s for J , and 3.87 and 38 L/g/ day for kb, respectively (Fernandez-Fontaina et al., 2013; Alvarino et al., 2014). 3. Microbial community structure or degrading strains. The mineralization of 0.5 mM 14Clabeled SMX could reach around 60% by enrichment cultures, whereas the Ralstonia microcosm (Ralstonia sp. HB1 and Ralstonia sp. HB2) could mineralize only 40% of SMX under the same conditions (Bouju et al., 2012). Different strains might secrete different dominant enzymes which could lead to different biotransformation results. For example, two white-rot fungi had different dominant enzyme systems (laccase for Trametes versicolor, and manganese-peroxidase for Ganoderma lucidum), and demonstrated different biodegradation ability of PPCPs (Vasiliadou et al., 2016). 4. Bioreactor conditions (substance concentration, temperature, and retention time). Initial concentration is another factor that can influence the biotransformation of PPCPs in WWTPs and other aqueous environments. In general, the biotransformation rate of PPCPs usually presents as a fast degradation process followed by a slow degradation process (Wang and Wang, 2018a). At low concentrations, the degradation rates of PPCPs increase with increasing substrate concentration. For example, the specific removal rates of CBZ were about 0.0013 and 0.0063 hour21 by Pseudomonas sp. CBZ-4 at the initial concentrations of 10 and 60 mg/L, respectively (Li et al., 2013). Since the detected concentration in wastewater and aqueous environments ranges from ng/L to mg/L (Wang and Wang, 2016), the bioavailability of PPCPs in the aqueous phase might be low. Therefore the degrading strains for PPCPs should be capable of utilizing PPCPs at very low concentrations.
Coexisting substances (usually an easily degradable carbon source, e.g., glucose, acetate) can accelerate the biodegradation process. For example, the biodegradation rate was much higher for diclofenac degradation with periodic feeding of carbon acetate (5.9 mM) (0.078 day21) than those with an addition of carbon acetate (5.9 mM) only at the beginning (0.028 day21) or those without any carbon source addition (0.017 day21) (Bessa et al., 2017). Many studies have noted improved biological transformation of certain PPCPs with increased retention time, both HRT and solids retention time (SRT). Sua´rez et al. (2008) summarized that shorter HRT reduced the elimination of ibuprofen and ketoprofen, and higher removal of most PPCPs was observed for longer SRT. A much greater increase in the removal of trimethoprim and macrolides (two to three times) was observed when SRT increased from 16 33 to 60 80 days in a membrane bioreactor (Go¨bel et al., 2007). Another factor affecting the biotransformation of PPCPs is temperature due to the influence of the enzyme activities of microorganisms. The optimum temperature of most organisms is above 20 C. However, in some cases, cold-adapted bacteria preferring low temperature are also reported. For example, Li et al. (2013) reported a cold-adapted CBZ-degrading bacterium (Pseudomonas sp. CBZ-4), for which the
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optimum temperature was 10 C. The average removal efficiency of CBZ could reach 26% at 10 C, whereas the removal efficiency dropped to only 9% when the temperature increased to 37 C.
15.4
Conclusion
The sorption, transmembrane process, and intercellular enzymatic degradation of PPCPs are successive processes, with each providing more available substance for the subsequent process. The sorption of PPCPs onto cell surface or biosolids surface could facilitate the transmembrane transport to the intracellular cytoplasm, providing sufficient substance for the intercellular catalytic enzymes. For a given contaminant in a certain wastewater treatment system, the transmembrane flux mainly depends on the sorption property of active biosolids (or biomass) and subsequently determines the efficiency of the biological degradation of the contaminant. However, less is known on the sorption and transmembrane process of PPCPs during wastewater treatment. Therefore further research is urgently needed to gain a better understanding of the behavior, removal efficiency, and fate of PPCPs after disposal into wastewater.
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Further reading Aronson, D., Citra, M., Shuler, K., Printup, H., Howard, P.H., 1999. Aerobic Biodegradation of Organic Chemicals in Environmental Media: A Summary of Field and Laboratory Studies. EPA Reports, Office of Research and Development Athens GA, 30605, New York.