Next Article in Journal
Effects of Long-Term Increases in Water Temperature and Stratification on Large Artificial Water-Source Lakes in South Korea
Next Article in Special Issue
Advanced Oxidation Processes Based on Sulfate Radicals for Wastewater Treatment: Research Trends
Previous Article in Journal
Changes in Algal Plankton Composition and Physico-Chemical Variables in a Shallow Oxbow Lake
Previous Article in Special Issue
Degradation of Diclofenac in Urine by Electro-Permanganate Process Driven by Microbial Fuel Cells
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Emerging Contaminants: An Overview of Recent Trends for Their Treatment and Management Using Light-Driven Processes

Department of Civil and Environmental Engineering, Faculty of Engineering, National University of Singapore, Block E1A, #07-01, 1 Engineering Drive 2, Singapore 117576, Singapore
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Water 2021, 13(17), 2340; https://doi.org/10.3390/w13172340
Submission received: 2 August 2021 / Revised: 20 August 2021 / Accepted: 21 August 2021 / Published: 26 August 2021
(This article belongs to the Special Issue AOP Processes for Organics Removal in Water and Wastewater)

Abstract

:
The management of contaminants of emerging concern (CECs) in water bodies is particularly challenging due to the difficulty in detection and their recalcitrant degradation by conventional means. In this review, CECs are characterized to give insights into the potential degradation performance of similar compounds. A two-pronged approach was then proposed for the overall management of CECs. Light-driven oxidation processes, namely photo/Fenton, photocatalysis, photolysis, UV/Ozone were discussed. Advances to overcome current limitations in these light-driven processes were proposed, focusing on recent trends and innovations. Light-based detection methodology was also discussed for the management of CECs. Lastly, a cost–benefit analysis on various light-based processes was conducted to access the suitability for CECs degradation. It was found that the UV/Ozone process might not be suitable due to the complication with pH adjustments and limited light wavelength. It was found that EEO values were in this sequence: UV only > UV/combination > photocatalyst > UV/O3 > UV/Fenton > solar/Fenton. The solar/Fenton process has the least computed EEO < 5 kWh m−3 and great potential for further development. Newer innovations such as solar/catalyst can also be explored with potentially lower EEO values.

1. Introduction

Due to growing concern about the potential harm to the aquatic ecosystem and human health, a growing emphasis has been placed on the research of cost-effective treatment technology for contaminants of emerging concern (CECs). CECs are currently unregulated in most countries, with their treatment options not well documented [1]. The global prevalence of CECs in surface waters, groundwaters, and municipal wastewaters has resulted in a tightening of standards for emerging contaminants [2]. Despite guidelines set by local authorities, the occurrence of unregulated discharge often occurs due to a lack of strong legislation and ecotoxicity data on CECs [3]. CECs are resilient to the conventional biological treatment processes or have slow kinetics for biodegradation [4]. Uncontrolled release of CECs into water environments causes problems like environmental persistence, ecotoxicity, and potential harm to human health. Current detection methods of CECs also suffer from few limitations, such as prohibitive cost, time-consuming detection means, and low throughput. Rapid industry changes also make the treatment and regulation of CECs particularly challenging. For instance, Ahearn et al. [5] found that the replaced substance (GenX), for the recently regulated perfluorooctanoic acid (PFOA), had a more detrimental environmental impact. Hence, there is a pressing need for the treatment and management of CECs to keep up with global trends.
Light-driven advanced oxidation processes (AOPs) are a viable solution for the overall management of CECs, due to their high efficiency in degrading a wide spectrum of organics. The drastic decrease in the cost of UV-light emitting diode (LED) with a single peak emission wavelength has also allowed for more precise and cheaper UV-based detection of compounds [6]. Furthermore, the occurrence of CECs in secondary effluents with characteristically low turbidity and high UV transmittance >35–65% [7], makes light-based treatment particularly attractive.
Despite the multiple advantages of light-based systems, there is a lack of review for the light-based treatment and management of CECs. Hence, this review aims to provide an updated overview of the occurrence, treatment and management of CECs, using light-based technology. This review discusses a two-pronged approach for the treatment and management of CECs, highlighting recent applications within the last 3 years. Lastly, a cost–benefit analysis is done on the various light-based technology for greater insights into their potential development. Future directions for the light-driven process have also been suggested.

1.1. Global Occurrence of Contaminants of Emerging Concern (CECs) and Their Categorization

The global occurrence of CECs in developed and developing countries shows a pressing need to address this class of contaminants. A compilation of global occurrence of CECs detected in the environment, and their use and chemical structure is presented in Table 1.
As seen from Table 1, the global prevalence of CECs in water bodies calls for better management strategies and treatment technology. Management of CECs could be done by first categorizing CECs. Categorization of CECs based on the usage and chemical structure would provide insights into the fates and potential degradation performance of newer CECs. Concerning treatment by light-driven processes, CECs should be best categorized based on their chemical structures. Polarity and hydrophobicity (Kow) of CECs are dependent on their molecular structures and hence CECs with similar chemical structures, generally exhibit similar chemical characteristics, removal mechanisms, spectral properties and toxicity [40]. CECs with similar structures were reported to have similar photodegradation performance an illustrated example is shown in the Supplementary S1. Structures such as halogens present in CECs result in a resonant stabilized structure [41]. Whereas chemical structures like electron-donating hydroxyl (–OH) and amino (–NH2) functional groups affected the resonance stability of ring structures, resulting in different degradation performances [42]. Chemical stability and optical properties are directly correlated to CECs degradation performance by light-based processes.

1.2. Conventional Detection of CECs in Water Bodies

Conventionally, CECs are concentrated via various extraction methods to improve their detection accuracy, such as liquid–liquid extraction (LLE), solid-phase extraction (SPE), solid-phase microextraction (SPME), stir bar sorptive extraction (SBSE), matrix solid-phase dispersion (MSPD), pressurized liquid extraction (PLE), ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), and liquid-phase microextraction [39,43]. A conventional mode of detection of CECs is usually performed using liquid chromatograph (LC) and gas chromatograph (GC) methods [44,45]. The key difference between the two methods is the mobile phase used. GC uses an inert gas (like helium) for its mobile phase, while LC uses a polar solvent (like water or methanol) for its mobile phase.
GC requires the volatilization of CECs and is detected via various means. GC coupled with electron capture detection (ECD) has been widely applied to analyze chlorinated-DBPs from chlorina (mina) ted water [46]. ECD is highly sensitive to electronegative compounds, such as compounds containing halogens, nitrogen, and sulfur. Andersson et al. [47] demonstrated GC equipped with a halogen-specific detector (XSD) for the simultaneous determination of traditional and emerging halogenated-DBPs. XSD shows high selectivity towards halogenated compounds only. It is noted that the methods proposed above are highly specific to their structure type and hence the classification of CECs (in particularly newer CECs) based on their chemical structures may be an attractive means to assess the suitability of present detection methods. GC can also be coupled with a quadrupole time-of-flight (QTOF)-MS for the detection of micropollutants [44]. More conventionally, GC is coupled with a mass spectrophotometer (MS) for the detection of CECs.
However, GC methods might not be suitable for compounds that are susceptible to thermal decomposition during acquisitions [45]. Liquid chromatograph (LC) uses the principle of mass transfer in a polar liquid and does not require sample volatilization, hence preventing compound degradation and the formation of new products under high heat conditions [45]. Liquid chromatograph (LC) coupled with triple quadrupole (QqQ) or quadrupole linear ion trap (QqLIT) MS/MS is one of the most widely used for CECs quantification [14], whereas LC-(QTOF)-MS uses the principle of molecular weight and has been used to study transformation products and the reaction pathway of PPCPs and the formation of DBPs [48]. Detection and characterization of DBPs in complex matrices can be performed by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) [48].
It is important to note that the use of the various methods is highly specific to the physico-chemical property of the CECs, with some CECs more suitable for a certain type of detection method [44]. The definition of CECs by their similar chemical structure mentioned in earlier segments is hence an attractive means not only to categorize the CECs but also to know its suitable detection methods. However, conventional methods of testing are labor-intensive and have limited application for online detection due to the expensive equipment needed [49].

2. Overview of Light-Driven Processes

Due to the global occurrence of CECs in various waters and the difficulty in detection of these compounds, there is a pressing need for the overall treatment and detection of CECs. Herein, a two-pronged approach is proposed for the treatment and management of CECs (Figure 1). Light-based treatment can be categorized into UV/oxidant, UV/ozone, photo-Fenton, and photocatalysis, with each having a range of UV wavelength and factors affecting its operation, which is discussed below, whereas the detection methods discussed involve rapid and alternative methods for the detection of CECs in wastewater.

2.1. Mechanism of Light-Driven Processes

Light-driven processes involve two main reactions: (1) direct photolysis and (2) generation of highly reactive oxidative substrates, such as hydroxyl (•OH), chlorine (•Cl), sulfate (SO4), and hydroperoxyl radicals (HO2•), by catalytically converting water or oxidants for the degradation of wastewater. Direct photolysis occurs when the light energy used (Eλ) is more than the associated bond energy of the contaminants [50]. The energy supplied by the light processes could be approximated to a specific wavelength, as shown in Supplementary S2, whereas radicals react directly with CECs and degrade them. The generation of reactive species is dependent on the process and will be further elaborated below. The key reaction mechanisms and graphical illustration of the mechanisms are summarised in Table 2 and Figure 2.
For UV/oxidant processes, UV light directly interacts with added reagents to produce highly reactive radicals [59,60]. The wavelength of light required is between 200–300 nm. For the UV/Ozonation process, the UV light interacts with ozone to form hydrogen. The produced hydrogen peroxide then further reacts with UV to form hydroxyl radicals [54]. In the homogenous photo-Fenton process, the Fe2+ in the aqueous form reacts directly with hydrogen peroxide to form hydroxyl radicals and Fe3+. Ferrous ions (Fe2+) are then photochemically regenerated by the photo-reduction of ferric ions (Fe3+) [55]. The wavelength of light required is reported to be between 190–900 nm. In heterogenous photo-Fenton, iron can also partake in dissolution to form aqueous forms of iron species, which would subsequently follow a similar reaction mechanism of Ferrous ions in homogenous photo-Fenton [54,55]. This step is further illustrated in Figure 2c. It should also be noted that the mechanism for heterogenous photo-Fenton is largely dependant on the pH operation and the iron species used. The wavelength of light required is reported to be between 180–400 nm, whereas in photocatalysis, light irradiation of catalyst produces electron–hole pairs within the conduction and valence bands. The subsequent excitation and recombination of the energy state of the catalyst form reactive radicals [61]. This illustration is further displayed in Figure 2d. The wavelength of light required is reported to be between 254–400 nm.

2.2. Critical Factors That Affect Light-Driven Processes

Generally, light-driven processes are affected by UV absorbance, pH, water matrix, reagent dosages, and UV sources (Table 3). The organic and inorganic composition of water affects the performance of light drive processes either by inhibition of chemical processes or inhibition of light penetration. The presence of turbid water or NOM reduces the photocatalytic effect due to charge carrier generation and UV absorbance [60,62]. The pH affects the process due to the inhibition effect from the stability of the oxidant or catalyst [63,64]. Too low a pH would affect the stability of metal-based catalyst in Fenton-based and photocatalyst systems [63,64,65]. Oxidant dosage and catalyst dosage affect the performance of CEC degradation since they affect the generation of oxidative species needed for CEC degradation. In excess, it might have scavenging effects or even change the physical characteristic of the water (i.e., light penetration), which affects the overall CEC degradation performance [66,67,68]. Lastly, the irradiation source also affects the CEC degradation performance of light-based systems. Generally, light waves with shorter wavelengths carry more energy and hence better performance [66,67,68]. However, a shorter wavelength of light is more energy-intensive and ongoing research is being done to have systems that use a longer wavelength of light or even natural sources of light for irradiation.

2.3. UV/Oxidant

Due to the ease of operations, UV/oxidant processes have been widely applied in synthetic and spiked wastewater effluent (Table 4). UV/oxidant processes use a variety of different oxidative species (chlorine, peroxide, nitrates, sulfate, and its derivatives) for the generation of oxidative radicals. Generally, the reaction is operated in the neutral pH, with a hydraulic retention time (HRT) between 20–180 min and a vast majority of the systems use high-energy UVC irradiation systems to activate the oxidants. Most of the investigations using UV/Oxidant processes have focused on the removal of target compounds spiked in synthetic matrices at laboratory and pilot scales with a good degree of degradation of between 80–90% of the CECs.
It was also found that generally, UV/H2O2 processes were less effective in the degradation of CECs as compared to other oxidative species [67,87,88]. Certain CECs are also more resilient to UV/H2O2 processes. Perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), and bisphenol A (BPA) were found to have low degradation performance of <30%, <30%, and <50% respectively. This is likely due to low values of H2O2 molar absorption coefficient at 254 nm (19.0 M−1 cm−1) [100], resulting in higher hydrogen peroxide and UV dose for efficient removal.
Many photooxidant processes use persulfate and its derivatives and these were found to be generally greater in performance as compared to UV/H2O2 processes. For instance, removals of caffeine (60%) and carbamazepine (70%) through UV/S2O32− were significantly better than in the UV/H2O2 [89]. Low quantum yield and photolysis coefficients could be due to the presence of imidazole groups in both caffeine and carbamazepine. However, it was found that persulfate tended to be slightly acidic when used as an oxidant; hence, this might not be suitable for the removal of CECs from treated effluent. Furthermore, radicals generated by persulfate had a lower charge as shown in the Supplementary S3. There are also limited studies on the use of UV/chlorine processes, hence presenting an opportunity for more studies on the use of this aspect, since both UV and chlorine (Cl2) are commonly used as disinfectants in water/wastewater treatment processes, although some studies suggest that disinfection by-products may potentially be formed by these processes [86,87].
Another area that is presently being studied is the use of pre-existing inorganics such as nitrate in wastewater for the promotion of radical species [97,98]. Rizzo [98] found that nitrate as an oxidant could remove CECs with moderate capacity even with the use of sunlight as an irradiation source. Carbamazepine (CBZ), diclofenac (DCF), and sulfamethoxazole (SMX) were removed from wastewater effluent with 70%, >90%, and 100% efficiency with UVC irradiation, respectively, whereas another study by Zhou et al. [99] showed that nitrates could reduce bisphenol A (BPA) and carbamazepine (CBZ) at an efficiency of ~80% and ~60%, respectively. These results were consistent with the finding from Li et al. [101]. They noted that the presence of NO3 promoted the photochemical loss of oxytetracycline (up to 82.9%) in an aqueous solution. The photolysis of NO3 leads to the formation of HO• which further promotes the indirect photodegradation of oxytetracycline.
At present, the effects of the inorganics such as nitrates and sulfates in wastewater are still not widely understood and the degradation performance could be studied further for a larger variety of CECs. These oxidants have fewer risks of synergistic impact of the blending of by-products in the effluent stream.

2.4. UV/Ozone

The majority of UV/O3 processes as summarized in Table 5 were operated at slightly alkaline pH (≥9), with low-pressure mercury lamps, bench-scale reactors, and an HRT of 20–180 min. UV/O3 showed better removal performance as compared to purely ozonation process with the same treatment condition. Jing et al. [102] reported a better removal of COD and NH3-N in the treatment of atrazine production wastewater (from 2% to 21% respectively in O3 to 55% and 65% respectively in UV/O3). Another study by Xu et al. [103] showed that UV/O3 had better degradation of synthetic wastewater containing sucralose as compared to UV and ozonation alone. Total organic carbon (TOC) removal of 89.8% for UV/O3 as compared to UV and O3 at <5% and 39.1% removal, respectively, was reported. This could be due to a large number of hydroxyl radicals that can be generated in a fast manner which is adequate for the mineralization of CECs. However, it was noted that the combination of ozone and UV process did not improve the degradation of gasoline compounds (benzene, toluene, and isomers of xylene) in comparison with ozone [104].
Despite the benefit of the enhanced degradation rate, there has been less focus on the study of the UV/O3 process, due to the higher cost of treatment. Ozonation and UV processes are widely known to be energy-intensive and as such the upscaling UV/O3 might not be viable. Furthermore, ozonation requires pH restriction to perform optimally [110], which might not be suitable for CEC treatment. CECs are concentrated mostly in the effluent stream of wastewater treatment facilities which generally have a neutral pH. As such, pH adjustment needs to be done before and after the UV/O3 process for effluent discharge, increasing treatment costs.

2.5. Photo-Fenton

Photo-Fenton processes have been used in a wide variety of CECs degradation of various scales and modes of operations, as seen in Table 6. The majority of the photo Fenton processes uses lower energy UV wavelengths and even solar-powered systems. Degradation time also varies from 10–180 min, depending on the compounds being degraded. Most of the investigations using photo-Fenton processes focus on the removal of target compounds spiked in synthetic matrices at laboratory scale and pilot scales with a good degree of degradation of between 80–90% of CECs.
Notable publications describing the effects of chemical structures on the efficiency of photo/Fenton-based systems are summarized above. Recent innovation with magnetic carbon-based heterogenous composites addresses the reusability and separation of conventional heterogenous catalysts. Alani et al. [115] reported that the photocatalytic performance of the magnetic catalyst remains relatively high even after 5 consecutive uses. Wu et al. [127] investigated the photo-Fenton degradation for methylene blue, methyl orange, rhodamine B, 2,4-chlorophenol, and bisphenol A with carbon quantum dots on the iron-based catalyst α-FeOOH. Methyl orange showed the lowest degradation (~50%) after 20 min of reaction. This could be due to the electrostatic repulsive force between methyl orange and the catalyst which limiting the adsorption. BPA and 2,4-chlorophenol showed good degradation (~100%) due to the π–π interaction between these compounds and carbon quantum dots. Different CECs may have different removal mechanisms during the photo-Fenton process. For instance, Guerra et al. [70] proposed degradation pathways for paracetamol and amoxicillin in the photo-Fenton solar process. With different functional groups such as amino, carbonyl, and hydroxyl groups, paracetamol may undergo oxidative attacks: aromatic hydroxylation, allylic and benzylic oxidations, amine dealkylation, and amine oxidation. Amoxicillin has more functional groups in its chemical structure compared to paracetamol. The degradation of amoxicillin would form amoxilloic acid resulting from the hydroxylation of the aromatic ring and the opening of the four-membered β-lactam ring. Conventional Fenton processes require strict pH control at acidic ranges which might not be a suitable process for downstream wastewater effluent, since the pH of the effluent is usually neutral [117,118,120,121]. Inhibition of the Fenton process due to the solubility of the iron species can be circumvented with the use of ligands where a large number of recent studies focus on the use of ligands like EDDS to operate the reactors at a neutral pH. Generally, the operation pH is at pH 7, with some studies running it at pH 3 [111,116]. More research is on homogenous Fenton, which has a higher reaction kinetics as compared to heterogeneous Fenton. Heterogenous photo Fenton also has issues with the retention of catalysts. Wang et al. reported that the nZVI catalyst lost 15% of its initial amount after 5 consecutive runs [114], due to the dissolution of iron into the solution.
The recent innovation of the use of solar-assisted Fenton, raceway pond reactors, shows promising results for the degradation of CECs [113,117,119,121,122]. This reactor configuration requires less energy input due to the use of solar energy and also shows a high degree of degradation. The height of the race pond bed seems to contribute significantly to the degradation performance of CECs. More recently, alternative and cheaper ligands such as NTA are also being test bedded against conventional EDDS ligands [123]. At present, the study of this reactor is still in its infancy stage and hence has the potential to be developed further.

2.6. Photocatalysis

Photocatalysis uses solar energy/UV lamps with a catalyst and has been used in a wide variety of CECs degradation of various scales and modes of operations. The majority of the photocatalysis processes utilize both lower energy UV wavelengths and even solar energy. Degradation time also varies from 10–180 min, depending on the compounds being degraded. Most of the investigations using photocatalysis processes have been focused on the removal of target compounds spiked in synthetic matrices at a laboratory scale with a good degree of degradation of between 80–90% of CECs. Various types of photocatalysts such as titanium dioxide (TiO2), zinc oxide (ZnO), tungsten trioxide (WO3), and graphitic carbon nitrides (g-C3N4) have been explored and are summarized in Table 7.
The degradation rate of CECs by photocatalysis is found to be closely related to their molecular structures. Eskandarian [68] found that decomposition kinetics of CECs by TiO2 photocatalytic followed the order: sulfamethoxazole > diclofenac > ibuprofen > acetaminophen. Sulfamethoxazole is highly reactive due to the NH group in its chemical structure. Ibuprofen could be decomposed via rearrangement of the acidic group, followed by decarboxylation reaction and dehydrogenation. However, it is less flexible in degradation sites due to its molecular structure. Degradation of acetaminophen is the most difficult of the four compounds. The mechanism involved the removal of the amide group (CH3CONH), formation of phenoxy radical that will react with superoxide radical. In another study, Alverez–Corena et al. [144] found the decreasing trend of UV/TiO2 degradation kinetics for 5 CECs: Gemfibrozil > 17β estradiol > N-nitrosodimethylamine (NDMA) > 1,4-dioxane > tris-2-chloroethyl phosphate (TCEP). The high degradation for gemfibrozil could be attributed to the presence of a deprotonated carboxyl group in its structure which can enhance its adsorption capacity on the photocatalyst surface. N-NO bond in NDMA could act as an electron donor to the TiO2 surfaces. C-O bonds in 1,4-dioxane could be served as hydrogen bond acceptors for dipolar attractions. TCEP is without ionizable functional groups in its structure. In addition, a high pKa of 14.86 of its leaving group 2-chloroethanol and higher dipole moment makes it difficult to be removed. Hence, TCEP showed the slowest degradation rate.
Conventionally, UVC wavelengths are used in photocatalysis especially for TiO2 based catalysts [140]. However, in recent years there has been a greater push for other lower energy-intensive wavelengths, such as visible light and solar power for the degradation of CECs. Morphological modification of TiO2 based catalyst has allowed for visible light to be used as an irradiation source [57]. An alternative catalyst such as zinc-based catalyst is also explored that is also able to use sunlight as an irradiation source [59,116]. However, the degradation performance is not as comparable to TiO2 based catalysts. A potential attractive perspective is the use of bimetallic catalyst which combines the efficiency of TiO2 based catalyst and the solar capabilities of zinc-based catalyst.
In most applications of photocatalysts, nano-catalysts are used due to their high surface-to-volume ratio. However, the recovery of this nano-catalyst after usage has been an ongoing issue bottlenecking the large-scale application of these processes [61,135,139]. To overcome this problem, recent researchers have incorporated magnetic elements such as magnetite so that resultant photocatalyst can be easily retrieved from the treated effluent [57,61], but this comes at the expense of adsorptive capacity as reported by Juang et al. [145], wherein the adsorption capacity of AC is reduced by 15% after doping Fe3O4 to achieve the magnetic capability. Another issue with the formed catalyst is the reusability of the catalyst. Kumar et al. [140] reported that modified TiO2 has a good separation of formed catalysts and high removal of ibuprofen even when reused 3 times. This is similarly reported for the heterogenous photo-Fenton process. However, the current study on the long-term usage of such catalysts is still in its infancy stages and presents the potential for future development. The coupling of solar and magnetite into conventional photocatalytic processes has scaled-up potential and presents an opportunity for future development.
Newer materials such as WO3 and carbon-based photocatalyst have also shown promising results in the degradation of CECs. However, these catalysts were mostly demonstrated for the use of degradation of a singular compound in synthetic wastewater. The cost consideration for the use of these catalysts was also not well evaluated in the literature, due to its novelty. Further research can be done to evaluate its suitability for CECs degradation in real wastewater.

2.7. Light-Driven Detection of CEC in Treatment Systems

A cheaper alternative for the detection of CECs is through the use of light-based spectroscopy. Light-driven detection techniques have been widely proven to be useful in characterizing natural organic matter (NOM) in natural water, drinking water, and wastewater. Light-driven detection methods could be broadly divided into 3 segments that have different mechanisms (Table 8). Adsorption of light measures the ‘missing’ wavelength of light that is shone on the water sample [146]. Light-driven detection is based on the absorption of light by organic compounds which results in the excitation of the electrons from the ground state to a higher energy state. The energy difference of each ground state and excitation state pair corresponds to an absorption band. Compounds that contain aromatic rings and double bonds can absorb energy in the form of ultraviolet light to excite the electrons to higher anti-bonding molecular orbitals. UV absorbance, especially at 254 nm, is one of the most widely used surrogate parameters to quantify NOM reactivity. Chon et al. demonstrated a piecewise linear correlation between the differential of UV254 and the elimination of 17α-ethynylestradiol (EE2), carbamazepine, atenolol, bezafibrate, ibuprofen, and p-chlorobenzoic acid [147]. On the other hand, Pisarenko et al. observed significant correlations between reduction in UV254 and removal of CEC by ozonation including tris-2-chloroethyl phosphate, meprobamate, primidone, meta-N, N-diethyl toluamide, phenytoin, trimethoprim, sulfamethoxazole, and carbamazepine with R2 ranged from 0.778 to 1.000 [148]. These CECs are electron-rich compounds, e.g., contain aromatic rings or unsaturated carbon bonds (double or triple) in their molecular structure. Previous studies also found that •OH radicals tend to react with large molecules with various reaction sites, aromatic compounds, electron-rich organic moieties [149]. Hence, UV254 or UV280 nm could be used as indicators to predict the removal of CECs from UV/H2O2. Newer forms of chemiluminescence use enzyme-linked immunosorbent assays (ELISA) and time-resolved fluoroimmunoassays to detect CECs [150] with a high degree of accuracy, whereas for the excitation and emission type, a known light source is shone on the sample and the measured signals would be the excitation wavelength vs. emission wavelength vs. fluorescence intensity [151]. The EEM could be used to discriminate different groups of NOM based on the difference in light emission and excitation of fluorophores. NOMs with certain molecular structures are reported to have fluorescent properties in a wide range of excitation/emission wavelengths [152]. Hence, samples with different NOM have distinct features with maxima located at characteristic combinations of excitation and emission wavelengths. These EEM features could be used to predict the removal of CECs. Lastly, the physicochemical signal detection relies on the physio-chemical reaction to light onto the sample and is highly dependent on the compounds being monitored [153]. Infrared spectroscopy was also explored to detect a low concentration of CECs. Quintelas et al. demonstrated the use of Fourier transform near-infrared (FT-NIR) spectroscopy for the detection of ibuprofen, sulfamethoxazole, 17β-estradiol, and carbamazepine, with a high degree of accuracy with R2~0.95 [154], although it was noted that other compounds tested were not as accurate. The analytical signal of this sensor is the refractive index change which was recorded along with concentration change of PFOA from 0 to 4 ppb. Recently, various core-shell nanostructures have been widely developed to enhance the surface-enhanced Raman scattering (SERS) technique for the detection of pesticides [155]. Fang et al. [156] developed an aptamer-based conformation cooperated enzyme-assisted SERS technology to detect CEC. LOD of 4.8 pg/L n aqueous solution was achieved for chloramphenicol. Newer methodology such as surface plasmon resonance (SPR) was also demonstrated to have the potential for CEC detection. Cennamo et al. demonstrated the potential for SPC detection of perfluorinated alkylated substances (PFAs) with an impressive detection limit of 0.13 ppb [157,158].
At present, there is limited usage of these methodologies due to their inaccuracy and selectivity of methods used. The main challenge for this technology depends on the correlation between the CECs and their physical and chemical properties. This correlation is site- and treatment-specific and needs to be calibrated and verified, while considering inner-filtering effects. Future development coupled with machine learning might potentially produce higher accuracy for light-driven detection strategies. Artificial neural network (ANN) modeling, which has been used to model and calibrates complex systems [164,165], shows potential to be coupled with UV-detection strategies. More studies could be done to use such calibration means to enhance current detection methods. To address the selectivity of detection means, categorizing of CECs according to their physical–chemical structures would give precedence to detection methods for newer CECs and also help with the screening of type of detection methods used.

3. Cost–Benefit Analysis of Light-Driven AOPs on the Treatment of CECs

Simulated treatment costs of various light-driven processes are studied and evaluated using a matrix EEO value developed by Bolton et al. [166] (Figure 3). Optimized conditions from the articles reviewed would be set as the operation condition for the computation of the EEO values. Detailed computation of the EEO values can be found in the Supplementary S4.
It can be seen that the average EEO values are in this sequence: UV only > UV/combination > photocatalyst > UV/O3 > UV/Fenton > solar/Fenton. The average EEO of the systems computed is 98.8, 5.11, 20.92, 37.18, 16.67, 2.62, and 76.80 kWh m−3 for UV only, UV/oxidant, UV/O3, photocatalyst, UV/Fenton, solar/Fenton, and UV/combination, respectively. UV and UV/oxidant strength and selectivity of oxidants result in certain CECs having better degradation than others [153], hence resulting in a varied computation. It was also noted that for selected studies relatively low initial concentration (<0.01 mg/L) of CECs used in the study for UV/oxidant, compared to other processes also likely contributed to the low EEO values. Whereas the UV/O3 process requires both an energy-intensive UV lamp and ozone generator, it could have resulted in high operational costs. However, since it can achieve high degradation efficiencies of contaminants [167], the average EEO values are not as high. For photocatalysis, articles presented in this study were lab-scaled reactors, resulting in a wide range of EEO values, ranging from 0.000038 kWh m−3 for a simple treatment of 1 mg/L of tris-(2-chloroisopropyl) phosphate to treating pesticides-containing wastewater with a COD of 1130 mg/L at 233 kWh m−3. A similar AOP treatment setup might yield different EEO values based on the degradation performance of the contaminant. For example, UV/H2O2 in the degradation for domestic wastewater treatment (0.22 kWh m−3) [168] is much lower than synthetic pharmaceutical wastewater treatment (322 kWh m−3) [169]. The photocatalyst presented relatively higher average EEO values. However, the higher values presented are due to the use of blacklight UV lamps [135]. Excluding the values computed with the blacklight UV lamps, the photocatalyst has a relatively low average EEO value of 4.77 kWh m−3. In the UV/combination list, the EEO values ranged from 0.32395–280 kWh m−3, as the combined process may be even more costly as more chemicals or operational costs from the additional equipment associated with the respective combined processes. Higher treatment efficiencies can result in lower treatment time and drive down the EEO as reported by Sgroi et. al. in the treatment of micro-pollutants found in tertiary wastewater effluent [170], whereas UV/Fenton had a relatively high EEO value due to the relatively poorer degradation performance. Solar/Fenton overall had a lower EEO value due to its low energy requirement, yet good degradation performance of CECs.
To circumvent the high cost associated with the operation of UV lamps, there has been growing interest in the use of solar-powered processes with a lower EEO value. Solar/Fenton presents low EEO values across various wastewater treatment applications: The average EEO value of solar/Fenton is also much lower than UV/Fenton at 2.62 and 16.67 kWh m−3, respectively. Furthermore, operating the solar/Fenton process at a pilot-scale did not significantly increase the EEO values (4.39 kWh m−3), as reported by Expósito et al. [171]. This shows that the solar/Fenton process has potential for scale-up operations and more studies can be done on this aspect for the overall management of CECs. While the computation of EEO might not be a fair comparison when the degradation nature of CECs is so vastly different, it provides a good indication of the potential cost for the degradation of such wastewater. UV and UV/Oxidant processes are also not as effective for hard to degrade compounds such as perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), and bisphenol A (BPA), whereas solar/Fenton and UV/catalysis show great potential due to their generally low EEO values. Besides, solar/catalyst also has the potential to be further developed with potentially lower EEO values than UV/catalysis processes. However, due to limited research on solar/catalyst, a reliable EEO value could not be obtained as a comparison.
Based on the literature review, the cost–benefit analysis of each process is summarized in Table 9. Generally, UV/oxidant processes are disadvantageous due to the selective nature of radical generation and low degradation performance of CECs, despite the ease of implementation. UV/H2O2 has a low molar absorption coefficient of H2O (ε = 18.6 M−1 cm−1 at 254 nm) resulting in the poor generation of •OH radicals [172]. For UV-persulfate and UV/Cl2 processes, the radical species generated are more selective than •OH. S2O82- was shown to display higher sensitivity to the DOM composition of water matrix as compared to UV/H2O2 [173], while •Cl reacts readily with electron-rich contaminants [53]. pH controls are also crucial in the stability of radical species [53].
UV/O3 has a relatively high degradation performance which resulted in a lower-than-expected EEO value. However, UV/O3 is a rather expensive method, as it utilizes both UV lamps and ozone generator(s), which are both energy-intensive [54]. Ozone also has a relatively limited UV absorbance wavelength [110] and pH is usually more effective at pH above 9, which makes it unsuitable for CEC treatment.
For photo-Fenton, heterogeneous Fenton can utilize a broadband of wavelengths of light (180–400 nm). However, with low quantum yield, it results in poor degradation performance [56]. Hence, recent research focuses on the use of Fe (III) ligands (e.g., ethylenediamine-N,N’-disuccinic acid (EEDS), ethylenediaminetetraacetic acid (EDTA), etc.) which have a higher quantum yield due to higher UV absorption [56]. These ligands also have the capabilities of utilizing a broader band of solar energy (180–800 nm) as reported by Soriano–Molina et al. [121].
Photocatalysts are generally hampered by the leaching of catalysts as well as the reusability of the catalyst. To combat these issues, researchers doped ferromagnetic materials to TiO2 for easy retrieval of catalysts [57,61,114]. The use of solar irradiation is also currently being explored, with modification of TiO2 based catalyst, to allow solar light to be used as an irradiation source [57]. While EEO for solar-based TiO2 has not to be computed, due to a general lack of study in this sector, this is a potential area for development, whereas zinc-based catalyst is generally not as effective due to inefficiency in the utilization of band gaps in light irradiation. An alternative new catalyst, such as WO3, and carbon-based catalysts suffer similar separation issues and efficiency, although they show potential to use sunlight/visible light as a source of irradiation.

4. Conclusions

In recent years there has been growing emphasis and efforts made for the management of CECs. Due to the rapid industry changes and growing concerns about the impact of CECs, the management of CECs is particularly challenging. CECs were conventionally classified based on their polarity and hydrophobicity (Kow). However, polarity and Kow of CECs are dependent on their molecular structures and hence the removal of CECs by light-driven processes could be better estimated from their molecular structures. A commonality among other unlisted CECs with reported CECs would serve as a basis for its degradation performance, which is an attractive means to manage newer and insufficiently studied CECs. The occurrence and fate of CECs also suggest that higher concentrations are detected at the effluents of treatment plants. Various point sources were found to concentrate at the effluent of water treatment facilities, due to their recalcitrance to biodegradation. Photo-driven processes for this application are particularly attractive as the effluent characteristics are particularly suitable for photo processes. Effluent is generally low in TSS and NOM after the pre-treatment step. This review then proposed a two-pronged approach in the overall management of CECs, which includes the photo remediation and detection of such contaminants. Key factors that affect light-driven processes were discussed: pH, water matrix, oxidant dosage, catalyst dosage, UV absorbance, and degradation mechanisms.
UV/oxidant processes are generally more selective in the degradation of CECs. As found in the review, UV/H2O2 has low values of H2O2 molar absorption coefficient at 254 nm. Photo oxidant processes use persulfate and its derivatives were found to be better at CECs degradation. However, this process requires pH adjustment and the radicals generated to have a lower charge compared to other radicals formed. Thus, the lower oxidative power of the generated radicals leads to selective CEC degradation. UV/chlorine process has a higher reported degradation performance than UV/H2O2. UV/chlorine uses chemicals that are common for pipeline disinfection for wastewater treatment plant effluent, and hence little modification of the treatment process is needed for immediate application. However, studies noted the formation of undesirable DBPs, and more studies need to be done to limit the formation of such products. Further study is needed on the mechanism and modeling of UV/chlorine to help limit the formation DBPs, for large-scale application of such a process. The computed EEO values of the UV/oxidant process are also the second-lowest next to the solar/Fenton process, which makes this technology particularly attractive.
The UV/ozone process has been demonstrated to have great performance in the degradation of CECs in smaller-scaled reactors and small-scaled pilot studies. However, the high cost of operation and high capital cost of ozone reactors makes this technology not as attractive as other photo technology used in this review. Computed EEO values are the second-highest of all the photo-based technology discussed.
Photo-Fenton has been applied to various large-scale and pilot plant applications, with varying degrees of success. CECs with functional groups that form intermediate compounds like hydroxylated derivatives may impede the performance of Photo-Fenton based degradation. Heterogenous photo-Fenton is shown to be not as popular due to lower reaction kinetics and issues with the catalyst recovery. However, the recent development of solar/Fenton shows promising degradation performance and cost-effectiveness. Computed EEO values showed that solar/Fenton has the lowest EEO values compared to the photo-based treatment of CECs in this review. Coupled with the higher UV wavelength bandwidth of solar/Fenton makes this technology attractive for upscaling, with a key drawback of a high footprint. Ongoing research has been done to reduce the footprint of Solar/Fenton systems. Innovative designs could be done to ensure a larger surface area with a low footprint could be achieved, such as the use of coiled tube reactors or raceway pond designs. Another avenue for further development is the use of high quantum yield ligands which have the ability to utilizing a broader band of solar energy (180–800 nm).
Photocatalysis has a generally lower CECs degradation performance as compared to other photo-based treatment technology, with TiO2 based catalyst having a relatively higher degradation performance. The computed EEO value was the second-highest next to the UV/ozone process. The lower degradation yield is likely due to the operation in a liquid-solid system, resulting in poor mass transfer. As such, many researchers attempted to circumvent this issue by using the nano-sized catalyst. However, the recovery of this nano-catalyst after usage has been an ongoing issue bottlenecking the large-scale application of these processes. Ongoing research has been done to either attach catalysts onto surfaces of reactors or activated carbon to achieve ease to solid-liquid separation. Alternatively, other researchers used magnetic separation as a means of solid–liquid separation by doping TiO2 onto magnetic materials. Due to the highly specific bandgaps needed to excite the catalyst, the general wavelength needed for photocatalysis is of the higher energy bandwidth of 245–400 nm. Hence, ongoing research has been done to decrease the bandgap between the energy states of the catalyst. Some of the ways utilized were also explored in this review, such as the use of doping other transition metals which were also reported to be able to use solar energy as a light irradiation source. Graphitic carbon nitrides (g-C3N4) were demonstrated to enhance photocatalyst and photo-Fenton as a composite material. Ongoing investigation has also been done on newer photocatalysts like WO3 and other carbon-based catalysts to further expand the irradiation bandwidth of light used. Further studies on this aspect could be done for the greater application of such technology. Due to its novelty, limited demonstration of degradation performance was done on a larger scale and in a real wastewater matrix. Another consideration is the cost of the production of these newer materials and the potential toxicity of such materials.
Lastly, light-driven monitoring and detection processes were also highlighted as a potential cost-effective replacement for conventional HPLC or GC detection. The current online monitoring of CEC is based on the removal of surrogate parameters in a specific water treatment process. Although light-driven monitoring and detection still need further research, it is undoubtedly a promising technology and could be served as an early-warning system for CECs contamination.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/w13172340/s1, Supplementary S1 An illustrated example of similar chemical structure of CECs with similar photodegradation performance; Supplementary S2 Bond dissociative energy and their corresponding threshold wavelength; Supplementary S3 Standard Reduction Potentials in Aqueous Medium for degradation of organic compounds; Supplementary S4 Cost Comparison of various light driven AOPs discussed in this review.

Author Contributions

B.C.Y.L.: Conceptualization, investigation, writing—original draft preparation. F.Y.L.: Overall review, editing, investigation, visualization. W.H.L.: Investigation, review, and editing; S.L.O.: Fund acquisition and project management, J.H.: Fund acquisition and project management, overall review. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Sembcorp Corporate Laboratory under the “Achieving low COD in industrial treated effluent through combination of AOPs and biological processes” research project, grant number WBS: R-261-513-005-281.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study is available on request from the corresponding author.

Acknowledgments

This research is supported by the National Research Foundation Singapore, Sembcorp Corporate Laboratory and the National University of Singapore.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Gavrilescu, M.; Demnerová, K.; Aamand, J.; Agathos, S.; Fava, F. Emerging pollutants in the environment: Present and future challenges in biomonitoring, ecological risks and bioremediation. New Biotechnol. 2015, 32, 147–156. [Google Scholar] [CrossRef]
  2. Richardson, S.D.; Ternes, T.A. Water Analysis: Emerging Contaminants and Current Issues. Anal. Chem. 2017, 90, 398–428. [Google Scholar] [CrossRef]
  3. Parida, V.K.; Saidulu, D.; Majumder, A.; Srivastava, A.; Gupta, B.; Gupta, A.K. Emerging contaminants in wastewater: A critical review on occurrence, existing legislations, risk assessment, and sustainable treatment alternatives. J. Environ. Chem. Eng. 2021, 9, 105966. [Google Scholar] [CrossRef]
  4. Choi, Y.Y.; Baek, S.R.; Kim, J.L.; Choi, J.W.; Hur, J.; Lee, T.U.; Park, C.J.; Lee, B.J. Characteristics and Biodegradability of Wastewater Organic Matter in Municipal Wastewater Treatment Plants Collecting Domestic Wastewater and Industrial Discharge. Water (Switz.) 2017, 9, 409. [Google Scholar] [CrossRef] [Green Version]
  5. Ahearn, A. A Regrettable Substitute: The Story of GenX. Pod. Res. Perspect. 2019, 2019. [Google Scholar] [CrossRef]
  6. Sharma, S.; Tolley, H.D.; Farnsworth, P.B.; Lee, M.L. LED-based UV absorption detector with low detection limits for capillary liquid chromatography. Anal. Chem. 2015, 87, 1381–1386. [Google Scholar] [CrossRef]
  7. Safari, G.H.; Yetilmezsoy, K.; Mahvi, A.H.; Zarrabi, M. Post-treatment of secondary wastewater treatment plant effluent using a two-stage fluidized bed bioreactor system. J. Environ. Health Sci. Eng. 2013, 11, 10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Watkinson, A.J.; Murby, E.J.; Kolpin, D.W.; Costanzo, S.D. The occurrence of antibiotics in an urban watershed: From wastewater to drinking water. Sci. Total Environ. 2009, 407, 2711–2723. [Google Scholar] [CrossRef] [PubMed]
  9. Mutiyar, P.K.; Mittal, A.K. Occurrences and fate of an antibiotic amoxicillin in extended aeration-based sewage treatment plant in Delhi, India: A case study of emerging pollutant. Desalination Water Treat. 2013, 51, 6158–6164. [Google Scholar] [CrossRef]
  10. Azanu, D.; Styrishave, B.; Darko, G.; Weisser, J.J.; Abaidoo, R.C. Occurrence and risk assessment of antibiotics in water and lettuce in Ghana. Sci. Total Environ. 2018, 622, 293–305. [Google Scholar] [CrossRef] [PubMed]
  11. Ashfaq, M.; Li, Y.; Rehman, M.S.U.; Zubir, M.; Mustafa, G.; Nazar, M.F.; Yu, C.P.; Sun, Q. Occurrence, spatial variation and risk assessment of pharmaceuticals and personal care products in urban wastewater, canal surface water, and their sediments: A case study of Lahore, Pakistan. Sci. Total Environ. 2019, 688, 653–663. [Google Scholar] [CrossRef]
  12. Ali, A.M.; Rønning, H.T.; al Arif, W.M.; Kallenborn, R.; Kallenborn, R. Occurrence of pharmaceuticals and personal care products in effluent-dominated Saudi Arabian coastal waters of the Red Sea. Chemosphere 2017, 175, 505–513. [Google Scholar] [CrossRef]
  13. Kermia, A.E.B.; Fouial-Djebbar, D.; Trari, M. Occurrence, fate and removal efficiencies of pharmaceuticals in wastewater treatment plants (WWTPs) discharging in the coastal environment of Algiers. Comptes Rendus Chim. 2016, 19, 963–970. [Google Scholar] [CrossRef]
  14. Rosal, R.; Rodríguez, A.; Perdigón-Melón, J.A.; Petre, A.; García-Calvo, E.; Gómez, M.J.; Agüera, A.; Fernández-Alba, A.R. Occurrence of emerging pollutants in urban wastewater and their removal through biological treatment followed by ozonation. Water Res. 2009, 44, 578–588. [Google Scholar] [CrossRef]
  15. Ngubane, N.P.; Naicker, D.; Ncube, S.; Chimuka, L.; Madikizela, L.M. Determination of naproxen, diclofenac and ibuprofen in Umgeni estuary and seawater: A case of northern Durban in KwaZulu–Natal Province of South Africa. Reg. Stud. Mar. Sci. 2019, 29, 100675. [Google Scholar] [CrossRef]
  16. Giang, C.N.D.; Sebesvari, Z.; Renaud, F.; Rosendahl, I.; Minh, Q.H.; Amelung, W. Occurrence and dissipation of the antibiotics sulfamethoxazole, sulfadiazine, trimethoprim, and enrofloxacin in the Mekong Delta, Vietnam. PLoS ONE 2015, 10, e0131855. [Google Scholar] [CrossRef] [Green Version]
  17. Wei, R.; Ge, F.; Huang, S.; Chen, M.; Wang, R. Occurrence of veterinary antibiotics in animal wastewater and surface water around farms in Jiangsu Province, China. Chemosphere 2011, 82, 1408–1414. [Google Scholar] [CrossRef] [PubMed]
  18. Guo, Y.C.; Krasner, S.W. Occurrence of primidone, carbamazepine, caffeine, and precursors for N-nitrosodimethylamine in drinking water sources impacted by wastewater. J. Am. Water Resour. Assoc. 2009, 45, 58–67. [Google Scholar] [CrossRef]
  19. Huerta-Fontela, M.; Galceran, M.T.; Ventura, F. Occurrence and removal of pharmaceuticals and hormones through drinking water treatment. Water Res. 2011, 45, 1432–1442. [Google Scholar] [CrossRef] [PubMed]
  20. Rimayi, C.; Odusanya, D.; Weiss, J.M.; de Boer, J.; Chimuka, L. Contaminants of emerging concern in the Hartbeespoort Dam catchment and the uMngeni River estuary 2016 pollution incident, South Africa. Sci. Total Environ. 2018, 627, 1008–1017. [Google Scholar] [CrossRef]
  21. Gan, Z.; Sun, H.; Feng, B.; Wang, R.; Zhang, Y. Occurrence of seven artificial sweeteners in the aquatic environment and precipitation of Tianjin, China. Water Res. 2013, 47, 4928–4937. [Google Scholar] [CrossRef]
  22. Kahl, S.; Kleinsteuber, S.; Nivala, J.; van Afferden, M.; Reemtsma, T. Emerging Biodegradation of the Previously Persistent Artificial Sweetener Acesulfame in Biological Wastewater Treatment. Environ. Sci. Technol. 2018, 52, 2717–2725. [Google Scholar] [CrossRef]
  23. Ouyang, W.; Zhang, Y.; Gu, X.; Tysklind, M.; Lin, G.; Wang, B.; Xin, M. Occurrence, transportation, and distribution difference of typical herbicides from estuary to bay. Environ. Int. 2019, 130, 104858. [Google Scholar] [CrossRef]
  24. Bachetti, R.A.; Ursela, N.; Morgante, V.; Damilano, G.; Porporatta, C.; Agostini, E.; Morgante, C. Monitoring of Atrazine Pollution and its Spatial-Seasonal Variation on Surface Water Sources of an Agricultural River Basin. Bull. Environ. Contam. Toxicol. 2021, 106, 929–935. [Google Scholar] [CrossRef] [PubMed]
  25. Rimayi, C.; Odusanya, D.; Weiss, J.M.; de Boer, J.; Chimuka, L. Seasonal variation of chloro-s-triazines in the Hartbeespoort Dam catchment, South Africa. Sci. Total Environ. 2018, 613-614, 472–482. [Google Scholar] [CrossRef] [PubMed]
  26. Carrera, G.; Vegué, L.; Ventura, F.; Hernández-Valencia, A.; Devesa, R.; Boleda, M.R. Dioxanes and dioxolanes in source waters: Occurrence, odor thresholds and behavior through upgraded conventional and advanced processes in a drinking water treatment plant. Water Res. 2019, 156, 404–413. [Google Scholar] [CrossRef] [PubMed]
  27. Stepien, D.K.; Diehl, P.; Helm, J.; Thoms, A.; Püttmann, W. Fate of 1,4-dioxane in the aquatic environment: From sewage to drinking water. Water Res. 2014, 48, 406–419. [Google Scholar] [CrossRef]
  28. Kunacheva, C.; Fujii, S.; Tanaka, S.; Seneviratne, S.T.M.L.D.; Lien, N.P.H.; Nozoe, M.; Kimura, K.; Shivakoti, B.R.; Harada, H. Worldwide surveys of perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) in water environment in recent years. Water Sci. Technol. 2012, 66, 2764–2771. [Google Scholar] [CrossRef] [PubMed]
  29. Hu, J.; Yu, J.; Tanaka, S.; Fujii, S. Perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) in water environment of Singapore. Water Air Soil Pollut. 2010, 216, 179–191. [Google Scholar] [CrossRef]
  30. Dalahmeh, S.; Tirgani, S.; Komakech, A.J.; Niwagaba, C.B.; Ahrens, L. Per- and polyfluoroalkyl substances (PFASs) in water, soil and plants in wetlands and agricultural areas in Kampala, Uganda. Sci. Total Environ. 2018, 631, 660–667. [Google Scholar] [CrossRef]
  31. Huang, Z.; Zhao, J.L.; Yang, Y.Y.; Jia, Y.W.; Zhang, Q.Q.; Chen, C.E.; Liu, Y.S.; Yang, B.; Xie, L.; Ying, G.G. Occurrence, mass loads and risks of bisphenol analogues in the Pearl River Delta region, South China: Urban rainfall runoff as a potential source for receiving rivers. Environ. Pollut. 2020, 263, 114361. [Google Scholar] [CrossRef]
  32. Lalwani, D.; Ruan, Y.; Taniyasu, S.; Yamazaki, E.; Kumar, N.J.I.; Lam, P.K.S.; Wang, X.; Yamashita, N. Nationwide distribution and potential risk of bisphenol analogues in Indian waters. Ecotoxicol. Environ. Saf. 2020, 200, 110718. [Google Scholar] [CrossRef] [PubMed]
  33. AlAmmari, A.M.; Khan, M.R.; Aqel, A. Trace identification of endocrine-disrupting bisphenol A in drinking water by solid-phase extraction and ultra-performance liquid chromatography-tandem mass spectrometry. J. King Saud Univ. Sci. 2019, 32, 1634–1640. [Google Scholar] [CrossRef]
  34. Santos, M.M.d.; Hoppe-Jones, C.; Snyder, S.A. DEET occurrence in wastewaters: Seasonal, spatial and diurnal variability - mismatches between consumption data and environmental detection. Environ. Int. 2019, 132, 105038. [Google Scholar] [CrossRef]
  35. Chase, D.A.; Karnjanapiboonwong, A.; Fang, Y.; Cobb, G.P.; Morse, A.N.; Anderson, T.A. Occurrence of synthetic musk fragrances in effluent and non-effluent impacted environments. Sci. Total Environ. 2011, 416, 253–260. [Google Scholar] [CrossRef] [PubMed]
  36. Zhao, Y.; Qin, F.; Boyd, J.M.; Anichina, J.; Li, X.F. Characterization and determination of chloro- and bromo-benzoquinones as new chlorination disinfection byproducts in drinking water. Anal. Chem. 2010, 82, 4599–4605. [Google Scholar] [CrossRef] [PubMed]
  37. Wu, H.; Long, K.; Sha, Y.; Lu, D.; Xia, Y.; Mo, Y.; Yang, Q.; Zheng, W.; Yang, M.; Wei, X. Occurrence and toxicity of halobenzoquinones as drinking water disinfection byproducts. Sci. Total Environ. 2021, 770, 145277. [Google Scholar] [CrossRef] [PubMed]
  38. Tugulea, A.M.; Aranda-Rodriguez, R.; Bérubé, D.; Giddings, D.; Lemieux, F.; Hnatiw, J.; Dabeka, L.; Breton, F. The influence of precursors and treatment process on the formation of Iodo-THMs in Canadian drinking water. Water Res. 2018, 130, 215–223. [Google Scholar] [CrossRef] [PubMed]
  39. Allard, S.; Charrois, J.W.A.; Joll, C.A.; Heitz, A. Simultaneous analysis of 10 trihalomethanes at nanogram per liter levels in water using solid-phase microextraction and gas chromatography mass-spectrometry. J. Chromatogr. A 2012, 1238, 15–21. [Google Scholar] [CrossRef] [Green Version]
  40. National Research Council. A Framework to Guide Selection of Chemical Alternatives; The National Academies Press: Washington, DC, USA, 2014. [Google Scholar] [CrossRef]
  41. DiGuiseppi, W.; Walecka-Hutchison, C.; Hatton, J. 1,4-Dioxane Treatment Technologies. Remediat. J. 2016, 27, 71–92. [Google Scholar] [CrossRef]
  42. Behnajady, M.A.; Eskandarloo, H.; Modirshahla, N.; Shokri, M. Influence of the chemical structure of organic pollutants on photocatalytic activity of TiO2 nanoparticles: Kinetic analysis and evaluation of electrical energy per order (EEO). Dig. J. Nanomater. Biostruct. 2011, 6, 1887–1895. [Google Scholar]
  43. Sivaranjanee, R.; Kumar, P.S. A review on remedial measures for effective separation of emerging contaminants from wastewater. Environ. Technol. Innov. Environ. Technol. Innov. 2021, 23, 101741. [Google Scholar] [CrossRef]
  44. Moschet, C.; Lew, B.M.; Hasenbein, S.; Anumol, T.; Young, T.M. LC- and GC-QTOF-MS as Complementary Tools for a Comprehensive Micropollutant Analysis in Aquatic Systems. Environ. Sci. Technol. 2017, 51, 1553–1561. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Perez, E.R.; Knapp, J.A.; Horn, C.K.; Stillman, S.L.; Evans, J.E.; Arfsten, D.P. Comparison of LC-MS-MS and GC-MS analysis of benzodiazepine compounds included in the drug demand reduction urinalysis program. J. Anal. Toxicol. 2016, 40, 201–207. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. García-Córcoles, M.T.; Rodríguez-Gómez, R.; Alarcón-Gómez, B.D.; Çipa, M.; Martín-Pozo, L.; Kauffmann, J.M.; Zafra-Gómez, A. Chromatographic Methods for the Determination of Emerging Contaminants in Natural Water and Wastewater Samples: A Review. Crit. Rev. Anal. Chem. 2018, 49, 160–186. [Google Scholar] [CrossRef] [PubMed]
  47. Andersson, A.; Ashiq, M.J.; Shoeb, M.; Karlsson, S.; Bastviken, D.; Kylin, H. Evaluating gas chromatography with a halogen-specific detector for the determination of disinfection by-products in drinking water. Environ. Sci. Pollut. Res. 2018, 26, 7305–7314. [Google Scholar] [CrossRef] [Green Version]
  48. Yang, M.; Zhang, X. Current trends in the analysis and identification of emerging disinfection byproducts. Trends Environ. Anal. Chem. 2016, 10, 24–34. [Google Scholar] [CrossRef]
  49. Ryu, H.; Li, B.; de Guise, S.; McCutcheon, J.; Lei, Y. Recent progress in the detection of emerging contaminants PFASs. J. Hazard. Mater. 2020, 408, 124437. [Google Scholar] [CrossRef]
  50. Calvert, J.G.; Pms, J.N., Jr. Photochemistry; John Wiley and Sons Inc.: Hoboken, NJ, USA, 1967; Volume 6, p. 601. [Google Scholar]
  51. Zhang, W.; Xiao, Z.; An, T.; Song, Z.; Fu, J.; Sheng, G.; Cui, M. Kinetics, degradation pathway and reaction mechanism of advanced oxidation of 4-nitrophenol in water by a UV/H2O2 process. J. Chem. Technol. Biotechnol. 2003, 78, 788–794. [Google Scholar] [CrossRef]
  52. Liu, Y.; He, X.; Fu, Y.; Dionysiou, D.D. Kinetics and mechanism investigation on the destruction of oxytetracycline by UV-254 nm activation of persulfate. J. Hazard. Mater. 2016, 305, 229–239. [Google Scholar] [CrossRef] [PubMed]
  53. Watts, M.J.; Linden, K.G. Chlorine photolysis and subsequent OH radical production during UV treatment of chlorinated water. Water Res. 2007, 41, 2871–2878. [Google Scholar] [CrossRef] [PubMed]
  54. Serpone, N.; Artemev, Y.M.; Ryabchuk, V.K.; Emeline, A.V.; Horikoshi, S. Light-driven advanced oxidation processes in the disposal of emerging pharmaceutical contaminants in aqueous media: A brief review. Curr. Opin. Green Sustain. Chem. 2017, 6, 18–33. [Google Scholar] [CrossRef]
  55. Cai, Q.Q.; Lee, B.C.Y.; Ong, S.L.; Hu, J.Y. Fluidized-bed Fenton technologies for recalcitrant industrial wastewater treatment–Recent advances, challenges and perspective. Water Res. 2020, 190, 116692. [Google Scholar] [CrossRef]
  56. Clarizia, L.; Russo, D.; di Somma, I.; Marotta, R.; Andreozzi, R. Homogeneous photo-Fenton processes at near neutral pH: A review. Appl. Catal. B Environ. 2017, 209, 358–371. [Google Scholar] [CrossRef]
  57. Dominguez, S.; Huebra, M.; Han, C.; Campo, P.; Nagagouda, M.N.; Rivero, M.J.; Ortiz, I.; Dionysiou, D.D. Magnetically recoverable TiO2-WO3 photocatalyst to oxidize bisphenol A from model wastewater under simulated solar light. Environ. Sci. Pollut. Res. 2016, 24, 12589–12598. [Google Scholar] [CrossRef] [Green Version]
  58. Cai, Q.; Hu, J. Decomposition of sulfamethoxazole and trimethoprim by continuous UVA/LED/TiO2 photocatalysis: Decomposition pathways, residual antibacterial activity and toxicity. J. Hazard. Mater. 2017, 323, 527–536. [Google Scholar] [CrossRef]
  59. Vela, N.; Calín, M.; Yáñez-Gascón, M.J.; Garrido, I.; Pérez-Lucas, G.; Fenoll, J.; Navarro, S. Photocatalytic oxidation of six endocrine disruptor chemicals in wastewater using ZnO at pilot plant scale under natural sunlight. Environ. Sci. Pollut. Res. 2018, 25, 34995–35007. [Google Scholar] [CrossRef]
  60. Ding, H.; Hu, J. Degradation of ibuprofen by UVA-LED/TiO2/persulfate process: Kinetics, mechanism, water matrix effects, intermediates and energy consumption. Chem. Eng. J. 2020, 397, 125462. [Google Scholar] [CrossRef]
  61. Mirzaei, A.; Chen, Z.; Haghighat, F.; Yerushalmi, L. Magnetic fluorinated mesoporous g-C3N4 for photocatalytic degradation of amoxicillin: Transformation mechanism and toxicity assessment. Appl. Catal. B Environ. 2018, 242, 337–348. [Google Scholar] [CrossRef]
  62. Garcia-Segura, S.; Bellotindos, L.M.; Huang, Y.H.; Brillas, E.; Lu, M.C. Fluidized-bed Fenton process as alternative wastewater treatment technology—A review. J. Taiwan Inst. Chem. Eng. 2016, 67, 211–225. [Google Scholar] [CrossRef]
  63. Szpyrkowicz, L.; Juzzolino, C.; Kaul, S.N. A comparative study on oxidation of disperse dyes by electrochemical process, ozone, hypochlorite and fenton reagent. Water Res. 2001, 35, 2129–2136. [Google Scholar] [CrossRef]
  64. Tang, W.Z.; Huang, C.P. 2,4-Dichlorophenol Oxidation Kinetics by Fenton’s Reagent. Environ. Technol. (UK) 1996, 17, 1371–1378. [Google Scholar] [CrossRef]
  65. Velichkova, F.; Delmas, H.; Julcour, C.; Koumanova, B. Heterogeneous fenton and photo-fenton oxidation for paracetamol removal using iron containing ZSM-5 zeolite as catalyst. AIChE J. 2016, 63, 669–679. [Google Scholar] [CrossRef] [Green Version]
  66. Zammouri, L.; Aboulaich, A.; Capoen, B.; Bouazaoui, M.; Sarakha, M.; Stitou, M.; Mahiou, R. Enhancement under UV–visible and visible light of the ZnO photocatalytic activity for the antibiotic removal from aqueous media using Ce-doped Lu3Al5O12 nanoparticles. Mater. Res. Bull. 2018, 106, 162–169. [Google Scholar] [CrossRef]
  67. Funai, D.H.; Didier, F.; Giménez, J.; Esplugas, S.; Marco, P.; Machulek, A. Photo-Fenton treatment of valproate under UVC, UVA and simulated solar radiation. J. Hazard. Mater. 2017, 323, 537–549. [Google Scholar] [CrossRef]
  68. Eskandarian, M.R.; Choi, H.; Fazli, M.; Rasoulifard, M.H. Effect of UV-LED wavelengths on direct photolytic and TiO2 photocatalytic degradation of emerging contaminants in water. Chem. Eng. J. 2016, 300, 414–422. [Google Scholar] [CrossRef]
  69. Summerfelt, S.T. Ozonation and UV irradiation—An introduction and examples of current applications. Aquac. Eng. 2003, 28, 21–36. [Google Scholar] [CrossRef] [Green Version]
  70. Guerra, M.M.H.; Alberola, I.O.; Rodriguez, S.M.; López, A.A.; Merino, A.A.; Alonso, J.M.Q. Oxidation mechanisms of amoxicillin and paracetamol in the photo-Fenton solar process. Water Res. 2019, 156, 232–240. [Google Scholar] [CrossRef]
  71. Long, M.; Brame, J.; Qin, F.; Bao, J.; Li, Q.; Alvarez, P.J.J. Phosphate Changes Effect of Humic Acids on TiO2 Photocatalysis: From Inhibition to Mitigation of Electron-Hole Recombination. Environ. Sci. Technol. 2017, 51, 514–521. [Google Scholar] [CrossRef]
  72. Lu, G.; Hu, J. Effect of alpha-hydroxy acids on transformation products formation and degradation mechanisms of carbamazepine by UV/H2O2 process. Sci. Total Environ. 2019, 689, 70–78. [Google Scholar] [CrossRef]
  73. Ren, M.; Drosos, M.; Frimmel, F.H. Inhibitory effect of NOM in photocatalysis process: Explanation and resolution. Chem. Eng. J. 2018, 334, 968–975. [Google Scholar] [CrossRef]
  74. Uyguner-Demirel, C.S.; Birben, N.C.; Bekbolet, M. Elucidation of background organic matter matrix effect on photocatalytic treatment of contaminants using TiO2: A review. Catal. Today 2017, 284, 202–214. [Google Scholar] [CrossRef]
  75. Du, Y.; Zhou, M.; Lei, L. The role of oxygen in the degradation of p-chlorophenol by Fenton system. J. Hazard. Mater. 2007, 139, 108–115. [Google Scholar] [CrossRef]
  76. Duesterberg, C.K.; Cooper, W.J.; Waite, T.D. Fenton-mediated oxidation in the presence and absence of oxygen. Environ. Sci. Technol. 2005, 39, 5052–5058. [Google Scholar] [CrossRef] [PubMed]
  77. Li, S.; Hu, J. Transformation products formation of ciprofloxacin in UVA/LED and UVA/LED/TiO2 systems: Impact of natural organic matter characteristics. Water Res. 2018, 132, 320–330. [Google Scholar] [CrossRef]
  78. Nasseh, N.; Taghavi, L.; Barikbin, B.; Nasseri, M.A. Synthesis and characterizations of a novel FeNi3/SiO2/CuS magnetic nanocomposite for photocatalytic degradation of tetracycline in simulated wastewater. J. Clean. Prod. 2018, 179, 42–54. [Google Scholar] [CrossRef]
  79. Yamal-Turbay, E.; Graells, M.; Pérez-Moya, M. Systematic assessment of the influence of hydrogen peroxide dosage on caffeine degradation by the photo-fenton process. Ind. Eng. Chem. Res. 2012, 51, 4770–4778. [Google Scholar] [CrossRef]
  80. Song, C.; Wang, L.; Ren, J.; Lv, B.; Sun, Z.; Yan, J.; Li, X.; Liu, J. Comparative study of diethyl phthalate degradation by UV/H2O2 and UV/TiO2: Kinetics, mechanism, and effects of operational parameters. Environ. Sci. Pollut. Res. 2015, 23, 2640–2650. [Google Scholar] [CrossRef]
  81. Qi, W.; Hu, J. NDMA formation potential removal in treated effluent by UV/ H2O2 process. J. Water Reuse Desalin. 2015, 6, 156–166. [Google Scholar] [CrossRef] [Green Version]
  82. Chu, X.; Xiao, C.; Hu, J.; Quek, E.; Xie, R.; Pang, T.; Xing, Y. Pilot-scale UV/H2O2 study for emerging organic contaminants decomposition. Rev. Environ. Health 2016, 31, 71–74. [Google Scholar] [CrossRef]
  83. Carotenuto, M.; Libralato, G.; Gürses, H.; Siciliano, A.; Rizzo, L.; Guida, M.; Lofrano, G. Nonylphenol deca-ethoxylate removal from wastewater by UV/H2O2: Degradation kinetics and toxicity effects. Process Saf. Environ. Prot. 2019, 124, 1–7. [Google Scholar] [CrossRef]
  84. Huang, Y.; Liu, Y.; Kong, M.; Xu, E.G.; Coffin, S.; Schlenk, D.; Dionysiou, D.D. Efficient degradation of cytotoxic contaminants of emerging concern by UV/H2O2. Environ. Sci. Water Res. Technol. 2018, 4, 1272–1281. [Google Scholar] [CrossRef]
  85. Huang, Y.; Kong, M.; Coffin, S.; Cochran, K.H.; Westerman, D.C.; Schlenk, D.; Richardson, S.D.; Lei, L.; Dionysiou, D.D. Degradation of contaminants of emerging concern by UV/H2O2 for water reuse: Kinetics, mechanisms, and cytotoxicity analysis. Water Res. 2020, 174, 115587. [Google Scholar] [CrossRef] [PubMed]
  86. Yu, L.; Iranmanesh, S.; Keir, I.; Achari, G. A field pilot study on treating groundwater contaminated with sulfolane using UV/H2O2. Water (Switz.) 2020, 12, 1200. [Google Scholar] [CrossRef] [Green Version]
  87. Rott, E.; Kuch, B.; Lange, C.; Richter, P.; Kugele, A.; Minke, R. Removal of emerging contaminants and estrogenic activity from wastewater treatment plant effluent with UV/chlorine and UV/H2O2 advanced oxidation treatment at pilot scale. Int. J. Environ. Res. Public Health 2018, 15, 935. [Google Scholar] [CrossRef] [Green Version]
  88. Cerreta, G.; Roccamante, M.A.; Oller, I.; Malato, S.; Rizzo, L. Contaminants of emerging concern removal from real wastewater by UV/free chlorine process: A comparison with solar/free chlorine and UV/H2O2 at pilot scale. Chemosphere 2019, 236, 124354. [Google Scholar] [CrossRef]
  89. Starling, M.C.V.M.; Souza, P.P.; le Person, A.; Amorim, C.C.; Criquet, J. Intensification of UV-C treatment to remove emerging contaminants by UV-C/H2O2 and UV-C/S2O82−: Susceptibility to photolysis and investigation of acute toxicity. Chem. Eng. J. 2019, 376, 120856. [Google Scholar] [CrossRef]
  90. Zhou, K.; Wang, Z.; Wang, X.; Jiao, G.; Li, Y.; Sun, S.P.; Chen, X.D. Degradation of emerging pharmaceutical micropollutants in municipal secondary effluents by low-pressure UVC-activated HSO5− and S2O82− AOPs. Chem. Eng. J. 2020, 393, 124712. [Google Scholar] [CrossRef]
  91. Dhaka, S.; Kumar, R.; Khan, M.A.; Paeng, K.J.; Kurade, M.B.; Kim, S.J.; Jeon, B.H. Aqueous phase degradation of methyl paraben using UV-activated persulfate method. Chem. Eng. J. 2017, 321, 11–19. [Google Scholar] [CrossRef]
  92. Ghauch, A.; Baalbaki, A.; Amasha, M.; el Asmar, R.; Tantawi, O. Contribution of persulfate in UV-254 nm activated systems for complete degradation of chloramphenicol antibiotic in water. Chem. Eng. J. 2017, 317, 1012–1025. [Google Scholar] [CrossRef]
  93. Hou, S.; Ling, L.; Shang, C.; Guan, Y.; Fang, J. Degradation kinetics and pathways of haloacetonitriles by the UV/persulfate process. Chem. Eng. J. 2017, 320, 478–484. [Google Scholar] [CrossRef]
  94. Ao, X.; Liu, W. Degradation of sulfamethoxazole by medium pressure UV and oxidants: Peroxymonosulfate, persulfate, and hydrogen peroxide. Chem. Eng. J. 2017, 313, 629–637. [Google Scholar] [CrossRef]
  95. Khan, S.; He, X.; Khan, J.A.; Khan, H.M.; Boccelli, D.L.; Dionysiou, D.D. Kinetics and mechanism of sulfate radical- and hydroxyl radical-induced degradation of highly chlorinated pesticide lindane in UV/peroxymonosulfate system. Chem. Eng. J. 2017, 318, 135–142. [Google Scholar] [CrossRef]
  96. Acero, J.L.; Benítez, F.J.; Real, F.J.; Rodríguez, E. Degradation of selected emerging contaminants by UV-activated persulfate: Kinetics and influence of matrix constituents. Sep. Purif. Technol. 2018, 201, 41–50. [Google Scholar] [CrossRef]
  97. Bao, Y.; Deng, S.; Jiang, X.; Qu, Y.; He, Y.; Liu, L.; Chai, Q.; Mumtaz, M.; Huang, J.; Cagnetta, G.; et al. Degradation of PFOA Substitute: GenX (HFPO-DA Ammonium Salt): Oxidation with UV/Persulfate or Reduction with UV/Sulfite? Environ. Sci. Technol. 2018, 52, 11728–11734. [Google Scholar] [CrossRef] [PubMed]
  98. Rizzo, L.; Agovino, T.; Nahim-Granados, S.; Castro-Alférez, M.; Fernández-Ibáñez, P.; Polo-López, M.I. Tertiary treatment of urban wastewater by solar and UV-C driven advanced oxidation with peracetic acid: Effect on contaminants of emerging concern and antibiotic resistance. Water Res. 2018, 149, 272–281. [Google Scholar] [CrossRef]
  99. Zhou, S.; Li, L.; Wu, Y.; Zhu, S.; Zhu, N.; Bu, L.; Dionysiou, D.D. UV365 induced elimination of contaminants of emerging concern in the presence of residual nitrite: Roles of reactive nitrogen species. Water Res. 2020, 178, 115829. [Google Scholar] [CrossRef]
  100. Li, M.; Li, W.; Bolton, J.R.; Blatchley, E.R.; Qiang, Z. Organic Pollutant Degradation in Water by the Vacuum-Ultraviolet/Ultraviolet/H2O2 Process: Inhibition and Enhancement Roles of H2O2. Environ. Sci. Technol. 2018, 53, 912–918. [Google Scholar] [CrossRef]
  101. Li, C.; Zhang, D.; Peng, J.; Li, X. The effect of pH, nitrate, iron (III) and bicarbonate on photodegradation of oxytetracycline in aqueous solution. J. Photochem. Photobiol. A Chem. 2018, 356, 239–247. [Google Scholar] [CrossRef]
  102. Jing, L.; Chen, B.; Wen, D.; Zheng, J.; Zhang, B. The removal of COD and NH3-N from atrazine production wastewater treatment using UV/O3: Experimental investigation and kinetic modeling. Environ. Sci. Pollut. Res. 2018, 25, 2691–2701. [Google Scholar] [CrossRef]
  103. Xu, Y.; Wu, Y.; Zhang, W.; Fan, X.; Wang, Y.; Zhang, H. Performance of artificial sweetener sucralose mineralization via UV/O3 process: Kinetics, toxicity and intermediates. Chem. Eng. J. 2018, 353, 626–634. [Google Scholar] [CrossRef]
  104. Derco, J.; Šimovičová, K.; Dudáš, J.; Valičková, M. Removal of BTX Contaminants with O3 and O3/UV Processes. In Physico-Chemical Wastewater Treatment and Resource Recovery; IntechOpen: London, UK, 2017; Available online: https://www.intechopen.com/chapters/52713 (accessed on 3 May 2021).
  105. Takashina, T.A.; Leifeld, V.; Zelinski, D.W.; Mafra, M.R.; Igarashi-Mafra, L. Application of Response Surface Methodology for Coffee Effluent Treatment by Ozone and Combined Ozone/UV. Ozone Sci. Eng. 2018, 40, 293–304. [Google Scholar] [CrossRef]
  106. Yao, W.; Rehman, S.W.U.; Wang, H.; Yang, H.; Yu, G.; Wang, Y. Pilot-scale evaluation of micropollutant abatements by conventional ozonation, UV/O3, and an electro-peroxone process. Water Res. 2018, 138, 106–117. [Google Scholar] [CrossRef] [PubMed]
  107. Dai, X.; Xie, Z.; Dorian, B.; Gray, S.; Zhang, J. Comparative study of PFAS treatment by UV, UV/ozone, and fractionations with air and ozonated air. Environ. Sci. Water Res. Technol. 2019, 5, 1897–1907. [Google Scholar] [CrossRef]
  108. Rajabizadeh, K.; Yazdanpanah, G.; Dowlatshahi, S.; Malakootian, M. Photooxidation Process Efficiency (UV/O3) for P-nitroaniline Removal from Aqueous Solutions. Ozone Sci. Eng. 2019, 42, 420–427. [Google Scholar] [CrossRef]
  109. Fu, P.; Ma, Y.; Yang, H.; Li, G.; Lin, X. Ozone and ozone/vacuum-UV degradation of diethyl dithiocarbamate collector: Kinetics, mineralization, byproducts and pathways. RSC Adv. 2019, 9, 23579–23588. [Google Scholar] [CrossRef] [Green Version]
  110. Von Sonntag, C.; von Gunten, U. Chemistry of Ozone in Water and Wastewater Treatment: From Basic Principles to Applications; IWA Publishing: London, UK, 2015. [Google Scholar] [CrossRef]
  111. Molina, C.B.; Sanz-Santos, E.; Boukhemkhem, A.; Bedia, J.; Belver, C.; Rodriguez, J.J. Removal of emerging pollutants in aqueous phase by heterogeneous Fenton and photo-Fenton with Fe2O3-TiO2-clay heterostructures. Environ. Sci. Pollut. Res. 2020, 27, 38434–38445. [Google Scholar] [CrossRef]
  112. Du, Z.; Li, K.; Zhou, S.; Liu, X.; Yu, Y.; Zhang, Y.; He, Y.; Zhang, Y. Degradation of ofloxacin with heterogeneous photo-Fenton catalyzed by biogenic Fe-Mn oxides. Chem. Eng. J. 2020, 380, 122427. [Google Scholar] [CrossRef]
  113. Raizada, P.; Kumari, J.; Shandilya, P.; Dhiman, R.; Singh, V.P.; Singh, P. Magnetically retrievable Bi2WO6/Fe3O4 immobilized on graphene sand composite for investigation of photocatalytic mineralization of oxytetracycline and ampicillin. Process Saf. Environ. Prot. 2017, 106, 104–116. [Google Scholar] [CrossRef]
  114. Wang, X.; Wang, A.; Lu, M.; Ma, J. Synthesis of magnetically recoverable Fe0/graphene-TiO2 nanowires composite for both reduction and photocatalytic oxidation of metronidazole. Chem. Eng. J. 2018, 337, 372–384. [Google Scholar] [CrossRef]
  115. Alani, O.A.; Ari, H.A.; Alani, S.O.; Offiong, N.A.O.; Feng, W. Visible-light-driven bio-templated magnetic copper oxide composite for heterogeneous photo-fenton degradation of tetracycline. Water (Switz.) 2021, 13, 1918. [Google Scholar] [CrossRef]
  116. Santos, M.M.d.; da Silva, T.D.; de Lucena, A.L.A.; Napoleão, D.C.; Duarte, M.M.M.B. Degradation of Ketoprofen, Tenoxicam, and Meloxicam Drugs by Photo-Assisted Peroxidation and Photo-Fenton Processes: Identification of Intermediates and Toxicity Study. Water. Air. Soil Pollut. 2020, 231, 35. [Google Scholar] [CrossRef]
  117. Soriano-Molina, P.; Plaza-Bolaños, P.; Lorenzo, A.; Agüera, A.; García Sánchez, J.L.; Malato, S.; Sánchez Pérez, J.A. Assessment of solar raceway pond reactors for removal of contaminants of emerging concern by photo-Fenton at circumneutral pH from very different municipal wastewater effluents. Chem. Eng. J. 2019, 366, 141–149. [Google Scholar] [CrossRef]
  118. Soriano-Molina, P.; Sánchez, J.L.G.; Malato, S.; Plaza-Bolaños, P.; Agüera, A.; Pérez, J.A.S. On the design and operation of solar photo-Fenton open reactors for the removal of contaminants of emerging concern from WWTP effluents at neutral pH. Appl. Catal. B Environ. 2019, 256, 117801. [Google Scholar] [CrossRef]
  119. Sun, S.; Yao, H.; Fu, W.; Xue, S.; Zhang, W. Enhanced degradation of antibiotics by photo-fenton reactive membrane filtration. J. Hazard. Mater. 2019, 386, 121955. [Google Scholar] [CrossRef] [PubMed]
  120. Arzate, S.; Campos-Mañas, M.C.; Miralles-Cuevas, S.; Agüera, A.; Sánchez, J.L.G.; Pérez, J.A.S. Removal of contaminants of emerging concern by continuous flow solar photo-Fenton process at neutral pH in open reactors. J. Environ. Manag. 2020, 261, 110265. [Google Scholar] [CrossRef] [PubMed]
  121. Costa, E.P.; Roccamante, M.; Amorim, C.C.; Oller, I.; Pérez, J.A.S.; Malato, S. New trend on open solar photoreactors to treat micropollutants by photo-Fenton at circumneutral pH: Increasing optical pathway. Chem. Eng. J. 2020, 385, 123982. [Google Scholar] [CrossRef]
  122. Soriano-Molina, P.; Miralles-Cuevas, S.; García, B.E.; Plaza-Bolaños, P.; Pérez, J.A.S. Two strategies of solar photo-Fenton at neutral pH for the simultaneous disinfection and removal of contaminants of emerging concern. Comparative assessment in raceway pond reactors. Catal. Today 2019, 361, 17–23. [Google Scholar] [CrossRef]
  123. Mejri, A.; Soriano-Molina, P.; Miralles-Cuevas, S.; Pérez, J.A.S. Fe3+-NTA as iron source for solar photo-Fenton at neutral pH in raceway pond reactors. Sci. Total Environ. 2020, 736, 139617. [Google Scholar] [CrossRef]
  124. Dong, W.; Jin, Y.; Zhou, K.; Sun, S.P.; Li, Y.; Chen, X.D. Efficient degradation of pharmaceutical micropollutants in water and wastewater by FeIII-NTA-catalyzed neutral photo-Fenton process. Sci. Total Environ. 2019, 688, 513–520. [Google Scholar] [CrossRef]
  125. Gonçalves, N.P.F.; Minella, M.; Fabbri, D.; Calza, P.; Malitesta, C.; Mazzotta, E.; Prevot, A.B. Humic acid coated magnetic particles as highly efficient heterogeneous photo-Fenton materials for wastewater treatments. Chem. Eng. J. 2020, 390, 124619. [Google Scholar] [CrossRef]
  126. Fiorentino, A.; Esteban, B.; Garrido-Cardenas, J.A.; Kowalska, K.; Rizzo, L.; Aguera, A.; Pérez, J.A.S. Effect of solar photo-Fenton process in raceway pond reactors at neutral pH on antibiotic resistance determinants in secondary treated urban wastewater. J. Hazard. Mater. 2019, 378, 120737. [Google Scholar] [CrossRef] [PubMed]
  127. Wu, P.; Zhou, C.; Li, Y.; Zhang, M.; Tao, P.; Liu, Q.; Cui, W. Flower-like FeOOH hybridized with carbon quantum dots for efficient photo-Fenton degradation of organic pollutants. Appl. Surf. Sci. 2020, 540, 148362. [Google Scholar] [CrossRef]
  128. Yu, X.; Yin, H.; Ye, J.S.; Peng, H.; Lu, G.; Dang, Z. Degradation of tris-(2-chloroisopropyl) phosphate via UV/TiO2 photocatalysis: Kinetic, pathway, and security risk assessment of degradation intermediates using proteomic analyses. Chem. Eng. J. 2019, 374, 263–273. [Google Scholar] [CrossRef]
  129. Zelinski, D.W.; Santos, T.P.M.d.; Takashina, T.A.; Leifeld, V.; Igarashi-Mafra, L. Photocatalytic Degradation of Emerging Contaminants: Artificial Sweeteners. Water. Air. Soil Pollut. 2018, 229, 207. [Google Scholar] [CrossRef]
  130. Ikhlef-Taguelmimt, T.; Hamiche, A.; Yahiaoui, I.; Bendellali, T.; Lebik-Elhadi, H.; Ait-Amar, H.; Aissani-Benissad, F. Tetracycline hydrochloride degradation by heterogeneous photocatalysis using TiO2(P25) immobilized in biopolymer (Chitosan) under UV irradiation. Water Sci. Technol. 2020, 82, 1570–1578. [Google Scholar] [CrossRef]
  131. Aissani, T.; Yahiaoui, I.; Boudrahem, F.; Chikh, S.A.; Aissani-Benissad, F.; Amrane, A. The combination of photocatalysis process (UV/TiO2(P25) and UV/ZnO) with activated sludge culture for the degradation of sulfamethazine. Sep. Sci. Technol. 2018, 53, 1423–1433. [Google Scholar] [CrossRef]
  132. Moreira, N.F.F.; Sampaio, M.J.; Ribeiro, A.R.; Silva, C.G.; Faria, J.L.; Silva, A.M.T. Metal-free g-C3N4 photocatalysis of organic micropollutants in urban wastewater under visible light. Appl. Catal. B Environ. 2019, 248, 184–192. [Google Scholar] [CrossRef]
  133. Wei, X.N.; Wang, H.L.; Wang, X.K.; Jiang, W.F. Facile fabrication of mesoporous g-C 3 N 4/TiO 2 photocatalyst for efficient degradation of DNBP under visible light irradiation. Appl. Surf. Sci. 2017, 426, 1271–1280. [Google Scholar] [CrossRef]
  134. Lin, L.; Wang, H.; Xu, P. Immobilized TiO2-reduced graphene oxide nanocomposites on optical fibers as high performance photocatalysts for degradation of pharmaceuticals. Chem. Eng. J. 2017, 310, 389–398. [Google Scholar] [CrossRef] [Green Version]
  135. Davididou, K.; McRitchie, C.; Antonopoulou, M.; Konstantinou, I.; Chatzisymeon, E. Photocatalytic degradation of saccharin under UV-LED and blacklight irradiation. J. Chem. Technol. Biotechnol. 2018, 93, 269–276. [Google Scholar] [CrossRef] [Green Version]
  136. Chauhan, P.S.; Kant, R.; Rai, A.; Gupta, A.; Bhattacharya, S. Facile synthesis of ZnO/GO nanoflowers over Si substrate for improved photocatalytic decolorization of MB dye and industrial wastewater under solar irradiation. Mater. Sci. Semicond. Process. 2019, 89, 6–17. [Google Scholar] [CrossRef]
  137. Shandilya, P.; Mittal, D.; Soni, M.; Raizada, P.; Hosseini-Bandegharaei, A.; Saini, A.S.; Singh, P. Fabrication of fluorine doped graphene and SmVO4 based dispersed and adsorptive photocatalyst for abatement of phenolic compounds from water and bacterial disinfection. J. Clean. Prod. 2018, 203, 386–399. [Google Scholar] [CrossRef]
  138. Shao, H.; Zhao, X.; Wang, Y.; Mao, R.; Wang, Y.; Qiao, M.; Zhao, Y.; Zhu, Y. Synergetic activation of peroxymonosulfate by Co3O4 modified g-C3N4 for enhanced degradation of diclofenac sodium under visible light irradiation. Appl. Catal. B Environ. 2017, 218, 810–818. [Google Scholar] [CrossRef]
  139. Anwer, H.; Park, J.W. Synthesis and characterization of a heterojunction rGO/ZrO2/Ag3PO4 nanocomposite for degradation of organic contaminants. J. Hazard. Mater. 2018, 358, 416–426. [Google Scholar] [CrossRef]
  140. Kumar, A.; Khan, M.; Zeng, X.; Lo, I.M.C. Development of g-C3N4/TiO2/Fe3O4@SiO2 heterojunction via sol-gel route: A magnetically recyclable direct contact Z-scheme nanophotocatalyst for enhanced photocatalytic removal of ibuprofen from real sewage effluent under visible light. Chem. Eng. J. 2018, 353, 645–656. [Google Scholar] [CrossRef]
  141. Xu, X.; Liu, S.; Cui, Y.; Wang, X.; Smith, K.; Wang, Y. Solar-Driven Removal of 1,4-Dioxane Using WO3/nγ-Al2O3 Nano-catalyst in Water. Catalysts 2019, 9, 389. [Google Scholar] [CrossRef] [Green Version]
  142. Cristino, V.; Longobucco, G.; Marchetti, N.; Caramori, S.; Bignozzi, C.A.; Martucci, A.; Molinari, A.; Boaretto, R.; Stevanin, C.; Argazzi, R.; et al. Photoelectrochemical degradation of pharmaceuticals at β25 modified WO3 interfaces. Catal. Today 2018, 340, 302–310. [Google Scholar] [CrossRef]
  143. Ghenaatgar, A.; Tehrani, R.M.A.; Khadir, A. Photocatalytic degradation and mineralization of dexamethasone using WO3 and ZrO2 nanoparticles: Optimization of operational parameters and kinetic studies. J. Water Process Eng. 2019, 32, 100969. [Google Scholar] [CrossRef]
  144. Alvarez-Corena, J.R.; Bergendahl, J.A.; Hart, F.L. Advanced oxidation of five contaminants in water by UV/TiO2: Reaction kinetics and byproducts identification. J. Environ. Manag. 2016, 181, 544–551. [Google Scholar] [CrossRef]
  145. Juang, R.S.; Yei, Y.C.; Liao, C.S.; Lin, K.S.; Lu, H.S.; Wang, S.F.; Sun, A.C. Synthesis of magnetic Fe3O4/activated carbon nanocomposites with high surface area as recoverable adsorbents. J. Taiwan Inst. Chem. Eng. 2018, 90, 51–60. [Google Scholar] [CrossRef]
  146. Rasheed, T.; Bilal, M.; Nabeel, F.; Adeel, M.; Iqbal, M.N. Environmentally-related contaminants of high concern: Potential sources and analytical modalities for detection, quantification, and treatment. Environ. Int. 2018, 122, 52–66. [Google Scholar] [CrossRef] [PubMed]
  147. Chon, K.; Salhi, E.; von Gunten, U. Combination of UV absorbance and electron donating capacity to assess degradation of micropollutants and formation of bromate during ozonation of wastewater effluents. Water Res. 2015, 81, 388–397. [Google Scholar] [CrossRef]
  148. Pisarenko, A.N.; Stanford, B.D.; Yan, D.; Gerrity, D.; Snyder, S.A. Effects of ozone and ozone/peroxide on trace organic contaminants and NDMA in drinking water and water reuse applications. Water Res. 2012, 46, 316–326. [Google Scholar] [CrossRef] [PubMed]
  149. Lee, Y.; von Gunten, U. Advances in predicting organic contaminant abatement during ozonation of municipal wastewater effluent: Reaction kinetics, transformation products, and changes of biological effects. Environ. Sci. Water Res. Technol. 2016, 2, 421–442. [Google Scholar] [CrossRef]
  150. Zhang, Z.; Zeng, K.; Liu, J. Immunochemical detection of emerging organic contaminants in environmental waters. TrAC Trends Anal. Chem. 2017, 87, 49–57. [Google Scholar] [CrossRef]
  151. Sgroi, M.; Anumol, T.; Roccaro, P.; Vagliasindi, F.G.A.; Snyder, S.A. Modeling emerging contaminants breakthrough in packed bed adsorption columns by UV absorbance and fluorescing components of dissolved organic matter. Water Res. 2018, 145, 667–677. [Google Scholar] [CrossRef]
  152. Sierra, M.M.D.; Giovanela, M.; Parlanti, E.; Soriano-Sierra, E.J. Fluorescence fingerprint of fulvic and humic acids from varied origins as viewed by single-scan and excitation/emission matrix techniques. Chemosphere 2005, 58, 715–733. [Google Scholar] [CrossRef]
  153. Yu, H.W.; Park, M.; Wu, S.; Lopez, I.J.; Ji, W.; Scheideler, J.; Synder, S.A. Strategies for selecting indicator compounds to assess attenuation of emerging contaminants during UV advanced oxidation processes. Water Res. 2019, 166, 115030. [Google Scholar] [CrossRef]
  154. Quintelas, C.; Mesquita, D.P.; Ferreira, E.C.; Amaral, A.L. Quantification of pharmaceutical compounds in wastewater samples by near infrared spectroscopy (NIR). Talanta 2018, 194, 507–513. [Google Scholar] [CrossRef] [Green Version]
  155. Wang, T.; Wang, S.; Cheng, Z.; Wei, J.; Yang, L.; Zhong, Z.; Hu, H.; Wang, Y.; Zhou, B.; Li, P. Emerging core–shell nanostructures for surface-enhanced Raman scattering (SERS) detection of pesticide residues. Chem. Eng. J. 2021, 424, 130323. [Google Scholar] [CrossRef]
  156. Fang, Q.; Li, Y.; Miao, X.; Zhang, Y.; Yan, J.; Yu, T.; Liu, J. Sensitive detection of antibiotics using aptamer conformation cooperated enzyme-assisted SERS technology. Analyst 2019, 144, 3649–3658. [Google Scholar] [CrossRef]
  157. Cennamo, N.; D’Agostino, G.; Sequira, F.; Mattiello, F.; Porto, G.; Biasiolo, A.; Nogueira, R.; Bilro, L.; Zeni, L. A simple and low-cost optical fiber intensity-based configuration for perfluorinated compounds in water solution. Sensors (Switz.) 2018, 18, 3009. [Google Scholar] [CrossRef] [Green Version]
  158. Cennamo, N.; D’Agostino, G.; Porto, G.; Biasiolo, A.; Perri, C.; Arcadio, F.; Zeni, L. A molecularly imprinted polymer on a plasmonic plastic optical fiber to detect perfluorinated compounds in water. Sensors (Switz.) 2018, 18, 1836. [Google Scholar] [CrossRef] [Green Version]
  159. Tagg, A.S.; Sapp, M.; Harrison, J.P.; Ojeda, J.J. Identification and Quantification of Microplastics in Wastewater Using Focal Plane Array-Based Reflectance Micro-FT-IR Imaging. Anal. Chem. 2015, 87, 6032–6040. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  160. Li, W.T.; Jin, J.; Li, Q.; Wu, C.F.; Lu, H.; Zhou, Q.; Li, A.M. Developing LED UV fluorescence sensors for online monitoring DOM and predicting DBPs formation potential during water treatment. Water Res. 2016, 93, 1–9. [Google Scholar] [CrossRef]
  161. Jaria, G.; Calisto, V.; Otero, M.; Esteves, V.I. Monitoring pharmaceuticals in the aquatic environment using enzyme-linked immunosorbent assay (ELISA)—A practical overview. Anal. Bioanal. Chem. 2020, 412, 3983–4008. [Google Scholar] [CrossRef]
  162. Vacher, M.; Fdez. Galván, I.; Ding, B.W.; Schramm, S.; Berraud-Pache, R.; Naumov, P.; Ferre, N.; Liu, Y.J.; Navizet, I.; Roca-Sanjuan, D.; et al. Chemi- and Bioluminescence of Cyclic Peroxides. Chem. Rev. 2018, 118, 6927–6974. [Google Scholar] [CrossRef]
  163. Lodeiro, P.; Achterberg, E.P.; El-Shahawi, M.S. Detection of silver nanoparticles in seawater at ppb levels using UV–visible spectrophotometry with long path cells. Talanta 2017, 164, 257–260. [Google Scholar] [CrossRef] [Green Version]
  164. Cai, Q.; Lee, B.C.Y.; Ong, S.L.; Hu, J. Application of a Multiobjective Artificial Neural Network (ANN) in Industrial Reverse Osmosis Concentrate Treatment with a Fluidized Bed Fenton Process: Performance Prediction and Process Optimization. ACS ES&T Water 2021, 1, 847–858. [Google Scholar] [CrossRef]
  165. Shen, W.; Huang, F.; Zhang, X.; Zhu, Y.; Chen, X.; Akbarjon, N. On-line chemical oxygen demand estimation models for. Water Sci. Technol. 2018, 78, 310–319. [Google Scholar] [CrossRef] [PubMed]
  166. Bolton, J.R.; Bircher, K.G.; Tumas, W.; Tolman, C.A. Figures-of-merit for the technical development and application of advanced oxidation technologies for both electric- and solar-driven systems. Pure Appl. Chem. 2001, 73, 627–637. [Google Scholar] [CrossRef]
  167. Jankunaite, D.; Tichonovas, M. Removal of Diclofenac, Ketoprofen, and Carbamazepine from Simulated Drinking Water by Advanced Oxidation in a Model Reactor. Water Air Soil Pollut. 2017, 228, 353. [Google Scholar] [CrossRef]
  168. Guo, K.; Wu, Z.; Yan, S.; Yao, B.; Song, W.; Hua, Z.; Zhang, X.; Kong, X.; Li, X.; Fang, J. Comparison of the UV/chlorine and UV/H2O2 processes in the degradation of PPCPs in simulated drinking water and wastewater: Kinetics, radical mechanism and energy requirements. Water Res. 2018, 147, 184–194. [Google Scholar] [CrossRef] [PubMed]
  169. Wardenier, N.; Liu, Z.; Nikiforov, A.; van Hulle, S.W.H.; Leys, C. Micropollutant elimination by O3, UV and plasma-based AOPs: An evaluation of treatment and energy costs. Chemosphere 2019, 234, 715–724. [Google Scholar] [CrossRef]
  170. Sgroi, M.; Snyder, S.A.; Roccaro, P. Comparison of AOPs at pilot scale: Energy costs for micro-pollutants oxidation, disinfection by-products formation and pathogens inactivation. Chemosphere 2020, 273, 128527. [Google Scholar] [CrossRef]
  171. Expósito, A.J.; Patterson, D.A.; Monteagudo, J.M.; Durán, A. Sono-photo-degradation of carbamazepine in a thin falling film reactor: Operation costs in pilot plant. Ultrason. Sonochem. 2017, 34, 496–503. [Google Scholar] [CrossRef] [Green Version]
  172. Miklos, D.B.; Remy, C.; Jekel, M.; Linden, K.G.; Drewes, J.E.; Hübner, U. Evaluation of advanced oxidation processes for water and wastewater treatment—A critical review. Water Res. 2018, 139, 118–131. [Google Scholar] [CrossRef] [PubMed]
  173. Ahn, Y.; Lee, D.; Kwon, M.; Choi, I.h.; Nam, S.N.; Kang, J.W. Characteristics and fate of natural organic matter during UV oxidation processes. Chemosphere 2017, 184, 960–968. [Google Scholar] [CrossRef]
  174. Tang, W.Z.; An, H. Photocatalytic degradation kinetics and mechanism of acid blue 40 by TiO2/UV in aqueous solution. Chemosphere 1995, 31, 4171–4183. [Google Scholar] [CrossRef]
  175. Scaria, J.; Gopinath, A.; Nidheesh, P.V. A versatile strategy to eliminate emerging contaminants from the aqueous environment: Heterogeneous Fenton process. J. Clean. Prod. 2020, 278, 124014. [Google Scholar] [CrossRef]
  176. Ortega-Liébana, M.C.; Sánchez-López, E.; Hidalgo-Carrillo, J.; Marinas, A.; Marinas, J.M.; Urbano, F.J. A comparative study of photocatalytic degradation of 3-chloropyridine under UV and solar light by homogeneous (photo-Fenton) and heterogeneous (TiO 2) photocatalysis. Appl. Catal. B Environ. 2012, 127, 316–322. [Google Scholar] [CrossRef]
Figure 1. Overview of the treatment and management of CECs.
Figure 1. Overview of the treatment and management of CECs.
Water 13 02340 g001
Figure 2. Graphical illustration of the reaction mechanisms for various light-driven processes: UV/oxidant (a), UV/ozone and photo-Fenton (homogenous) (b), photo-Fenton (heterogenous) (c), photocatalyst (d).
Figure 2. Graphical illustration of the reaction mechanisms for various light-driven processes: UV/oxidant (a), UV/ozone and photo-Fenton (homogenous) (b), photo-Fenton (heterogenous) (c), photocatalyst (d).
Water 13 02340 g002
Figure 3. Average EEO values of reviewed light-driven AOPs (note: A range of EEO values were shown in the figure as error bars, while the bar graphs showed the average EEO values).
Figure 3. Average EEO values of reviewed light-driven AOPs (note: A range of EEO values were shown in the figure as error bars, while the bar graphs showed the average EEO values).
Water 13 02340 g003
Table 1. Global occurrence of CECs and their use in the industry.
Table 1. Global occurrence of CECs and their use in the industry.
Category of ContaminantChemical Structure within the ContaminantName of ContaminantUses in IndustryConcentration DetectedReferences
PharmaceuticalsAzetidine, BenzeneAmoxicillinAntibioticsQueensland: 6.9 µg/L
Delhi: Up to 172.6 ng/L
Ghana: Up to 0.0027 ng/L
[8,9,10]
Halogenic-BenzeneDiclofenacAnti-inflammatory drugAlgiers: 85.2 ± 9.3 ng/L
Saudi Arabian coastal waters: 10,221 ng/L
Lahore: 260–470 ng/L
WWTP effleunt from South Africa: 5.56–243.6 ng/L
[11,12,13]
BenzeneIbuprofenPainkillerMadrid: 4.1 ng/L
Algiers: 372.8 ± 19.8 ng/L
Saudi Arabian coastal waters: 127–660 ng/L
Lahore: 1728–2300 ng/L
Sea water Durban, South
Africa: <0.17 ng/L
[11,12,13,14,15]
Benzene, PiperidineAcetaminophen (ACE)PainkillerSaudi Arabian coastal waters: 1234 and 2346 ng/L
Lahore: 12,120–13,880 ng/L
[11,12]
Benzene, PyrazineSulfamethoxazole (SMX)AntibioticsMekong Delta: 21 ng/L
Jiangsu Province: 63.6 ng/L
Madrid: 162–530 ng/L
Ghana: 0.013–2.861 ng/L
[10,14,16,17]
Benzene, 7-member ringCarbamazepineAnticonvulsantVarious plants in the USA: 2–207 ng/L
Spain treatment plants: <54 ng/L
Hartbeespoort Dam catchment
and the uMngeni River estuary: up to 94 ng/L
[18,19,20]
Food additiveOxadiazineAcesulfame potassium (ACE-K):Artificial sweetener in food and beveragesJiangsu: 2.9 μg/L to 0.20 mg/L
German Elbe river: 100 to 900 mg/s (mass load)
[21,22]
BenzothiazoleSucraloseJiangsu: up to 3.6 μg/L.[21]
PesticidesTriazineAtrazineHerbicideJiaozhou Bay: 76 ng/L
Ctalamochita river basin: 0.23 to 0.26 ng/L (urban), 0.28 to 3 ng/L (rural)
Hartbeespoort Dam catchment (South Africa): up to 1570 ng/L
[23,24,25]
Industrial chemicalsDioxane1,4-DioxaneOrganic solventsSant Joan Despí: 4360 (average), 32,370 (max)
Oder River:143–2245 ng/L
Rhine/Main River:110–850 ng/L
[26,27]
Pyrazole, 6 membered heterocyclic ringCaffeineFood and beverage industrySaudi Arabian coastal waters: 7708 ng/L
Various plants in the USA: 7–687 ng/L
Madrid: 5010–65 625 ng/L
WWTP effluent from South Africa: 85.76–4878 ng/L
[12,14,18]
-Perfluorooctane sulfonate (PFOS) and Perfluorooctanoic acid (PFOA)Industrial manufacturing and use and disposal of PFAS-containing products,Worldwide: 0.2–1630.2 ng/L
Singapore: 532–1060 ng/L (WWTP treated effluent)
WWTP effluent from Kampala, Uganda: PFOS (1.3–1.5 ng/L) and PFOA (1.5–2.4 ng/L)
[28,29,30]
-N-Nitroso-dimethylamine (NDMA)A by-product of industrial processes that use nitrates and/or nitrites.Various plants in the USA: 12–321 ng/L[18]
PhenolsBisphenol A (BPA)Plastic formationYamuna/Cooum River: 1420–14,800 ng/L
Zhujiang/Dongjiang River: 101–2310 ng/L
Zhujiang/Dongjiang WWTP: 29,400 ng/L
Riyadhm Saudi Arabia/Drinking water:
291–41,190 ng/L
[31,32,33]
Personal care products (PCPs)BenzeneDiethyltoluamide (DEET)Insect repellentArizona: 1570–15,200 ng/L[34]
Benzene, 5- member cycloalkaneGalaxolideSynthetic muskMadrid: <24 971 ng/L
Lubbock: 3789–10,525 ng/L
[14,35]
Disinfection by-products (DBPs)Dihalobenzoquinones2,6-dichloro-1,4-benzoquinoneDisinfection by-product from water treatmentCanada WWTP: 165.1 ng/L
China Drinking WTP: 2.6–19.70 ng/L
[36,37]
IodotrihalomethanesDibromoiodomethane (DBIM), Chlorodiiodomethane (CDIM),
Bromodiiodomethane (BDIM),
Iodoform (TIM)
Disinfection by-product from water treatmentChina Drinking WTPs: 0.007–0.23 ng/L
Australia Advanced water recycling plant: <1–7 ng/L
[38,39]
Table 2. Mechanism of conventional light-driven AOPs.
Table 2. Mechanism of conventional light-driven AOPs.
Name of AOPTypeMechanismReferences
UV/OxidantUV-H2O2 H 2 O 2 + hv 2 HO [51]
UV-persulfate S 2 O 2 2 + hv 2 SO 4
SO 4 + H 2 O SO 4 2 + HO + H +
SO 4 + OH SO 4 2 + HO
[52]
UV-chlorine HOCl H + + OCl (pKa = 7.5 at 25 °C)
HOCl + hv HO + Cl
OCl + hv O + Cl
O + H 2 O HO + OH
Cl + OH ClHO
[53]
UV/OzoneMicrobubble O 3 + hv O 2 + O ( 1 D )
O ( 1 D ) +   H 2 O H 2 O 2
H 2 O 2 + hv 2 HO
3 O 3 + hv +   H 2 O   2 HO + 4 O 2
[54]
Photo-FentonHomogenous H 2 O 2 + Fe 2 + Fe 3 + + HO + OH
H 2 O 2 + Fe 3 + Fe 2 + + HO 2 + H +
hv + Fe 3 + Fe 2 + + HO Fe 3 + + HO 2 Fe 2 + + O 2 + H +
[55]
Heterogenous Fe 2 + + H 2 O 2 Fe 3 + + OH + HO
Fe 3 + + H 2 O 2 Fe OOH 2 + + H +
Fe OOH 2 + Fe 2 + +   HO 2
Fe 2 + + hv Fe 2 + + HO
Fe 3 + L + hv Fe 3 + L * Fe 2 + + L
[55,56]
PhotocatalysisTiO2 TiO 2 + hv e cb + h vb +
h vb + + OH HO
h vb + + H 2 O HO + H +
e cb + h vb + heat
[57,58]
ZnO ZnO + hv e cb + h vb +
e cb +   O 2 O 2 h vb + + OH HO .
[59]
Table 3. Critical factors that affect light-driven processes.
Table 3. Critical factors that affect light-driven processes.
Factors that Affect the ProcessEffects on ProcessReferences
Low UV-absorbanceUV/Ozone:
-
Reduces UV dose to generate OH radicals
-
Works well only with UVC rays, due to the highest molar adsorption coefficient
Photocatalysis:
-
Affects UV dose for degradation
Photo-Fenton:
-
Reduces UV dose to generate OH radicals
Photocatalysis:
-
Affects the charge carrier generation for photocatalytic activity and is only effective in selected wavelengths
[65,69]
Low pHUV/Ozone:
-
Ozone degradation rate
-
Stability of pollutant
UV/Oxidant:
-
Stability of peroxide ions
-
Stability of ClO
Photo-Fenton:
-
Stability of Fe2+ ions and peroxide ions
-
Leaching of iron species in low pH
Photocatalysis:
-
Leaching of metal species in low pH
-
Attraction between catalyst and pollutants
[63,64,65]
Water matrix: Presence of natural organic matter (NOM)UV/Ozone:
-
Adsorption of UV
-
Scavenge OH radicals
UV/Oxidant:
-
UV-absorbing humic matter fraction easily degraded but non-absorbing low-molecular-weight (LMW) fractions get concentrated
Photo-Fenton:
-
Ligand-to-metal charge transfer (LMCT) effect inhibiting Fenton reaction
Photocatalysis:
-
Higher molecular weight fractions adsorbed onto catalysts and acted as electron-hole scavengers to reduce the photocatalytic degradation rate
-
Scavenge h+
-
Occluding radicals generation sites
[60,62,70,71,72,73]
Water matrix: Presence of O2Photocatalysis:
-
Acts as a scavenger for e- to stop the recombination with h+
Photo-Fenton:
-
Oxygen competed against hydroxyl radical for degradation of organics
[59,74,75,76,77]
Excess oxidant/catalyst dosageUV/Ozone:
-
Increased O3 dose, increase degradation rates, the excess might have scavenging effects
Photo-Fenton:
-
Scavenge OH radicals if H2O2 is in excess
-
H2O2 needed to generate OH radicals
-
Reduce light penetration if added in excess
UV/Oxidant:
-
Self-scavenge, reducing degradation rates
Photocatalysis:
-
Reduce light penetration if added in excess
[59,78,79,80]
Type of irradiationof light usedPhoto-Fenton:
-
Solar/Fenton can achieve the same efficiency as UV/Fenton but at slower degradation rates
Photocatalysis:
-
Doping of catalyst allows the bandgap to be reduced, increasing the capacity to utilize both visible light and UV
[66,67,68]
Table 4. Current application of UV/oxidant processes.
Table 4. Current application of UV/oxidant processes.
CategoryWastewater/
Compounds
Operation ConditionRemoval EfficiencyReferences
UV/peroxideWastewater treatment plant effluent
  • UV/H2O2
  • Low-pressure UV lamp
  • UV wavelength: 254 nm (UVC)
  • Power output: 2 mW/cm2
  • Chemical dose: 100 mg/L (H2O2)
  • HRT: 60 min
The initial concentration of contaminant: ~400 ng/L
DOC: 70%
DON: 20–30%
NDMA: ~80%
[81]
Wastewater treatment plant effluent
  • UV/H2O2
  • Medium-pressure UV lamp
  • UV wavelength: 254 nm (UVC)
  • Power output: 3 kW
  • Chemical dose: 10 mg/L (H2O2)
  • HRT: up to 60 min
Initial concentration of contaminant: 150–300 ng/L and 10–20 μg/L of bisphenol A (BPA).
perfluorooctanoic acid (PFOA): <30%
perfluorooctane sulfonate (PFOS): <30%
N-nitrosodimethylamine (NDMA): ~60%
Diclofenac: ~90%
(BPA): ~60%
2-Methylisoborneol (MIB): ~80%
Geosmin: ~90%
17 beta-estradiol (E2): ~70%
[82]
Wastewater treatment plant effluent doped CECS
  • UV/H2O2
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: (UVC)
  • Chemical dose: 100 mg/L
  • Power: 5.3 W
  • UV fluence: 55.2 mW/cm2
  • HRT: 80 min
Initial concentration of contaminant: 1 μM
Nonylphenol ethoxylated: 99% removal
[83]
Synthetic wastewater, simulating pharmaceuticals discharges
  • UV/H2O2
  • Low-pressure UV lamp, UV-LED
  • UV wavelength: 254 nm (UVC)
  • Power output: 15 W
  • Chemical dose: 1 mM (H2O2)
  • HRT: 120 min
  • UV Fluence: 0.1 mW/cm2 (UV lamp) 0.13 mW/cm2 (UV-LED)
Initial concentration of contaminant: 1 μM
Triclosan: ~80% (both systems) Diclofenac: ~80% (both systems)
[84]
RO treatment plant effluent
  • UV/H2O2
  • Low-pressure UV lamp (LP-UV) and UV-LED
  • UV wavelength: 254 nm (UVC)
  • Power output: 15 W
  • Chemical dose: 10 mg/L (H2O2)
  • HRT: 0.44 h
  • UV fluence rate: 0.1 mW/cm2 (LP-UV) 0.03 mW/cm2 (UV-LED)
  • pH: buffered at 7.4
Initial concentration of contaminant: 1 μM
Ibuprofen (IBP): ~80%
Triclosan (TCS): ~80%
Estrone (E1): ~80%
Diclofenac: ~80% (mostly contributed by UV)
Bisphenol A (BPA): ~60%
[85]
Sulfolane containing wastewater
  • UV/H2O2
  • UV wavelength: NA
  • Chemical dose: 40–70 mg/L [H2O2]
  • UV Power: 2140 W
  • HRT: 10–30 min
  • pH: 7.75–9.07
Initial concentration of contaminant: 200 µg/L
Sulfolane removal: 36–89%
[86]
UV/peroxide and UV/chlorineWastewater treatment plant effluent
  • UV/H2O2 and UV/Cl
  • Medium-pressure UV lamp
  • UV wavelength: 200–400 nm (UVA, UVB, UVC)
  • Power output:1 kW
  • Energy consumption: 0.4 kW, 1 m3/h
  • Chemical dose: 6 mg/L (H2O2, Cl2)
4t-octylphenol: 65% (UV/Cl), 0% (UV/H2O2)
bisphenol A (BPA), 4-nonylphenols, TCPP: ~90% (UV/C, UV/H2O2)
Diclofenac: ~90% (UV/C, UV/H2O2)
DEET, HHCB: ~40% (UV/C, UV/H2O2)
[87]
Wastewater treatment plant effluent
  • UV/H2O2, Solar/H2O2, UV/Cl
  • Medium-pressure UV lamp
  • UV wavelength: 254 nm (UVC)
  • Power output: 230 W
  • sup>∙ Energy consumption: 87.7 W/m2
  • Chemical dose: 10 mg/L (Cl), 10 mg/L (H2O2)
  • HRT: 60 min
Initial concentration of contaminant: 1:1 dilution with real wastewater 200 μg/L
Sulfamethoxazole: ~90% ((UV/Cl):3 min, (UV/H2O2): 6 min)
Imidacloprid: ~90% (UV/Cl):8.5 min, (UV/H2O2): 12 min
Carbamazepine: ~90% (UV/Cl): 6 min, (UV/H2O2): 25 min
Diclofenac: ~90% ((UV/Cl): 3 min, (UV/H2O2): 6 min
Blended CEC matrix: 82% (UV/Cl) and 87% (UV/H2O2) (60 min)
[88]
UV/persulfate and UV/peroxideSynthetic wastewater, simulating pharmaceuticals discharges
  • sup>∙ UV/H2O2 and UV/S2O82−
  • Low-pressure UV lamp (LP-UV) and UV-LED
  • UV wavelength: 254 nm (UVC- using a UV filter)
  • Power output: 280 W
  • Chemical dose: Molar ratio of chemical dose: compound = 20:1
  • HRT: 0.44 h
  • pH: buffered at 7.4
Initial concentration of contaminant: 2.2, 3.0, 5.2, and 5.5 µM (LP, FRSM, CAF, and CBZ)
losartan potassium (LP): 85% ((UV/S2O82−): 290 mJ cm−2 (UV/H2O2): 620 mJ cm−2)
furosemide (FRSM): 85% ((UV/S2O82−): 290 mJ cm−2 (UV/H2O2): 620 mJ cm−2)
caffeine (CAF): 85% ((UV/S2O82−): 290 mJ cm−2 (UV/H2O2): 620 mJ cm−2)
carbendazim (CBZ): 85% ((UV/S2O82−): 290 mJ cm−2 (UV/H2O2): 620 mJ cm−2)
[89]
Wastewater treatment plant effluent spiked with synthetic compounds
  • UV/S2O82− and UV/HSO5
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 254 nm (UVC- using a UV filter)
  • Mean intensity: 4.17 mW/cm2
  • Power output: 9 W
  • Chemical dose: 1.0 mM
  • HRT: 30 min
  • pH: 7.8 ± 0.2
Initial concentration of contaminant: 500 µg/L
Carbamazepine (CBZ): >90%
Crotamiton (CRMT): >90%
N,N-diethyl-meta-toluamide (DEET): >90% (80% UV/S2O82−)
Gemfibrozil (GEM): >90%
Ibuprofen (IBP): >90%
Trimethoprim (TMP): >90%
TOC removal: 31.8% (UV/HSO5) and 33.7% (UV/S2O82−)
[90]
UV/persulfateSynthetic wastewater, simulating pharmaceuticals discharges
  • UV/P
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 254 (UVC)
  • Chemical dose: 1 mM PS
  • Power: 0.44 μE/s
  • HRT: 90 min
  • pH: 6.5
Initial concentration of contaminant: 32.8 μM
Methyl paraben: 98.9% removal
[91]
Synthetic wastewater, simulating pharmaceuticals discharges
  • UV/P
  • Low-pressure lamps
  • UV wavelength: 254 (UVC)
  • Chemical dose: 0.25 mM PS
  • Power: 4.9 W
  • HRT: 100 min
  • pH: 6.07
Initial concentration of contaminant: 31 μM
Chloramphenicol: 100% removal
[92]
Synthetic wastewater, simulating pharmaceuticals discharges
  • UV/P
  • Low-pressure lamp
  • UV wavelength: 254 (UVC)
  • Chemical dose: 0.2 mM PS
  • Power: 10 W
  • HRT: 180 min
  • pH: 6
Initial concentration of contaminant: 2 μM
Haloacetonitriles: 99.8% removal
[93]
Synthetic wastewater, simulating pharmaceuticals discharges
  • UV/P
  • Medium-pressure lamp
  • UV wavelength: 200–300 (UVC)
  • Chemical dose: 1 mM PS
  • Power: 2.8 kW
  • pH: 5.85
Initial concentration of contaminant: 23.69 μM
Sulfamethoxazole: 97% removal
[94]
Synthetic wastewater, simulating pharmaceuticals discharges
  • UV/P
  • Low-pressure lamps
  • UV wavelength: 254 (UVC)
  • Chemical dose: 0.25 mM, PMS
  • Power: 15 W
  • HRT: 180
  • pH: 5.8
Initial concentration of contaminant: 3.43 μM
Lindane: ~90% removal
[95]
Wastewater treatment plant effluent and surface water spiked with synthetic compounds
  • UV/H2O2
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 254 nm (UVC)
  • Power output: 15 W
  • Chemical dose: 50 μM
  • UV fluence: 4.23 mW/cm2
  • HRT: 30 min
  • pH: natural pH of wastewater
Initial concentration of contaminant: 1 μM each
1H-benzotriazole (BZ): ~0% (secondary effluent), ~80% (surface water)
N,N-diethyl-m-toluamide (DEET): ~20% (secondary effluent), ~70% (surface water)
Chlorophene (CP): ~90% (secondary effluent), ~90% (surface water)
3-methylindole (ML): ~90% (secondary effluent), ~90% (surface water)
Nortriptyline hydrochloride (NH): ~90% (secondary effluent), ~90% (surface water)
[96]
GENx
  • UV/S2O82− and UV/sulfite
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 253.7 nm
  • Power output: 35 W
  • Chemical dose: 20 mM
  • UV fluence: 8.0 mW/cm2,
  • HRT: 180 min
  • pH: 10
Initial concentration of contaminant: 1 mg/L
Perfluorooctanoic acid (PFOA): <90% (UV/sulfite), ~10% (UV/S2O82−)
Hexafluoropropylene oxide dimer acid (GenX): <95% (UV/sulfite), ~35% (UV/S2O82−)
[97]
UV/nitrateWastewater treatment plant effluent doped with CECS
  • Solar/PAA UV/PAA
  • Low-pressure UV lamp (LP-UV) and sunlight
  • UV wavelength: 254 nm (UVC)
  • Chemical dose: 10 mg/L
  • Power: 230 W
  • UV fluence: 40 kJ/L
  • HRT: 30 min
Initial concentration of contaminant: 100 μg/L
Carbamazepine (CBZ): 30% (sunlight), ~70% (UVC)
Diclofenac (DCF): >90% (sunlight), >90% (UVC: 2 kJ L−1)
Sulfamethoxazole (SMX): >90% (sunlight), ~100% (with UVC alone)
[98]
Generic CECs
  • UV/NO2
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 365 nm
  • Chemical dose: 0.5 mg-N/L
  • UV fluence: 3.05 mW/cm2
  • HRT: 20 min
  • pH: 7
Initial concentration of contaminant: 2 μM
Bisphenol A (BPA): ~80%
Carbamazepine (CBZ): ~60%
[99]
Table 5. Current application of UV/Ozone processes.
Table 5. Current application of UV/Ozone processes.
Wastewater/
Compounds
Operation ConditionRemoval EfficiencyReferences
Synthetic wastewater
  • UV/O3 (benchtop)
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 254 nm (UVC)
  • Power: 125 W
  • UV intensity: 4.6 × 10−7 Einstein/Ls
  • Ozone dose: 14.7 mg/L
  • HRT: 52 min
  • pH: 9.2
Initial concentration of caffeine: 300 mg/L
Color: 99.1% removal
Caffeine degradation: 96.5% removal
[105]
Synthetic wastewater with sucralose
  • UV/O3 (benchtop)
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 254 nm (UVC)
  • UV intensity: 33.4 W/m2
  • Ozone dose: 19.4 mg/L
  • Ozone flow rate: 35 L/h
  • HRT: 120 min
  • pH: 7.0
Initial concentration of sucralose: 50 mg/L
Degradation: 100% (after 30 min)
Mineralization: 89.8% (after 2 h)
[103]
Real wastewater containing atrazine
  • UV/O3 (benchtop)
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 254 nm (UVC)
  • Power: 8 × 3.5 W
  • Ozone flow rate: 15 g/h
  • HRT: 180 min
  • pH: 12.0
Initial concentration of atrazine: 0.0232 mM
Initial COD: 0.447 M
Initial NH3 – N: 1.44 mM
Initial Cl-: 5.56 M
Atrazine degradation: 95% (after 180 min)
COD: 55% removal
NH3-N: 65% removal
[102]
Groundwater/surface water/secondary effluent spiked with CECs
  • UV/O3 (pilot-scale)
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 254 nm (UVC)
  • Specific ozone dose: 1.5 mg O3/mg DOC
  • Ozone flow rate: 0.2 L/min
  • HRT: 20 min
  • pH: 8.0–8.2
Initial concentration of micropollutants: ∼150 μg/L each
Groundwater: 76–~100% removal
Surface water: 84–100% removal
Secondary effluent: 89–97% removal
[106]
Synthetic wastewater, mixing waste firefighting foam
  • UV/O3 (benchtop)
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 254 nm (UVC)
  • UV intensity: 83 μW/cm2 (R1), 43 μW/cm2 (R2)
  • Ozone flow rate: 30 L/min
  • HRT: 20 min
  • pH: natural
  • UV/O3 (pilot-scale)
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 254 nm (UVC)
  • Ozone flow rate: 160 L/min
  • HRT: 20 min
  • pH: natural
Initial concentration of PFAS:
3–10 μg/L (benchtop)
3.15 μg/L (pilot plant)
Benchtop: 73% removal
Pilot plant: 73% removal
[107]
Synthetic wastewater with P-nitroaniline and coal washing plant wastewater
  • UV/O3 (benchtop)
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 254 nm (UVC)
  • Power: 15 V
  • UV intensity: 254 mW/m3
  • Ozone flow rate: 0.9 g/h
  • HRT: 40 min
  • pH: 9.0
Initial concentration of nitroaniline:
10–25 mg/L
PNA: 81% removal
TOC: 81% removal
[108]
Synthetic wastewater
  • VUV/O3 (benchtop)
  • Vacuum-UV lamp (VUV)
  • UV wavelength: 185 nm (VUV)
  • Power: 40 W
  • Ozone flow rate: 1.16 mg/min
  • HRT: 90 min
  • pH: 10.0
Initial concentration:
Diethyl dithiocarbonate collector = 100 mg/L
Degradation: 99.55% removal
TOC: 34% removal
[109]
Table 6. Current application of photo-Fenton processes.
Table 6. Current application of photo-Fenton processes.
CategoryWastewater/
Compounds
Operation ConditionRemoval EfficiencyReferences
HeterogenousPharmaceutical wastewater
  • UV/Fe
  • UV wavelength: 190–900 nm (UVC)
  • Catalyst dose: 1 g/L
  • Power: 600 W/m2
  • HRT: 60 min
  • pH: 3.5
Initial concentration of contaminant: 5 mg/L
ACT removal: ~90%
ACE removal: ~90%
[111]
Synthetic water containing ofloxacin (OFL)
  • UV/bio-FeMnOx
  • UV wavelength: <420 nm (sunlight/UVA)
  • Low power mercury lamp
  • Chemical dose: stoichiometric amount [H2O2], 5 mg/L [bio-FeMnOx]
  • UV power: 500 W
  • HRT: 90 min
Initial concentration of OFL: 30 mg/L
Ofloxacin (OFL) removal: ~90% (UV)
[112]
Synthetic wastewater containing tetracycline (TC)
  • UV/nZVI (benchtop)
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 254 nm (UVC)
  • Power: 18 W
  • Lamp intensity = 2500 mcW/cm2
  • Catalyst dose: 5 mg/L
  • HRT: 200 min
  • pH: 9.0
Initial concentration of TC: 10 mg/L
TC: 96.71% removal
[78]
Synthetic wastewater containing amoxicillin (AMX)
  • UV/Fe3O4/g-C3N4 (benchtop)
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 254 nm (UVC)
  • Power: 10 W
  • Lamp intensity = 3.5 mJ/cm2
  • Catalyst dose: 1 g/L
  • HRT: 200 min
  • pH: Natural
Initial concentration of AMX: 0.25 mMAMX: 89% removal
TOC: 60% removal
[61]
Synthetic wastewater containing oxytetracycline (OTC) and ampicillin (AMP)
  • UV/Fe3O4/Bi2WO6 (benchtop)
  • UV wavelength: Visible
  • Power: 1000 W
  • Light intensity = 35 × 103 ± 1000 lx
  • Catalyst dose: 0.5 g/L
  • HRT: 120 min (OTC), 60 min (AMP)
  • pH: 6.0
Initial concentration of OTC and AMP: 0.1 mM each
OTC: 73% removal (after 1 h)
COD: 62% removal (after 10 h)
AMP: 73% removal (after 1 h)
COD: 60% removal (after 10 h)
[113]
Synthetic wastewater containing metronidazole (MNZ)
  • UV/nZVI (benchtop)
  • High-pressure UV lamp (HP-UV)
  • UV wavelength: 365 nm (UVA)
  • Power: 160 W
  • Catalyst dose: 0.2 g/L
  • HRT: 80 min
  • pH: Natural
Initial concentration of metronidazole: 35 mg/L
MNZ: 71.98% removal
[114]
Synthetic wastewater containing tetracycline (TC)
  • UV/(Fe3O4/CuO/C) (benchtop)
  • High-pressure UV lamp (HP-UV)
  • UV wavelength: 420 nm (UVA)
  • Power: 100 W
  • Catalyst dose: 3 g/L
  • HRT: 60 min
  • pH: 7
Initial concentration of TC: 50 mg/L
TC: ~90% removal
[115]
HomogenousSynthetic wastewater containing an anti-inflammatory substance
  • UV/Fe
  • UV wavelength: 289 and 367 nm (UVC)
  • Chemical dose: 400 mg/L of [H2O2], 1.75 mg/L of [Fe]
  • HRT: 120 min
  • pH: 3–4
Initial concentration of contaminant: 10 mg/L
(Ketoprofen, tenoxicam, and meloxicam drugs)
COD removal: 98% removal
[116]
Real wastewater
  • UV/Fe-EDDS
  • UV wavelength: 327–384 nm (UVC)
  • Chemical dose: 0.88, 0.1 mM Fe3+
  • Power: 30 W m−2
  • HRT: 15 min
  • pH: 7–8
Initial concentration of contaminant: 5000–50,000 ng/L (total CECs)
COD removal: <80% removal
[117]
Real wastewater
  • UV/Fe-EDDS
  • UV wavelength: 327–384 nm (sunlight)
  • Chemical dose: 0.88 mM [H2O2] 0.1 mM [Fe3+-EDDS]
  • HRT: 30 min
  • pH: 7–8
Initial concentration of contaminant: na
Total EC removal: >80%
[118]
Pharmaceuticals wastewater
  • UV/Fe-oxalic + sonication
  • UV wavelength: 254 nm
  • Chemical dose: 5 mg/L [Goethite α-FeOOH], 10 mmol/L [H2O2]
  • HRT: 5–60 min
  • pH: 7.84
Initial concentration of contaminant: 12 mg/L (SDZ), 5.8 mg/L (TOC)
sulfadiazine (SDZ) removal: ~100%(5 min), ~80%(60 min)
[119]
Real wastewater
  • Solar/Fe-EDDS
  • UV wavelength: 327 to 384 nm (sunlight)
  • Chemical dose: 0.88 [H2O2], 0.1 mM [Fe]
  • HRT: 40 min
  • pH: neutral
Initial concentration of CECs: 59.1–77.7 ng/L
Total removal: ~61% (20 min)
[120]
Real wastewater
  • Solar/Fe- EDDS
  • UV wavelength: 327 to 384 nm (sunlight)
  • Chemical dose: 3 ppm [Fe2(SO4)3] and 2.75 ppm [H2O2]
  • HRT: 60 min
  • pH: 7–8
Initial concentration of contaminant: 1 ppm
Total removal (DI): 89–94% (AMX), 92–95% (PC)
Total removal (WW): ~50% (AMX), ~30% (PC)
[70]
Real wastewater
  • Solar/Fe-EDDS
  • UV wavelength: 300–800 nm (sunlight/artificial sunlight)
  • Chemical dose: 0.054 [Fe], 1.47 mM [H2O2]
  • HRT: 10–30 min
  • pH: 7
Initial concentration of contaminant: 100 µg/L
Caffeine removal: ~90%
Carbamazepine removal: ~90%
Diclofenac removal: ~90%
Sulfamethoxazole removal: ~90%
Trimethoprim removal: ~90%
[121]
Real wastewater doped with SMX
  • UV/Fe-EDDS, solar/Fe-EDDS,
  • UV wavelength: 327–384 nm (sunlight/UVA)
  • Chemical dose: 0. [Fe3+-EDDS], 0.88 mM [H2O2]
  • UV Power: 30 W/m2
  • HRT: 60 min
  • pH: 6.5–7.5
Initial concentration of contaminant: 50 µg/L
Sulfamethoxazole removal: ~80% (UV), ~40% (solar)
Wild E. coli inactivation: below the detection limit
[122]
Real wastewater doped with SMX and IMD
  • Solar/Fe-NTA
  • UV wavelength: 327–384 nm (sunlight/UVA)
  • Chemical dose: 0.1 [Fe3+-EDDS], 0.88 mM [H2O2]
  • UV Power: 35 ± 2 W/m2
  • HRT: 60 min
  • pH: 6–7
Initial concentration of contaminant: 50 µg/L
Sulfamethoxazole removal: ~90% (UV) Imidacloprid removal: ~80% (UV)
[123]
PPCPs removal in wastewater effluent
  • UV/FeIII-NTA
  • UV wavelength: 365 nm (sunlight/UVA)
  • Low-power mercury lamp
  • Chemical dose: 4.54 [H2O2], 0.178 mM [FeIII-NTA]
  • UV Power: 4.05 mW/cm2
  • HRT: 60 min
  • pH: 7
Initial concentration of contaminant: 452.6 (CBZ), 394.6 (CRMT), and 101.1 (IBP) μg/L
CBZ removal: ~80%
Crotamiton (CRMT): ~80%
Ibuprofen (IBP): ~80%
[124]
PPCPs removal in wastewater effluent
  • UV/Fe-HA
  • UV wavelength: 295–400 nm (artificial sunlight)
  • Low-power mercury lamp
  • Chemical dose: 1.0 [H2O2], 100 mg/L [FeIII-NTA]
  • UV Power: 1500 W, cut-off filter at below 340 nm
  • HRT: 60 min
  • pH: 3
Initial concentration of contaminant: 0.2, 0.1 mM carbamazepine, 20 μmol (blended)
Ibuprofen removal: ~30%
Bisphenol A removal: ~80%
tolylbenzotriazole removal: ~90%
Carbamazepine removal: ~80%
Blended mix: ~90% (in DI water)
[125]
PPCPs removal in wastewater effluent
  • UV/Fe
  • UV wavelength: natural sunlight
  • Chemical dose: 50 mg/L [H2O2], 20 mg/L [Fe]
  • UV Power: 21–26.5 W/m2
  • HRT: 180 min
  • pH: neutral
Initial concentration of contaminant: as per wastewater stream
Blended: 73–82%
Azithromycin (AZT) removal: 24%
Ciprofloxacin (CIP) removal: 100%
Clarithromycin (CLR) removal: 8–24%
Clindamycin: 57–86%
Enrofloxacin (ERF) removal: 100%
Erythromycin (ENR) removal: 22–36% levofloxacin (LEV) removal: 61–75%
Lincomycin (LIN) removal: 84%
Metronidazole (MET) removal: 70%
[126]
Table 7. Current application of photocatalysis processes.
Table 7. Current application of photocatalysis processes.
CategoriesWastewater/
Compounds
Operation ConditionRemoval EfficiencyReferences
Titanium oxide-based catalystSynthetic wastewater containing tris-(2-chloroisopropyl) phosphate (TCPP)
  • UV/TiO2 (benchtop)
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 254 nm (UVC)
  • Power: 15 W
  • UV fluence: 4.7 mW/cm2
  • Catalyst dose: 0.1 g/L
  • HRT: 60 min
  • pH: 7.0
Initial concentration of contaminant: 1 mg/L
TCPP: ~100% removal
TOC: ~80% removal
[128]
Synthetic wastewater with acesulfame potassium (ACE-K) and sodium saccharin (SAC)
  • UV/TiO2 (benchtop)
  • High-pressure UV lamp (HP-UV)
  • UV wavelength: 365 nm (UVA)
  • Power: 125 W
  • Photon flux rate: 3.435 × 10−7 Einstein/Ls
  • Catalyst dose: 0.375 g/L
  • HRT: 60 min
  • pH: 6.0
Initial concentration of contaminant: 20 mg/L each
Degradation: Both: 100% removal
Mineralization:
ACE-K: 57% removal
SAC: 49% removal
[129]
Synthetic wastewater containing tetracycline hydrochloride (TC)
  • UV/TiO2 (benchtop)
  • High-pressure UV lamp (HP-UV)
  • UV wavelength: 360 nm (UVA)
  • Power: 30 W
  • Catalyst dose: 0.12 g
  • HRT: 240 min
  • • pH: 4.0
Initial concentration of contaminant: 40 mg/L
TC: 96% removal
[130]
Synthetic wastewater containing sulfamethazine (SMT)
  • UV/TiO2(P25) (benchtop)
  • High-pressure UV lamp (HP-UV)
  • UV wavelength: 360 nm (UVA)
  • Power: 9 W
  • Catalyst dose: 0.25 g/L
  • HRT: 240 min
  • pH: 6.0
Initial concentration of contaminant: 50 mg/L
SMT: 42% removal
COD: 34% removal
[131]
Secondary treatment effluent
  • UV/TiO2(P25) (Benchtop)
  • High-pressure UV lamp (HP-UV)
  • UV wavelength: 417 nm (UVA)
  • UV intensity: 400–500 W/m2
  • Catalyst dose: 1.0 g/L
  • HRT: 240 min
  • pH: natural
Initial concentration of contaminants: varied
Contaminants: 25–90% removal
[132]
Wastewater effluent or synthetic wastewater containing Bisphenol A
  • UV/TiO2 and variants (Benchtop)
  • Solar simulator
  • UV wavelength: Visible
  • Power: 500 W
  • Catalyst dose: 0.5 g/L
  • HRT: 120 min
  • pH: natural
Initial concentration of BPA: 10 mg/L
Synthetic wastewater:
With UV/TiO2 (P25): 100% removal With UV/TiO2-WO3: 30% removal
With UV/TiO2: 38% removal
Real wastewater:
With UV/TiO2 (P25): 62% removal
With UV/TiO2-WO3: 2% removal
[57]
Synthetic wastewater with dinitro butyl-phenol (DNBP) (herbicide)
  • UV/TiO2
  • Xe lamp
  • UV wavelength: 420 nm (UVA)
  • Power: 500 W
  • Catalyst dose: 0.5 g/L
  • HRT: 240 min
  • pH: natural
Initial concentration of DNBP: 20 mg/L
DNBP: 28% removal
[133]
Synthetic wastewater containing carbamazepine (CBZ), ibuprofen (IBU,) and sulfamethoxazole (SMX)
  • UV/TiO2(P25) (benchtop)
  • High-pressure UV lamp (HP-UV)
  • UV wavelength: 365 nm (UVA)
  • Power: 160 W
  • Catalyst dose: Bundle of 30 fibers
  • HRT: 180 min
  • pH: natural
Initial concentration of pharmaceuticals: 5 mg/L each
IBU: 38% removal
CBZ: 38% removal
SMX: 64% removal
[134]
Synthetic wastewater containing metronidazole (MNZ)
  • UV/TiO2(P25) (benchtop)
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 254 nm (UVC)
  • Power: 20 W
  • Catalyst dose: 0.2 g/L
  • HRT: 80 min
  • pH: natural
Initial concentration of MNZ: 35 mg/L
MNZ: 43.02% removal
[114]
Synthetic wastewater containing saccharin (SAC)
  • UV/TiO2(P25) (benchtop)
  • LED lamp
  • UV wavelength: 365 nm (UVA)
  • Power: 11 W
  • Catalyst dose: 0.125 g/L
  • HRT: 45 min
  • pH: 4.6
Initial concentration of saccharin: 5 mg/L
SAC: 100% removal (after 30 min)
[135]
Zinc based catalystSynthetic wastewater containing sulfamethazine (SMT)
  • UV/ZnO (benchtop)
  • High-pressure UV lamp (HP-UV)
  • UV wavelength: 360 nm (UVA)
  • Power: 9 W
  • Catalyst dose: 0.25 g/L
  • HRT: 240 min
  • pH: 6.0
Initial concentration of contaminant: 50 mg/L
SMT: 64% removal
COD: 45% removal
[131]
Synthetic wastewater
  • UV/ZnO (benchtop)
  • High-pressure UV lamp (HP-UV)
  • UV wavelength: 366 nm (UVA)
  • Power: 2 × 8 W
  • Catalyst dose: 0.2 g/L
  • HRT: 120 min
  • pH: 7.0
  • UV/ZnO (pilot plant)
  • Natural sunlight
  • UV wavelength: VIS + NIR, UVA, UVB, and UVC
  • UV Intensity: 0.2 ± 0.1–1011.6 ± 66.2 iW/m2
  • Catalyst dose: 0.2 g/L
  • HRT: 240 min
  • pH: 7.2
Initial concentration of contaminants: 0.3 mg/L
Synthetic wastewater:
Contaminants: 97.5–99.7% removal (after 90 min)
Real wastewater:
Contaminants: 76.0–100.0% removal (after 240 min)
[59]
Synthetic wastewater with sulfathiazole (STZ)
  • UV/ZnO (benchtop)
  • Xe lamp
  • UV wavelength: 350–400 nm (UVA)
  • Power: 1000 W
  • Catalyst dose: 2 g/L
  • HRT: 90 min
  • pH: natural
Initial concentration of STZ: 0.1 M
STZ: 69% removal
[66]
Synthetic wastewater containing methylene blue (MB) and real industrial dye
  • UV/ZnO (benchtop)
  • Sunlight
  • UV wavelength: visible
  • Catalyst dose: 0.2 g/L
  • HRT: 80 min
  • pH: natural
Initial concentration of MB: 20 mg/L MB: ~99% removal (after 120 min)[136]
Carbon-based catalystSynthetic wastewater containing phenol and dinitrophenol
  • UV/SV/FG24 (benchtop)
  • LED lamp
  • UV wavelength: visible light
  • Power: 35 W
  • UV intensity: 750 lx
  • Catalyst dose: 0.5 g/L
  • HRT: 180 min
  • pH: 4.0
Initial concentration of phenol and DNP: 0.1 mM each
Phenol: 95% removal
DNP: 88% removal
[137]
Synthetic wastewater containing amoxicillin (AMX)
  • UV/g-C3N4 (benchtop)
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 254 nm (UVC)
  • Power: 10 W
  • Lamp intensity = 3.5 mJ/cm2
  • Catalyst dose: 1 g/L
  • HRT: 200 min
  • pH: natural
Initial concentration of AMX: 0.25 mM
AMX: 67% removal
TOC: 42% removal
[61]
Synthetic wastewater with diclofenac sodium (DCF)
  • UV/0.20%Co3O4-g-C3N4 (benchtop)
  • Xe lamp
  • UV wavelength: 420 nm (visible)
  • Power: 50 W
  • Catalyst dose: 0.5 g/L
  • HRT: 120 min
  • pH: natural
Initial concentration of DCF: 10 mg/L
DCF: 100% removal (30 min in presence of 0.1 mM PMS)
[138]
Synthetic wastewater containing carbamazepine, ibuprofen, and sulfamethoxazole
  • UV/TiO2–2.7% rGO (benchtop)
  • High-pressure UV lamp (HP-UV)
  • UV wavelength: 365 nm (UVA)
  • Power: 160 W
  • Catalyst dose: bundle of 30 fibers
  • HRT: 180 min
  • pH: natural
Initial concentration of pharmaceuticals: 5 mg/L each
IBU: 81% removal
IBU-TOC: 52% removal
CBZ: 54% removal
CBZ-TOC = 54% removal
SMX: 92% removal
SMX-TOC = 59% removal
[134]
Synthetic wastewater containing phenol and dinitrophenol (DNP)
  • UV/FG24 (benchtop)
  • LED lamp
  • UV wavelength: visible light
  • Power: 35 W
  • UV intensity: 750 lx
  • Catalyst dose: 0.5 g/L
  • HRT: 180 min
  • pH: 4.0
Initial concentration of phenol and DNP: 0.1 mM each
Phenol: 36% removal
DNP: 25% removal
[137]
Synthetic wastewater containing 4-nitrophenol (PNP)
  • UV/3%rGO/ZrO2/Ag3PO4 (benchtop)
  • Low-pressure UV lamp (LP-UV)
  • UV wavelength: 254 nm (UVC)
  • Power: 4 × 6 W
  • Catalyst dose: 0.25 g/L
  • HRT: 120 min
  • pH: 6.0
Initial concentration of PNP: 15 mg/L
PNP: 97% removal
[139]
Other types of catalystSynthetic wastewater and treated sewage effluent spiked with ibuprofen (IBU)
  • UV/gCTFS (benchtop)
  • Fluorescent lamp
  • UV wavelength: visible
  • Power: 8 × 8 W
  • Lamp intensity = 330 W/m2
  • Catalyst dose: 1 g/L (syn wastewater), 2 g/L (real wastewater)
  • HRT: 180 min
  • pH: natural (syn wastewater), 6.9 ± 0.2 (real wastewater)
Initial concentration of IBU: 2 mg/L Synthetic wastewater:
IBU: 97% removal (15 min)
Real wastewater:
IBU: 92% removal (180 min)
[140]
Synthetic wastewater with sulfathiazole (STZ)
  • UV/LuAG: Ce/ZnO (benchtop)
  • Xe lamp
  • UV wavelength: 350–400 nm (UVA)
  • Power: 1000 W
  • Catalyst dose: 2 g/L
  • HRT: 90 min
  • pH: natural
Initial concentration of STZ: 0.1 M
STZ: ~100% removal
[66]
Synthetic wastewater with 1,4-dioxane (1,4-D)
  • Solar/WO3/nγ-Al2O3 (benchtop)
  • Solar simulator
  • Wavelength: 190–1100 nm
  • Power: 40 mW
  • Catalyst dose: 100–700 mg/L
  • HRT: 4 h
  • pH: 6.8
Initial concentration of 1,4-D: 50 mg/L
1,4-D: >75% mineralization
[141]
Synthetic wastewater with levofloxacin (LFX) and ketoprofen (KPT)
  • Photoelectrochemical/β25 modified WO3 (benchtop)
  • Hg medium pressure lamp
  • Wavelength: 360–380 nm
  • Power: 0.128 W
  • Catalyst dose: 0.1 g
  • HRT: 5 h
  • pH: 6.8
Initial concentration of LFX: 10 mg/L
LFX: ~90% removal
Initial concentration of KPT: 10 mg/L
KPT: >65% removal
[142]
Synthetic wastewater with dexamethasone (DXM)
  • BLB/WO3 (benchtop)
  • UV and halogen lamps
  • Wavelength: 254 nm (UVC), 365 nm (UVA) and >380 nm (halogen)
  • Power: 0.128 W
  • Catalyst dose: 500 mg/L
  • HRT: 60 min
  • pH: 3
Initial concentration of DXM: 5 mg/L
DXM: ~100% removal
[143]
Table 8. Evaluation of light-driven technology for the detection and management of CECs.
Table 8. Evaluation of light-driven technology for the detection and management of CECs.
Type of ProcessName of ProcessTheoryMechanismAdvantagesDisadvantagesReferences
Adsorption of lightFT-NIR spectroscopyPolychromatic light beam at a sample, measure the intensity of the light as a function of time. It allows simultaneous measurement over the whole wavelength rangeElectromagnetic radiation (EMR) interacts with atoms and molecules in discrete ways to produce characteristic absorption profiles
f = c λ
E = h c λ × 10 9
Planck’s constant, h = 6.63 × 10−34 Js
Speed of light in a vacuum, c = 2.998 × 108 ms−1
Able to do online analysisMight not be the most accurate due to similar adsorption spectra of various chemical bonds[153]
Infrared (IR) spectrumBeam containing a different combination of frequencies shone on the sample and measured for light absorbed. This process is rapidly repeated with different combinations of light wavelengths. Correlations between the data points would be used to infer contaminants in the sampleSlightly more accurate due to the use of multiple combined wavelengths-[159]
Excitation and emissionExcitation emission matrix (EEM)An EEM is a 3D scan, of the excitation wavelength vs. emission wavelength vs. fluorescence intensity of a sample when a given monochromatic beam of light is shone on the sampleSimilar to the mechanism above, EEM also uses a similar mechanism, however, it correlates the excitation, reemission and fluoresces as wellFast and quick analysisAt present might not be as accurate. It requires a large scale of intensive data processing [151]
Size exclusion coupled UV based detectionSize exclusion coupled detection methodsThis method is similar to the EEM analysis except it has an element of size exclusionMore accurate measurements, due to an additional size exclusion mechanismMight not be as accurate presently[160]
ImmunoassayMeasure changes in color
or emission of light
Antibodies competitively capture dissolved targets and immobilized
antigens. After the washing step, the labeled secondary antibodies
bond to the corresponding antibodies. Signals can be obtained after
incubation and another washing procedure
Low cost, simple procedure Organic solvent and environmental factors may interfere with the immunoreaction[161]
Physio-chemical signal detectionChemiluminescenceChemical reactions occur, exhibiting light signaturesExplores the chemical signature of selected types of compounds which gives off fluorescenceAble to identify the luminescence compounds with a fairly high degree of accuracyHighly selective to types of compounds that emit fluorescence[162]
Surface-enhanced Raman
scattering (SERS)
Novel spectrum analysis technology based on the combination of Raman spectroscopy and nanotechnologyThe molecular fingerprint specificity of Raman spectroscopy is used for the detection of CECs. The enhancement Raman signal is achieved through electromagnetic enhancement mechanism and chemical enhancement
mechanism
High sensitivity, non-destructive, capability for molecular fingerprintNot stable, not cost-effective, Complex synthetic procedure, complicated synthetic procedure [155]
Surface plasmon resonance bandCollective electron charge oscillations of contaminants excited by light are measured-Only available for detection for nano-metal detection and costlyOnly available for detection for nano-metal detection[163]
Table 9. Advantages and disadvantages of light-driven AOPs.
Table 9. Advantages and disadvantages of light-driven AOPs.
Name of AOP AdvantagesDrawbacksReferences
UV/OxidantUV/H2O2Cheap and easy to implementLow degradation performance
Control of peroxide dosage needed to meet discharge limits
[51]
UV-persulfateFast reaction time
Large pH range
Selective degradation at pH below 7, since SO4•−; is the dominant free radical
pH adjustment needed
[52]
UV-chlorineEasy to implement
Higher kinetics constants with CECs than HO•
Formation of disinfection by-products
Selective degradation of electron-rich moieties compared to unsaturated C-C bonds
[53]
UV/OzoneMicrobubbleFast reaction time
Low EEO
High operational and capital cost
Works better at elevated pH
The limited usable wavelength of light
[54]
Photo-FentonHeterogeneousDoes not require sludge treatment
The large bandwidth of light usage
Slower reaction kinetics[174]
HomogeneousFast reaction time
pH range can be extended via the use of chelators like EDDS
Able to use solar energy
Large surface area needed for solar processes[70,175]
PhotocatalysisTiO2Able to use solar energy with morphology modificationsHigh energy consumption is needed for the activation
Requires high energy UV wavelength
Conventional photocatalyst has issues with the reuse of the catalyst
[57,61,176]
ZnOAble to utilize sunlight without any modifications
Cheaper catalyst compared to TiO2
High energy consumption is needed for the activation
Conventional photocatalyst has issues with the reuse of the catalyst
Degradation performance was not as good as the titanium-based catalyst
[59]
Alternative catalystAble to use solar energyRelatively new and requires further study of efficiency
Suffers potential issues with conventional TiO2 and ZnO issues like the reuse and separation of catalyst
[137,138,141]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Lee, B.C.Y.; Lim, F.Y.; Loh, W.H.; Ong, S.L.; Hu, J. Emerging Contaminants: An Overview of Recent Trends for Their Treatment and Management Using Light-Driven Processes. Water 2021, 13, 2340. https://doi.org/10.3390/w13172340

AMA Style

Lee BCY, Lim FY, Loh WH, Ong SL, Hu J. Emerging Contaminants: An Overview of Recent Trends for Their Treatment and Management Using Light-Driven Processes. Water. 2021; 13(17):2340. https://doi.org/10.3390/w13172340

Chicago/Turabian Style

Lee, Brandon Chuan Yee, Fang Yee Lim, Wei Hao Loh, Say Leong Ong, and Jiangyong Hu. 2021. "Emerging Contaminants: An Overview of Recent Trends for Their Treatment and Management Using Light-Driven Processes" Water 13, no. 17: 2340. https://doi.org/10.3390/w13172340

APA Style

Lee, B. C. Y., Lim, F. Y., Loh, W. H., Ong, S. L., & Hu, J. (2021). Emerging Contaminants: An Overview of Recent Trends for Their Treatment and Management Using Light-Driven Processes. Water, 13(17), 2340. https://doi.org/10.3390/w13172340

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop