4.2.2. Full-Scale

For the full-scale field studies, multiple treatment units either incorporating both traditional water treatment processes (such as filtration, sedimentation, anaerobic digestion, etc.) and constructed wetland treatment processes, or a hybrid system with a series of different CW cells (such as SF, SSHF, SSVF, etc.) were used [40,91]. For assessing treatment performance, an entire system's performance over a long time was monitored [40,91] or the performances at various stages within the system were compared by collecting samples at multiple sampling points [72,90,94]. The application of CW in the field could be an entire system, or sometimes just one unit in addition to the traditional treatment units. For example, the Chen et al. (2012) study compared a traditional swine wastewater treatment system (A) with another system (B) containing additional aquatic vegetation ponds (serving as SF CWs) as a final polishing unit [40]. The results showed that biological activities had a significant impact on the degradation of target contaminants but less impact on the dissipation of contaminants at low concentrations [40]. One common challenge for field studies compared to the pilot-scale or lab-scale studies is there is no perfect "control treatment" to refer to. Therefore, background/influent concentration data for such field studies are extremely important, as they can serve for the comparison of pre and post CW treatment. As an example, Locke et al. (2011) simulated a runoff study which sampled before and after the flushing events for comparisons of the removal rate [91]. Their results indicated that CW could help to protect the downstream water quality through degradation and sorption of the pollutants and retention caused by adsorption and/or uptake by vegetation even after the flushing event throughout the entire 21-day study period [91]. For the integrated/hybrid CW system using multiple treatment units with different designs, the concentrations of target contaminants typically showed a decreasing trend along water flow through various stages. For example, in the study by Chen et al. (2015), which utilized the field CW system containing six units in series and receiving rural wastewater, the antibiotic concentrations decreased continuously along the treatment train as each unit's effluent concentrations were lower than that of the previous unit [90]. This indicated that with careful design and reasonable arrangements, multiple CW treatment units could run in series to achieve better overall removal performance. More complicated systems, such as in the Abdel-Mohsein et al. study (2011), which applied various CW types in series and operated in parallel at each stage with three different treatments in a rotational mode, proved to further enhance the retention time and achieve remarkable removal efficiencies of antibiotic-resistant bacteria with zero residues in the effluent water [72]. Besides these studies looking into the performance at different stages of CW system, some full-scale field studies investigated the removal ability of a single established CW system for various contaminants. For example, Choi et al. (2016) monitored a mature CW system receiving livestock wastewater without any spikes and their results showed various removal rates for the eight antibiotics present in the wastewater [41]. Therefore, it is important to consider whether CW is suitable for the target pollutants based on its chemistry and properties. Conversely, unsatisfying operation performance may occur if the target pollutants are out of the scope from the designed CW's treatment ability.

#### **5. Factors Impacting CW Performance**

Based on the literature research, 34 relevant studies have been reviewed for the removal of CECs by CWs. The information has been summarized in Table 1 and various parameters and their impacts on CW performance are discussed in the following section in random order.


*Sustainability* **2021**, *13*, 13578

**Table 1.** Removal of pollutants in agricultural runoff wastewater by constructed wetlands.



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19



21


Note: SF—surface flow; SSHF—subsurface horizontal flow; SSVF—subsurface vertical flow; HLR—hydraulic loading rate; HRT—hydraulic retention time; NA—not applicable.

#### *5.1. Target Contaminant Property*

Based on the various physicochemical properties, such as pKa, molecular weight, solubility, and functional groups, different contaminants showed different levels of removal by CW systems. From the study by Choi et al. (2016), the major removal mechanism was the adsorption to soil, which was favored for compounds with lower molecular weights and higher pKa values [41]. Gorito et al. (2018) also suggested that high removal of azithromycin through sorption onto the soil and plant uptake were likely due to its high octanol–water coefficient (Kow) and pKa values [74]. In addition, contaminants with low solubility and high soil adsorption coefficient (Koc > 1000) would have better sorption and retention in soils. For example, Gikas et al. (2018) found poor adsorption of selected pesticides due to moderate solubility and low Koc [92]. This was also supported by a pesticide study conducted by Agudelo et al. (2010) as target contaminant chlorpyrifos with low solubility and high adsorption coefficient showed grea<sup>t</sup> sorption into the soil substrate or the humic colloids suspended in the water [78]. Vystavna et al. (2017) indicated that compounds, such as propranolol, tend to accumulate in sediments due to its hydrophobicity, therefore, the utilization of porous filter materials with high sorption ability could improve the removal percentages for such compounds [100]. Functional group and structure could also impact pollutant removal mechanism, as the Lyu et al. (2018) study showed that hydrolysis was negligible for tebuconazole removal due to its chemical properties [97]. Overall, to achieve optimal removal performance by CW systems, one should consider the physical and chemical properties of the target contaminant during the design of the CWs as those properties are likely to impact their removal mechanisms.
