The procedures for analyzing pharmaceuticals and personal care products (PPCPs) are typically tedious and expensive and thus, it is necessary to synthesize all available information from previously conducted research. An extensive collection of PPCP data from the published literature was compiled to determine the occurrence, pathways, and the effectiveness of current treatment technologies for the removal of PPCPs in water and wastewater. Approximately 90% of the compiled published papers originated from Asia, Europe, and the North American regions. The incomplete removal of PPCPs in different water and wastewater treatment processes was widely reported, thus resulting in the occurrence of PPCP compounds in various environmental compartments. Caffeine, carbamazepine, diclofenac, ibuprofen, triclosan, and triclocarban were among the most commonly reported compounds detected in water and solid matrices. Trace concentrations of PPCPs were also detected on plants and animal tissues, indicating the bioaccumulative properties of some PPCP compounds. A significant lack of studies regarding the presence of PPCPs in animal and plant samples was identified in the review. Furthermore, there were still knowledge gaps on the ecotoxicity, sub-lethal effects, and effective treatment processes for PPCPs. The knowledge gaps identified in this study can be used to devise a more effective research paradigm and guidelines for PPCP management.

Stormwater runoff monitoring was carried out from 2011 to 2015 to investigate the relationships between rainfall conditions (antecedent dry days (ADDs), rainfall intensity, depth and duration), and water quality parameters of stormwater from a paved road in Korea. Factor analysis suggested that the effect of rainfall conditions on the concentrations of selected pollutants varied depending on the pollutant. As total COD (total chemical oxygen demand) concentration increased, the level of heavy metals increased and resulted in a decrease of BOD 5 (biochemical oxygen demand) because of their toxicity. In addition, ADDs had a significant impact on the wash-off of solids from paved road. The predominant particles in stormwater were 30 µm and smaller, and increased in concentration as ADDs increased. Thus, the initial load of accumulated particles became a major factor in the wash-off process. The mass of particle-related pollutants was also subject to the effect of ADDs due to the affinity between pollutants and predominant particles (<30 µm). However, the effect of ADDs on the mass of organic matter and nitrogen was relatively weak. ADDs contributed to the decrease of some pollutants by photo-oxidation, volatilization and natural decay over dry days, as well as desorption from solids during rainfall.

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This study was conducted to develop a vertical subsurface flow (VSF) wetland remediation system packed with woodchips to control stormwater pollution arising from livestock agriculture. Three lab-scale VSF wetlands were operated with recirculation during the interval (Δ) between storms as 2, 4 and 8 days, respectively. The fed water was 100% recirculated one time per 24 h; the recirculation frequency was 1, 3 and 7 times at Δ of 2, 4 and 8 days, respectively. The constructed wetland systems proved to be effective in reducing total suspended solid (TSS), but also had potential for increasing TSS in the effluent due to the properties of the woodchips. The release of organic matter, especially in the dissolved form, occurred during the initial 60 days. The removal efficiencies of total nitrogen (TN) were 26.2%, 34.1% and 50.0% at Δ of 2, 4 and 8 days, respectively. Nitrification was promoted by the abundant oxygen supplied when the water in wetland was recirculated and fed into the wetland. Denitrification was stable and effective due to the availability of carbon sources. The influent total phosphorus (TP) was reduced from an average of 2.05 mg L(-1) to 1.79 mg L(-1), 1.36 mg L(-1) and 0.86 mg L(-1) at Δ as 2, 4 and 8 days, respectively. The result shows that woodchips can be used as substrate material for VSF wetland treatment systems to control nutrient influx from livestock stormwater.

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A laboratory study was undertaken to pursue the filter performance of a micro-filter module employing highly porous fibre media under a high filtration rate (≥1,500 m/day), faster than that of any conventional filter process. The effects of filtration rate, head loss, raw water turbidity, and filter aid chemicals on filter performance were analysed. In spite of the extremely high filtration rate, the filter achieved an attractive efficiency, reducing the raw water turbidity by over 80%. As with other filter systems, the filter aid used ((polyaluminium chloride (PAC)) greatly affected the performance of this particular fibre filter. Long-term repetitive runs were additionally carried out to confirm the reproducibility of the filter performance. Also, a comparison was carried out with other high-rate filter systems which are either being tested for use in experimental studies, or are already commercially available. This study reveals that the filter performance under a high filtration speed is still attractive especially as PAC is used. Due to the high porosity of the fibre, the filter had small head loss even though the filtration rate was high. These results ascertain that it is possible to operate the filters with high filtration rate achieving reliable treatment performance.

Twenty-three rainfall events were monitored to determine the characteristics of the stormwater runoff entering a rain garden facility and evaluate its performance in terms of pollutant removal and volume reduction. Data gathered during the five-year monitoring period were utilized to develop a deep learning-based model that can predict the concentrations of Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), Total Nitrogen (TN), and Total Phosphorus (TP). Findings revealed that the rain garden was capable of effectively reducing solids, organics, nutrients, and heavy metals from stormwater runoff during the five-year period when hydrologic and climate conditions have changed. Volume reduction was also high but can decrease over time due to the accumulation of solids in the facility which reduced the infiltration capacity and increased ponding and overflows especially during heavy rainfalls. A preliminary development of a water quality prediction model based on long short-term memory (LSTM) architecture was also developed to be able to potentially reduce the labor and costs associated with on-site monitoring in the future. The LSTM model predicted pollutant concentrations that are close to the actual values with a mean square error of 0.36 during calibration and a less than 10% difference from the measured values during validation. The study showed the potential of using deep learning architecture for the prediction of stormwater quality parameters entering rain gardens. While this study is still in the preliminary stage, it can potentially be improved for use in performance monitoring, decision-making regarding maintenance, and design of similar technologies in the future.