This study assessed the factors affecting the growth and survival of microorganisms in a small horizontal subsurface flow constructed wetland (HSSF CW) treating stormwater runoff from highly impervious road and parking lot through long-term monitoring from 2010 until present. The HSSF CW facility consisted of sedimentation or pre-treatment zone, vegetation zone, and effluent zone, and employed filter media including bio-ceramics, sand, gravel, and wood chips. Results showed that flow reduction in the wetland through filtration and sedimentation played an important part in the overall performance of the HSSF CW. In addition, vegetation growth was found to be affected by pollutant and stormwater inflow in the HSSF CW. Vegetation near the outflow port exhibited greater growth rates by about 6.5% to 64.2% compared to the vegetation near the inflow port due to the less stormwater pollutant concentrations via filtration mechanism in the plant or media zone of the HSSF CW. The pollutant inflow from road and parking lot played an important role in providing good environment for microbial growth especially for the dominant microbial phyla including Proteobacteria, Actinobacteria and Acidobacteria in the HSSF CW. The findings of this research are useful in understanding treatment mechanisms and identifying appropriate design considerations for HSSF CW.

This study was conducted to assess the applicability of green roofs as a low impact development (LID) technology to reduce storm water runoff volume and nonpoint source pollutants. Specifically, the water cycle effects and pollutant removal efficiency through six monitoring results were analyzed. Based on the results, the green roof system achieved an average runoff discharge rate of 72% for storage, exhibiting a rainfall outflow reduction rate of about six times greater than that of the ordinary concrete rooftop. The average reduction efficiency of pollutants was 77%, 43%, 74%, 57%, and 43% for TSS, BOD, TOC, TN, and TP, respectively. In addition, the reduction efficiencies for heavy metals, including Cu and Zn, and isomers such as n-H were all greater than 72%. However, this removal efficiency was highly dependent on rainfall, which was observed specifically for nutrients, including TN and TP, which showed a negative removal for a 40 mm rainfall. Therefore, it seems to be better when the green roof system was installed with LID technologies such as infiltration trenches, rain gardens, infiltration planters, and other infiltration facilities.

The efficiency of nature-based facilities is mostly evaluated in terms of their pollutant removal capabilities; however, apart from water purification functions, constructed wetlands also perform ecological, cultural, and environmental education functions. A multi-criteria performance index was developed in this study to evaluate the overall efficiency of constructed wetlands. A total of 54 constructed wetlands installed across South Korea were monitored to evaluate the pollutant removal performance of the facilities. A comparison between the conventional pollutant removal-based evaluation and the developed multi-criteria index was also performed to determine the key changes in the results of evaluation when different methods are employed. Among the different types of wetlands studied, hybrid systems were found to be the most effective in terms of pollutant removal due to their complex configurations and functions. Newly constructed treatment wetlands have unstable performance and thus, a stabilization period ranging from two to five years is required to assess the facility’s pollutant removal capabilities. As compared to the conventional pollutant removal-based efficiency evaluation, the comprehensive evaluation method provided a more strategic tool for identifying the facilities’ strengths and weaknesses. Generally, the multi-criteria performance index developed in this inquiry can be utilized as a general tool for evaluating the sustainability of similar nature-based facilities.

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.

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