Contaminant removal and hydraulic conductivity of laboratory rain garden systems for stormwater treatment

Assessment of Rain Garden Effects for the Management of Urban Storm Runoff in Japan

Storm runoff is a growing concern against a background of increasing urban densification, land-use adaptation and climate change. In this study, a storm water management model was used to analyze the hydrological and water-quality effects of rain gardens (also known as bioretention cells) as nonpoint source control solutions in low-impact development (LID) practices for an urban catchment in the Nakagyo Ward area of Kyoto in Japan. The results of simulations with input involving Chicago hyetographs derived for different rainfall return periods (referred to as 3 a, 5 a, 10 a, 30 a, 50 a and 100 a) indicated the effectiveness of this arrangement, in particular for rainstorm 3 a, which exhibited the maximum contaminant reduction ratio (Total Suspended Solids (TSS) 15.50%, Chemical Oxygen Demand (COD) 16.17%, Total Nitrogen (TN) 17.34%, Total Phosphorus (TP) 19.07%) and a total runoff reduction volume of 46.56 × 106 L. With 5 a, the maximum number of flooding nodes was reduced to 87, demonstrating that rain gardens handle rainfall effectively over a five-year return period. There was a one-minute delay for 100 a, which again indicates that rain gardens support control of urban runoff and mitigate flooding. Such gardens were associated with reduced stormwater hazards and enhanced resistance to short-term rainstorms at the research site, and should be considered for urban planning in Kyoto and other cities all over the world.

The impact of media, plants and their interactions on bioretention performance: A review

Bioretention systems have gained considerable popularity as a more natural approach to stormwater management in urban environments. The choice of bioretention media is frequently cited as one of the critical design parameters with the ultimate impact on the performance of the system. The goal of this review is to highlight data that challenge the importance of media as being the dominant design parameter and argue that the long-term performance is shaped by the interactions between media and the living components of a bioretention system, especially vegetation. Some of the key interactions are related to the impact of plant roots on media pore structure, which has implications on infiltration, storage capacity, and treatment. Another relevant interaction pertains to evapotranspiration and the associated impacts on the water balance and the water quality performance of bioretention systems. The impacts of vegetation on the media are highlighted and actual, as well as potential, impacts of plant-media interactions on bioretention performance are presented.

Predicting bioretention pollutant removal efficiency with design features: A data-driven approach

The objective of this study is to synthesize previous research findings from bioretention experiments and identify design features that lead to the best performance of bioretention pollutant removal with a data-driven approach. A bioretention database was built from 79 bioretention publications, composed of 182 records of bioretention cells with their design features and the corresponding pollutant removal efficiency data. Non-parametric correlation analysis, multiple linear regression (MLR), and decision tree classifiers were applied to investigate the relationships between bioretention design features and pollutant removal efficiencies. Non-parametric statistics and MLR results indicated that bioretention surface area, media depth, the presence of an internal water storage (IWS) layer, soil composition, and vegetation cover are all significantly correlated with pollutant removal efficiencies. The impacts of design features are significantly different under different climate and inflow conditions. Decision tree classifiers showed that non-vegetated bioretention cells with sand filter media generally have higher than 80% total suspended solid (TSS) mass removal efficiencies; bioretention cells with minimum organic matter and greater than 0.58 m soil media depth tend to remove more than 51% of total nitrogen (TN); and vegetated bioretention cells with minimum organic matter remove more than 67% of total phosphorus (TP). The overall accuracy of decision tree classifiers in the test set is around 70% to predict TSS, TN, and TP mass removal efficiency classes. This study suggests that the data-driven approach provides insights into understanding the complex relationship between bioretention design features and pollutant removal performance.