Michigan State University, 552 W Circle Dr, East Lansing, MI, 48823, United States.
Rochester Institute of Technology, 54 Lomb Memorial Dr, Rochester, NY, 14623, United States.
J Environ Manage. 2019 Jul 15;242:403-414. doi: 10.1016/j.jenvman.2019.04.064. Epub 2019 May 3.
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.
本研究旨在通过数据驱动的方法综合以往生物滞留实验的研究成果，确定导致生物滞留污染物去除性能最佳的设计特征。通过 79 篇生物滞留文献建立了一个生物滞留数据库，其中包含 182 个生物滞留单元的设计特征及其相应的污染物去除效率数据。本研究采用非参数相关性分析、多元线性回归（MLR）和决策树分类器来探讨生物滞留设计特征与污染物去除效率之间的关系。非参数统计和 MLR 结果表明，生物滞留表面积、介质深度、内部蓄水层（IWS）的存在、土壤组成和植被覆盖均与污染物去除效率显著相关。在不同的气候和入流条件下，设计特征的影响有显著差异。决策树分类器显示，无植被的生物滞留单元，若采用沙质过滤介质，其总悬浮固体（TSS）的去除率通常高于 80%；生物滞留单元中有机物含量最低，介质深度大于 0.58 米，总氮（TN）的去除率通常高于 51%；无植被的生物滞留单元中有机物含量最低，且去除率高于 67%的总磷（TP）。决策树分类器在测试集的整体准确率约为 70%，可用于预测 TSS、TN 和 TP 的去除效率类别。本研究表明，数据驱动的方法为理解生物滞留设计特征与污染物去除性能之间的复杂关系提供了新视角。
