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

Runzi Wang1, Xuewen Zhang2, Ming-Han Li1

  • 1Michigan State University, 552 W Circle Dr, East Lansing, MI, 48823, United States.

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Summary
This summary is machine-generated.

Bioretention design features like media depth and vegetation significantly impact pollutant removal. A data-driven approach identified key factors for optimizing bioretention systems for cleaner water.

Keywords:
Decision treeLow impact developmentMachine learningRain gardenStormwater managementWater quality

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Area of Science:

  • Environmental Engineering
  • Water Quality Management
  • Sustainable Urban Drainage Systems

Background:

  • Bioretention systems are crucial for managing stormwater runoff and improving water quality.
  • Optimizing bioretention design is essential for maximizing pollutant removal efficiency.

Purpose of the Study:

  • To synthesize research on bioretention performance using a data-driven approach.
  • To identify key design features influencing pollutant removal in bioretention cells.

Main Methods:

  • Compiled a database from 79 publications, including 182 bioretention cell records.
  • Applied non-parametric correlation, multiple linear regression (MLR), and decision tree classifiers.
  • Analyzed relationships between design features and pollutant removal efficiencies (TSS, TN, TP).

Main Results:

  • Surface area, media depth, internal water storage (IWS) layer, soil composition, and vegetation cover significantly correlate with pollutant removal.
  • Specific design configurations were linked to high removal rates for TSS (>80%), TN (>51%), and TP (>67%).
  • Model accuracy for predicting removal efficiency classes was approximately 70%.

Conclusions:

  • A data-driven approach effectively reveals complex relationships between bioretention design and performance.
  • Design features must be considered alongside climate and inflow conditions for optimal pollutant removal.
  • Findings provide insights for designing more effective bioretention systems.