Classifying pollutant flush signals in stormwater using functional data analysis on TSS MV curves

Area of Science:

• Environmental Science
• Hydrology
• Water Quality Management

Background:

• Stormwater pollution is a significant environmental concern, with pollutant flushes during rain events contributing substantially to the total load.
• Existing research often focuses narrowly on the 'first flush' phenomenon or uses predefined categories, limiting a comprehensive understanding of flush dynamics.
• Effective stormwater management strategies require a nuanced understanding of how pollutants are transported and released during rainfall events.

Purpose of the Study:

• To develop a data-driven methodology for classifying stormwater pollutant flushes based on Mass Volume (MV) curves for Total Suspended Solids (TSS).
• To explore the relationship between different classes of MV curves and rainfall characteristics.
• To introduce Functional Data Analysis (FDA) as a novel approach for analyzing and categorizing pollutant flush signals.

Main Methods:

• Application of Functional Data Analysis (FDA) to analyze the dynamics of Mass Volume (MV) curves for TSS.
• Utilized two large datasets: 343 measured and 915 modeled stormwater events.
• Employed a priori clustering to explore links between MV curve classes and combinations of rainfall characteristics.

Main Results:

• The study identified distinct classes of MV curves tailored to specific monitoring locations.
• A moderate success rate (23-63%) was achieved in assigning events to classes using combinations of MV curve clustering and rainfall data.
• Global rainfall characteristics were found to influence pollutant flushes but were insufficient as sole predictors of flush phenomena.

Conclusions:

• Functional Data Analysis (FDA) offers a promising new methodology for classifying and understanding stormwater pollutant flushes.
• Additional explanatory variables beyond global rainfall characteristics are needed for robust classification of MV curves.
• The findings support the development of more precise stormwater control measures informed by location-specific flush dynamics.