Work

Leveraging Supervised Learning to Optimize Urban Greening Strategies for Combined Sewer Overflow Pollution Reduction

Year: 2026

Type: article

Abstract: Many cities adopt greening strategies to reduce contamination from combined sewer overflows (CSOs). Nonetheless, quantifying the impact of urban greening on CSO-affected water quality at the city scale remains challenging. To address this challenge, this work leveraged supervised learning to link water swimmability with the greening of a CSO shed (the drainage area of a CSO outfall), using New York City (NYC) as a case study. Random forest classification models were built to predict water swimmability after rainfall at 46 sites in NYC water bodies impacted by CSOs. A 14-feature model (AUROC =0.81, accuracy = 0.78) revealed that greening improved local water quality. However, water flow speed, antecedent rain depth, and CSO shed area were also influential. A simplified four-feature model (AUROC = 0.8, accuracy = 0.75) explored links between levels of greening and the probability of non-swimmable waters (Pns) following different 18 h rainfall depths. Increased greening was found to be most impactful in reducing Pns for CSO sheds discharging to water bodies with flow speeds < 6 cm/s. For CSO sheds discharging to water bodies with flow speeds ≥ 14.7 cm/s, urban greening had no impact on Pns. The work illustrates the utility of supervised learning in supporting citywide decisions regarding urban greening investments.

Source: Water

DOI: 10.3390/w18090994

Authors: Siyan Wang, Haokai Zhao, Gregory Yetman, Wade R. McGillis, Patricia J. Culligan

Institutions: Columbia University, Massachusetts Institute of Technology, University of Notre Dame

Language: English

Topic: Urban Stormwater Management Solutions

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Clean water and sanitation, Sustainable cities and communities, Climate action, Life on land

Open Access status: gold

Estimated APC paid: $3,137