New Paper Published in Envrionmental Science & Technology

In a new paper published in Environmental Science & Technology, ERG members Bowen Zhou, Chris T. Parsons and Philippe Van Cappellen compare the effects of traditional and low-impact development (LID) stormwater best management practices (BMPs) in controlling urban phosphorus (P) export using the data extracted from the International Stormwater BMP Database. They then develop machine learning models to predict the reduction and enrichment factors of surface runoff concentrations and loadings of total P (TP) and soluble reactive P (SRP) for the different categories of BMP systems. They find that LIDs generally enrich TP and SRP concentrations in stormwater surface outflow and yield poorer P runoff load control relative to traditional BMPs. Machine learning modeling further indicates that LIDs are more likely to enrich surface runoff SRP under certain climate and watershed characteristics. This study implies that stormwater BMPs do not universally attenuate urban P export and that preferentially implementing LIDs over traditional BMPs may increase TP and SRP export to receiving freshwater bodies, hence magnifying eutrophication risks.