Beyond Bulk Analysis: Particle Size-Resolved Fingerprinting in Urban Stormwater

Particulates play a critical role in urban stormwater pollution, yet their accurate source apportionment remains challenging due to the neglect of particle size effects in conventional bulk methods. This study presents a novel particle size-resolved fingerprinting approach that significantly enhances source tracking accuracy. For particulates collected from potential sources and runoff, a rigorous protocol was applied, involving stable fingerprint selection, Kruskal-Wallis H test, and Discriminant Function Analysis to identify optimal fingerprint combinations with source contributions quantified using the Bayesian mixing model MixSIAR. Results demonstrated that particle size-resolved approach dramatically increases the correct source discrimination, a 27.7∼31.1%p improvement over the conventional bulk method. Additionally, distinct source patterns were identified for fine (<44 μm) and coarse (>44 μm) particulates with fine particulates predominantly originating from road (29.25%) and parking lot deposited sediments (23.77%) and urban soil (26.22%), while coarse particulates are dominated by roof deposited sediments (34.83%) and urban soil (36.44%). The novelty in this study is the proposed methodology for adopting a particle size-resolved fingerprinting approach, which is generic and significantly more accurate when compared to the conventional composite fingerprinting methods with their attendant limitations. The study outcomes will contribute to accurate source apportionment essential for targeted pollution control strategies against specific and critical particulate fractions in urban stormwater.