Chemical Fingerprinting of Urban Runoff Using a Combined Iterative DDA and DIA Workflow

High-resolution mass spectrometry (HRMS) is commonly used for non-target screening (NTS) of environmental samples, but identifying unknown pollutants remains difficult because of complex sample matrices and limited reference data. This study presents a workflow combining iterative data-dependent acquisition (DDA) with data-independent acquisition (DIA) to enhance compound annotation. Pooled samples were analyzed using iterative DDA, annotated with Sirius, and matched to DIA features in individual samples. Of the 44 target compounds in runoff, 50% were correctly identified as the top Sirius hit, with an additional 18% recovered through manual review—outperforming earlier studies using single-injection DDA. The method tentatively identified several tire-related compounds in urban runoff, including two novel findings in environmental samples.