Development of suspect and non-target screening methods for detection of organic contaminants in highway runoff and fish tissue with high-resolution time-of-flight mass spectrometry

Untreated urban stormwater runoff contributes to poor water quality in receiving waters. The ability to identify toxicants and other bioactive molecules responsible for observed adverse effects in a complex mixture of contaminants is critical to effective protection of ecosystem and human health, yet this is a challenging analytical task. The objective of this study was to develop analytical methods using liquid chromatography coupled to high-resolution quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) to detect organic contaminants in highway runoff and in runoff-exposed fish (adult coho salmon, Oncorhynchus kisutch). Processing of paired water and tissue samples facilitated contaminant prioritization and aided investigation of chemical bioavailability and uptake processes. Simple, minimal processing effort solid phase extraction (SPE) and elution procedures were optimized for water samples, and selective pressurized liquid extraction (SPLE) procedures were optimized for fish tissues. Extraction methods were compared by detection of non-target features and target compounds (e.g., quantity and peak area), while minimizing matrix interferences. Suspect screening techniques utilized in-house and commercial databases to prioritize high-risk detections for subsequent MS/MS characterization and identification efforts. Presumptive annotations were also screened with an in-house linear regression (log K-ow vs. retention time) to exclude isobaric compounds. Examples of confirmed identifications (via reference standard comparison) in highway runoff include ethoprophos, prometon, DEET, caffeine, cotinine, 4(or 5)-methyl-1H-methylbenzotriazole, and acetanilide. Acetanilide was also detected in runoff-exposed fish gill and liver samples. Further characterization of highway runoff and fish tissues (14 and 19 compounds, respectively with tentative identification by MS/MS data) suggests that many novel or poorly characterized organic contaminants exist in urban stormwater runoff and exposed biota.