Effect-directed analysis of endocrine and neurotoxic effects in stormwater depending discharges

Abstract

The investigation of pollutant inputs via stormwater runoff and subsequent effects in receiving waters is becoming increasingly urgent in view of climate change with accompanying extreme weather situations such as heavy rainfall events. In this study, two sampling areas, one urban and one rural but dominated by a highway, were investigated using effect-directed analysis to identify endocrine and neurotoxic effects and potentially responsible substances in stormwater structures and receiving waters. For this purpose, a transgenic yeast cell assay for the simultaneous detection of estrogenic, androgenic, and progestogenic effects (YMEES) was performed directly on high-performance thin-layer chromatography (HPTLC) plates. Concomitantly, estrogens were analyzed by GC–MS/MS and other micropollutants typical for wastewater and stormwater by LC-MS/MS. Discharges from the combined sewer overflow (CSO) contribute a large portion of the endocrine load to the studied water body, even surpassing the load from a nearby wastewater treatment plant (WWTP). An effect pattern similar to the CSO sample was shown in the receiving water after the CSO with lower intensities, consisting of an estrogenic, androgenic, and progestogenic effect. In contrast, after the WWTP, only one estrogenic effect with a lower intensity was detected. Concentrations of E1, 17α-E2, 17β-E2, EE2, and E3 in the CSO sample were 2000, 410, 1100, 560, and 2700 pg/L, respectively. HPTLC-YMEES and GC–MS/MS complement each other very well and help to elucidate endocrine stresses. An Acetylcholinesterase (AChE) inhibitory effect could not be assigned to a causative compound by suspect and non-target analysis using LC-HRMS. However, the workflow showed how information from HPTLC separation, effect-based methods, and other meta-information on the sampling area and substance properties can contribute to an identification of effect-responsible substances. Overall, the study demonstrated that effect-based methods in combination with HPTLC and instrumental analysis can be implemented to investigate pollution by stormwater run-off particularly regarding heavy rain events due to climate change.