Retrospective HRMS Screening and Dedicated Target Analysis Reveal a Wide Exposure to Pyrrolizidine Alkaloids in Small Streams.
Barbara F GünthardtFelix E WettsteinJuliane HollenderHeinz P SingerJana HärriMartin ScheringerKonrad HungerbühlerThomas Daniel BucheliPublished in: Environmental science & technology (2020)
Pyrrolizidine alkaloids (PAs) are found to be toxic pollutants emitted into the environment by numerous plant species, resulting in contamination. In this article, we investigate the occurrence of PAs in the aquatic environment of small Swiss streams combining two different approaches. Pyrrolizidine alkaloids (PAs) are toxic secondary metabolites produced by numerous plant species. Although they were classified as persistent and mobile and found to be emitted into the environment, their occurrence in surface waters is largely unknown. Therefore, we performed a retrospective data analysis of two extensive HRMS campaigns each covering five small streams in Switzerland over the growing season. All sites were contaminated with up to 12 individual PAs and temporal detection frequencies between 36 and 87%. Individual PAs were in the low ng/L range, but rain-induced maximal total PA concentrations reached almost 100 ng/L in late spring and summer. Through PA patterns in water and plants, several species were tentatively identified as the source of contamination, with Senecio spp. and Echium vulgare being the most important. Additionally, two streams were monitored, and PAs were quantified with a newly developed, faster, and more sensitive LC-MS/MS method to distinguish different plant-based and indirect human PA sources. A distinctly different PA fingerprint in aqueous plant extracts pointed to invasive Senecio inaequidens as the main source of the surface water contamination at these sites. Results indicate that PA loads may increase if invasive species are sufficiently abundant.
Keyphrases
- risk assessment
- drinking water
- heavy metals
- human health
- health risk
- endothelial cells
- ms ms
- ionic liquid
- high glucose
- heart rate
- gene expression
- genome wide
- big data
- oxidative stress
- resistance training
- single cell
- deep learning
- induced pluripotent stem cells
- high resolution mass spectrometry
- quantum dots
- gas chromatography
- artificial intelligence