Estrogenic Hormones in São Paulo Waters (Brazil) and Their Relationship with Environmental Variables and Sinapis alba Phytotoxicity
2020
Coelho, Lucia Helena Gomes | de Jesus, Tatiane Araújo | Kohatsu, Marcio Yukihiro | Poccia, Geovana Tognella | Chicarolli, Vitor | Helwig, Karin | Hunter, Colin | Roberts, Joanne | Teedon, Paul | Pahl, Ole
The present study evaluated the relationship between estrogenic hormone concentrations (17α-ethinylestradiol and 17β-estradiol) in surface waters in the Metropolitan Area of São Paulo (Brazil) and environmental variables. Four sampling stations were monitored ranging from a protected area to streams discharging human effluent in and around Billings Reservoir. Four sampling campaigns were carried out in each seasonal period: dry and wet. Samples for hormone analysis (in ng L⁻¹) were concentrated (1000×) using solid-phase extraction C₁₈ cartridges and analyzed by liquid chromatography coupled to quadrupole mass spectrometry detection, with 100 ng L⁻¹ limit of quantification. Water temperature, pH, electrical conductivity (EC), and total dissolved solids were determined in situ; total phosphorus and Sinapis alba bioassays were performed subsequently. Reservoir active capacity (AC) and precipitation were also obtained. Estrogenic hormone concentrations were always below limit of quantification at pristine site; at the other sampling stations, 17β-estradiol concentrations varied from below limit of quantification to 1720 ng L⁻¹ and 17α-ethinylestradiol from below limit of quantification to 1200 ng L⁻¹, with the highest concentrations found in the streams discharging into the reservoir. These streams showed higher Pearson’s correlation between 17α-ethinylestradiol, total phosphorus, and electrical conductivity when compared with reservoir stations. Germination index and EC presented negative correlation (Pearson’s r = − 0.61), denoting a phytotoxicity increase with EC increment. AC influenced the dilution of pollutants and showed negative correlations with total phosphorus (Pearson’s r = −0.56). These results highlight the relevance of including streams in water-monitoring programs, since they are important pollutants loads into watersheds.
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