Monitoring survey of caffeine in surface waters (Lis River) and wastewaters located at Leiria Town in Portugal
Investigation during 11-month period was performed to study the presence of caffeine in the Lis River in Leiria Town in Portugal, and a monitoring during 9-month period was realized to check the contribution of the human pollution of two wastewater treatment plants (WWTPs) that discharge their effluents to the studied river. The samples were collected in five sampling points along the river and in two influents and two effluents of the studied WWTPs. Caffeine was detected in all ninety-one collected samples. The caffeine concentration ranged from 25.3 to 321 ng/L in the river samples, from 112 to 1927 ng/L in the WWTP effluents, and from 9478 to 83,901 ng/L in the WWTP influents. The highest concentration in the river was detected in the two sampling points located after the effluent discharge points and reached 315 and 321 ng/L. Risk assessment was performed for three trophic levels using the risk quotient calculation and revealed that caffeine do not cause toxic effect on Daphnia magna and on fish but could be possibly toxic to algae. The results proved that caffeine can be an effective indicator of human-born pollution.
KeywordsCaffeine Liquid chromatography Mass spectrometry Monitoring study Risk assessment
The authors received funding from the EU and FCT/UEFISCDI/FORMAS for funding, in the frame of the collaborative international consortium REWATER financed under the ERA-NET Cofund WaterWorks2015 Call. This ERA-NET is an integral part of the 2016 Joint Activities developed by the Water Challenges for a Changing World Joint Programme Initiative (Water JPI). This work was also supported by UID/QUI/50006/2019 with funding from FCT/MCTES through national funds.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 2002/657/EC (2002) European Commission 2002/657/EC. Commission decision of 12 August 2002 implementing council directive 96/23/EC concerning the performance of analytical methods and the interpretation of results (notified under document number C(2002) 3044, L221/8. Available at: http://faolex.fao.org/docs/pdf/eur49615.pdf. Accessed date: August 2017
- Boisvert M, Fayad P, Sauvé S (2012) Development of a new multi-residue laser diode thermal desorption atmospheric pressure chemical ionization tandem mass spectrometry method for the detection and quantification of pesticides and pharmaceuticals in wastewater samples. Anal Chim Acta 754:75–82CrossRefGoogle Scholar
- ECOSAR Ecological Structure Activity Relationships (ECOSAR) (n.d.) Predictive ModelGoogle Scholar
- EMEA (2006) Guideline on the environmental risk assessment of medicinal products for human use. European Medicines Agency Pre-Authorisation Evaluation of Medicines for Human Use. London, 01 June 2006. Doc. Ref. EMEA/CHMP/SWP/4447/00 corr 2. Available at: http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2009/10/WC500003978.pdf. Accessed date: June 2017)
- Florescu D, Ionete RE, Sandru C, Iordache A, Culea M (2011) The influence of pollution monitoring parameters in characterizing the surface water quality from Romania southern area. Rom J Phys 56:1001–1010Google Scholar
- LeiriaMunicipality (n.d.) Available http://www.cm-leiria.pt/. Accessed 14 Dec 2017
- Loos R, Wollgast J, Huber T, Hanke G (2007) Polar herbicides, pharmaceutical products, perfluorooctanesulfonate (PFOS), perfluorooctanoate (PFOA), and nonylphenol and its carboxylates and ethoxylates in surface and tap waters around Lake Maggiore in Northern Italy. Anal Bioanal Chem 387:1469–1478CrossRefGoogle Scholar
- Peteffi GP, Fleck JD, Kael IM, Girardi V, Bündchen R, Krajeski DM, Demoliner M, Silva FP, daRosa DC, Antunes MV, Linden R (2018) Caffeine levels as a predictor of Human mastadenovirus presence in surface waters-a case study in the Sinos River basin-Brazil. Environ Sci Pollut Res Int 25(16):15774–15784. https://doi.org/10.1007/s11356-018-1649-3 CrossRefGoogle Scholar
- R Development Core Team (2016) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
- Yang Y-Y, Liu W-R, Liu Y-S, Zhao J-L, Zhang Q-Q, Zhang M, Zhang J-N, Jiang Y-X, Zhang L-J, Ying G-G (2017) Suitability of pharmaceuticals and personal care products (PPCPs) and artificial sweeteners (ASs) as wastewater indicators in the Pearl River Delta, South China. Sci Total Environ 590–591:611–619CrossRefGoogle Scholar