Real-time monitoring of contaminants of emerging concern in effluent wastewater with dynamic headspace gas chromatography mass spectrometry analysis
Year: 2025
Type: article
Abstract: • A DHS-GC–MS method was developed for real-time monitoring of CECs in wastewater. • Automated sample collection, preparation, and analysis implemented at a WWTP. • Non-target identification of CECs in effluent wastewater. • Identified semi-polar aromatics such as benzophenone and amberonne. Wastewater treatment plants are a major source of contamination, introducing pharmaceuticals, pesticides, and combustion products into aquatic environments. Currently, real-time monitoring of parameters such as nitrate and ammonia enables automatical control of these plants. In this paper we present the implementation and development of a dynamic headspace gas chromatography mass spectrometry method for real-time monitoring of contaminants of emerging concern in effluent wastewater. We observed a clear relation between polarity, water solubility, and analytical sensitivity, with the lowest sensitivity for the most polar compounds. Limits of quantification ranged from 15 to 3000 ng L -1 in full scan mode depending on the physico-chemical properties of the internal standards. Both the proprietary software MassHunter and open-source software PARADISe were used for non-target screening. Both software solutions were in general able to identify similar compound structures and sample time profiles using the specified data processing methods. The identified non-target compounds were typically semipolar aromatic compounds such as fuel-related compounds, transformation products from biological wastewater treatment, and compounds such as the UV blocker benzophenone and the odorant amberonne.
Source: Journal of Chromatography A
Authors: Mathias B. Jørgensen, Jimi B. Dan, Kristian R. Nielsen, Tore Vulpius, Jan H. Christensen
Institution: University of Copenhagen
Language: English
Subfield: Pollution
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,040
Funder: Innovationsfonden
Award: G5491865130
OpenAlex ID: W4408765496
