Direct Measurement of Organic Micropollutants in Water and Wastewater Using Fluorescence Spectroscopy

Abstract

Quantifying organic micropollutants (OMPs) in aquatic environments and assessing their removal by water treatment requires expensive and time-consuming analyses typically using liquid chromatographic separation and tandem mass spectrometry (LC-MS/MS). In this study, we evaluated the potential for detecting fluorescent OMPs via spectroscopy, which is cheap, rapid, and widely accessible. The method involved using a priori PARAFAC models to eliminate interfering background fluorescence emitted by naturally occurring dissolved organic matter. Of 20 screened pharmaceutical OMPs, three (ciprofloxacin, naproxen, and zolpidem) with calculated fluorescence quantum yields 0.14, 0.21, and 0.71, respectively, could be quantified in the low μg L −1 range when added alone or in combination to water samples without any sample pretreatment other than filtration and pH adjustment. Limits of detection for all three OMPs were 1.0−3.3 μg L −1 in surface waters, while in wastewater, they were 0.6− 9.0 μg L −1 for ciprofloxacin and naproxen and 1.0−2.6 μg L −1 for zolpidem. Given the high cost of pharmaceutical analyses and widespread availability of fluorometers, the new approach will improve access to rapid and cost-effective results by supporting dataintensive lab-scale studies, wherein the types of OMPs studied and their concentration ranges are under the control of the analyst.

In-situ optical water quality monitoring sensors—applications, challenges, and future opportunities

“…They also recommended using portable fluorometers for in-situ tracking of ECs by proxy associations, especially in waters with high background CDOM and TLF concentrations, such as tertiary wastewater, and during events of high EC contamination, such as spills and leakages. Paradina-Fernández et al (2023) demonstrated the very-low detection limits achieved by a benchtop fluorometer and PARAFAC modeling while quantifying organic micropollutants (e.g., pharmaceuticals) in surface and wastewater in Sweden. Moshtaghi et al (2021) used experimental VIS-IR reflectance data to validate the algorithms used to remotely detect marine microplastics.…”

“…Future sensor networks (i.e., node locations) should, whenever possible, align with statutory monitoring locations where manual sampling occurs routinely (Kinar and Brinkmann, 2022;Gaviria Salazar et al, 2023). This will ensure coupled datasets of laboratory and in-situ measurements, which are of comparable quality and can lead to the development of more rigorous calibration algorithms and proxy models (Ighalo et al, 2021;Paradina-Fernández et al, 2023). With the potential for more measurement data from more locations, there is a clear need for the community to embrace emerging artificial intelligence and machine learning (AI-ML) approaches to aid calibration, reduce the signal-to-noise ratio, and capture and correct drift and outliers.…”

Coupling pathway prediction and fluorescence spectroscopy to assess the impact of auxiliary substrates on micropollutant biodegradation

“…PARAFAC analysis allows the decomposition of a sample's bulk fluorescence signal into its underlying independent components (Murphy et al, 2013). When working with chemically identifiable compounds and mixtures thereof, the approach is further capable of isolating the autofluorescence of the individual substrates from the background media and allows tracing their development at nanomolar concentrations (Paradina-Fern andez et al, 2023;Sjöstedt et al, 2022).…”

Dissolved Organic Matter Behaviour by Conventional Treatments of a Drinking Water Plant: Controlling Its Changes with EEM-PARAFAC

“…Due to these reasons, PARAFAC is the most employed algorithm to analyse fluorescent organic matter within water treatment plants; the information about cases of application can be find elsewhere [16][17][18]. Currently, EEM-PARAFAC is still gaining momentum with its application with DOM real-time continuous monitoring [19][20][21], combination with machine learning [22,23], or even its detection of fluorescent emerging contaminants in wastewater effluents [24]. In this sense, our group has thoroughly explored, with success, the use of EEM-PARAFAC to visualise oxidative changes in fluoroquinolones (antibiotics), also being able to track the formation of fluorescent oxidation by-products and estimate its molecular structure [25][26][27].…”