Periodic Reporting for period 1 - ECO-SOS (Development of Emerging Contaminants – Hybrid Soft Sensor for on-line monitoring of contaminants of emerging concern in water)
Reporting period: 2021-06-01 to 2023-05-31
In 2019, the European Union (EU) conducted a thorough evaluation of the Water Framework Directive (WFD) and related directives, recognizing the importance of detecting CECs in European waters. To address these concerns, new EU recommendations are set to closely monitor water and wastewater. Because of that, the stricter requirements could be put in place to remove these harmful substances and protect human health and the environment. The new requirements present few major challenges. One of them is that current analytical methods are costly because CECs are present in extremely low concentrations, requiring very high precision for detection. Moreover, these methods are time consuming, because it involves collecting water samples from various sources, transporting them to laboratories, and conducting specialized tests using complex analytical methods. Additionally, we still don't fully understand how CECs are removed during water and wastewater treatments, which delays the optimization of the treatment process to effectively remove these contaminants.
To solve this problem, ECO SOS aimed to combine the experience in the development of real-time control and monitoring systems with knowledge of the analysis and removal of CECs during treatment processes by creating a soft sensor. They are also called virtual sensors or surrogate sensors. It is a smart estimation method of difficult and expensive parameters via cheap and widely available physical online sensors. Powerful statistical tools, artificial intelligence and machine learning concepts have proven to establish algorithms discovering hidden correlations among the two types of parameters. The hypothesis of this research project was that it can be possible for soft sensors to detect CECs in water by combining data from multiple sensors and advanced analytical methods (fluorescence, liquid chromatography, DNA sequencing). The action also included an industrial secondment in DOSCON SA, Norway as well as hands-on and transferable skills training.
Pharmaceuticals: five 3-days sampling campaigns were conducted from one of the Norwegian wastewater treatment plants. Physicochemical parameters were measured, along with samples' spectral properties and chemical composition. Through screening and target analysis, a larger number of substances were identified, including phthalates (plasticizers), pharmaceuticals, and bisphenol A. Most these compounds were present at very low levels, below the limit of detection. Diclofenac was detected in all the samples, both in influent and effluent of the wastewater treatment. However, the other pharmaceuticals were only found in the influent, which means they were successfully removed during the wastewater treatment process. An interesting finding was the high frequency of phthalates detection. Phthalates are chemicals commonly used as plasticizers. Among them, the most frequently detected were bis-methyl-glycol ester of phthalic acid (found in 94% of the samples), bis-ethyl-ester of phthalic acid (found in 69% of the samples), bis-isobutyl ester of phthalic acid (found in 33% of the samples), and bis-methyl-ester of phthalic acid (found in 33% of the samples). Currently, the data analysis is taking place, to discover any underlying correlations that can be the basis for soft-sensor development. The results will be published in open access peer-review journal.
PFAS: The work on PFAS focused mostly on the potential usage of spectral probes. The task has been proven challenging, as PFAS do absorb light as well as they do not have inert fluorescence. However, the presence of PFAS affected the fluorescence signal of humic acid which are commonly found in water. The increasing concentration of PFAS caused the decrease of humic acid signal. The tests with synthetic and natural samples were conducted). Currently, the data analysis of spectral responses is conducted, to discover any underlying correlations that can be the basis for soft-sensor development. The results will be published in open access peer-review journal.
To achieve the best results as well as to be more independent and mature researcher, the postdoctoral fellow, Agnieszka Cuprys, participated in 5 different conferences, 5 webinars/seminars, 3 workshops, and in various training sessions for big data analysis. Additionally, the project and its objective were presented during Norwegian Research Days 2021, European Researchers Night (2021), Science is wonderful! 2021 (1 webinar and 8 individual sessions), Science Festival Pint of Science Norway 2022, and other smaller events.