Self-Organizing Maps: An AI Tool for Identifying Unexpected Source Signatures in Non-Target Screening Analysis of Urban Wastewater by HPLC-HRMS.

(1) Background: Monitoring effluent in water treatment plants has a key role in identifying potential pollutants that might be released into the environment. A non-target analysis approach can be used for identifying unknown substances and source-specific multipollutant signatures. (2) Methods: Urban and industrial wastewater effluent were analyzed by HPLC-HRMS for non-target analysis. The anomalous infiltration of industrial wastewater into urban wastewater was investigated by analyzing the mass spectra data of "unknown common" compounds using principal component analysis (PCA) and the Self-Organizing Map (SOM) AI tool. The outcomes of the models were compared. (3) Results: The outlier detection was more straightforward in the SOM model than in the PCA one. The differences among the samples could not be completely perceived in the PCA model. Moreover, since PCA involves the calculation of new variables based on the original experimental ones, it is not possible to reconstruct a chromatogram that displays the recurring patterns in the urban WTP samples. This can be achieved using the SOM outcomes. (4) Conclusions: When comparing a large number of samples, the SOM AI tool is highly efficient in terms of calculation, visualization, and identifying outliers. Interpreting PCA visualization and outlier detection becomes challenging when dealing with a large sample size.

(Eco)toxicological maps: A new risk assessment method integrating traditional and in silico tools and its application in the Ledra River (Italy).

Contaminants giving rise to emerging concern like pharmaceuticals, personal care products, pesticides and Endocrine Disrupting Chemicals (EDCs) have been detected in wastewaters, as reported in the literature, but little is known about their (eco)toxicological effects and consequent human health impact. The present study aimed at overcoming this lack of information through the use of in silico methods integrated with traditional toxicological risk analysis. This is part of a pilot project involving the management of wastewater treatment plants in the Ledra River basin (Italy). We obtained data to work up a global risk assessment method combining the evaluations of health risks to humans and ecological receptors from chemical contaminants found in this specific area. The (eco)toxicological risk is expressed by a single numerical value, permitting the comparison of different sampling sites and the evaluation of future environmental and technical interventions.