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(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. [ABSTRACT FROM AUTHOR]

Spatiotemporal changes in the concentration of UV filters were investigated along the shore according to increasing distance from breakwaters, from the shoreline, as well as according to seasonality in three locations of different anthropogenic pressures, involving those from cosmetic products being released during touristic activity. Nine organic UV filters (benzophenone-1 (BP-1), benzophenone-2 (BP-2), benzophenone-3 (BP-3), octocrylene (OCR), 4-methoxy benzylidene camphor (4-MBC), ethylhexyl methoxycinnamate (EHMC), ethylhexyl salicylate (EHS), homosalate (HMS), and butyl methoxydibenzoylmethane (BMDM)) were determined in core sediments, and the range of determined concentrations above the limit of quantification was between 19.2 ng·kg−1 d.w. (HMS) and 539.5 μg·kg−1 d.w. (4-MBC). Unexpectedly, contrary to the level of anthropogenic pressure, the concentrations of four (BP-1, BP-2, BP-3, OCR) UV filters decreased in the following order: Darłówko > Ustka > Rowy. Higher concentrations of BP-1, BP-2, BP-3, and OCR were determined in spring than in summer and autumn. The maximal concentration of HMS and EHMC/EHS was found in the summer and in the autumn, respectively. BMDM was determined occasionally only in two samples collected in Ustka. The higher maximal concentration range of all UV filters was determined in core sediments taken from the eastern (539.5 μg·kg−1 d.w.) rather than from the western (11.3 μg·kg−1 d.w.) parts of the beaches. According to increasing distance from the breakwaters, higher concentrations of UV filters were determined in sites located up to 100 m away in all locations and seasons. Spatial variation in the concentration of UV filters was observed in profiles perpendicular to the water line. Typically, higher concentrations were determined at sites having contact with water, although incidentally, high concentrations were also noticed at sites located further into the beach. The Polish coast of the Baltic Sea is not free from organic UV filters, and expectations concerning the abundance of UV filters in a given location are far from recorded data due to the impact of hydro-technical treatments (i.e., stony and wooden breakwaters, artificial reefs, nourishment) and coastal littoral drift. [ABSTRACT FROM AUTHOR]

Byline: Aleksander M. Astel Two data sets were apportioned by the use of several models (chemical mass balance (CMB), principal components analysis-absolute principal components scores (PCA-APCS), positive matrix factorisation (PMF) and UNMIX). Source apportionment were results derived from the CMB modelling applied to the data from Vienna point traffic source, paved road and crustal dust as the dominant contributors to the PM10 total mass. The major contributors of PM10 aerosol mass in Cracow with respect to PCA-APCS, PMF and UNMIX applied include: boiler, stoves and industrial power plants (coal), secondary emission (including coal), gasoline, soil and road dust.