Wastewater (WW) from urban and industrial activities is often contaminated with microorganisms and chemical pollutants. To reduce the concentration of microorganisms in WW to levels comparable to those found in natural waters, the sewage effluent is usually subjected to disinfection with chlorine, ozone, or ultraviolet light, which may lead to the formation of toxic products and contribute to the selection of resistant genes. Moreover, the changing patterns of infectious diseases and the emerging of multidrug resistant microbial strains entail the development of new technologies for WW decontamination. Microbial photodynamic inactivation (PDI) with photosensitizers, oxygen, and visible light has demonstrated to be effective in the inactivation of microorganisms via photogeneration of reactive oxygen species able to induce microbial damage at the external structures level. The promising results of PDI suggest that this principle can be applied to WW treatment to inactivate microorganisms but also to photodegrade chemical pollutants. The aim of this study was to assess the applicability of PDI for the microbial and chemical decontamination of secondarily treated WW. To evaluate the efficiency of bacterial inactivation in WW, experiments were done in both phosphate buffer saline (PBS) and filtered WW with the bioluminescent Escherichia coli, using small and large volumes of WW. The potential of PDI to inactivate the native bacteria (E. coli and Enterococcus) present in WW was tested and assays without the adding of bacteria to the WW were performed. It was also tested if the same PDI protocol was able to induce phototransformation of phenol. The cationic porphyrin 5,10,15,20-tetrakis(1-methylpyridinium-4-yl)porphyrin tetra-iodide (Tetra-Py + -Me) was shown to be effective against both bacterial groups representing both Gram-negative and Gram-positive bacteria used as microbiological parameters to instigate water quality and even showing the power to photooxidate organic compounds. As the photosensitizer when immobilized on solid matrixes can be easily removed, recovered, and reused, an effective, less-expensive, easy-applicable, and environmentally friendly technology can be applied to treat WW, inactivating microorganisms and degrading chemical contaminants at the same time.

This paper explores robust unconditional and conditional nonparametric approaches to support performance evaluation in problematic samples. Real-world assessments often face critical problems regarding available data, as samples may be relatively small, with high variability in the magnitude of the observed indicators and contextual conditions. This paper explores the possibility of mitigating the impact of potential outlier observations and variability in small samples using a robust nonparametric approach. This approach has the advantage of avoiding unnecessary loss of relevant information, retaining all the decision-making units of the original sample. We devote particular attention to identifying peers and targets in the robust nonparametric approach to guide improvements for underperforming units. The results are compared with a traditional deterministic approach to highlight the proposed method's benefits for problematic samples. This framework's applicability in internal benchmarking studies is illustrated with a case study within the wastewater treatment industry in Portugal.

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