Naturally, microorganisms decompose the organic material existing in nature, both in the presence or absence of oxygen. The majority of materials such as poisonous chemical compounds, heavy metals, would prevent the treatment process from taking place, lead to the entry of these contaminants into the environment results in the emergence of numerous diseases. In the present study, using the TOXChem4.1 simulation model, attempts were made to simulate a wastewater treatment plant and then assess the dispersions of contaminants including 1,2-Dimethylnaphthalene, 1,3-Dinitropyrene, 1,6-Dimethylnaphthalene, 1,6-Dinitropyrene, and 17a-ethinylestradiol (EE2) in concentrations of a common scenario. The results of computer simulations showed that the EE2 contaminant is of the highest percentage of decomposition among others, due to its wider chemical structure. Consequently, it is clear that such contaminant is of the highest mass in the sludge exiting the treatment plant. In addition, the results of the simulations demonstrated that the highest volumes of gaseous pollutants take place in the modulation and initial sedimentation units.

One of the most important emerging pollutants is pharmaceutical active compounds, which may be responsible, for example, for ecotoxicological changes and microbiological resistance. Wastewater treatment plants are not adequately equipped to remove all of the emerging pollutants contained in the wastewater. The ultrafiltration process has been proven to be effective in traditional wastewater treatment, so it is important to assess the performance of such a technique in the partial elimination of pharmaceutical compounds to avoid contamination. In this work, an assessment of ultrafiltration process operating conditions for eliminating six pharmaceutical compounds: Ibuprofen, Acetaminophen, Naproxen, Diclofenac, Caffeine, and trimethoprim, present in different types of water is performed. Experimental design is a systematic and structured approach to conducting experiments, and its application can significantly improve the study of membrane technology, reducing the number of assays necessary to obtain meaningful results. The statistical principles application ensures that the results obtained are reliable and representative of the true effects of the variables being studied. Its application helps to obtain valid conclusions from the data and provides a solid basis for making decisions or recommendations regarding the tests and variables to take into account. Membrane processes can involve complex interactions between several factors. Experimental design helps to identify and understand these interactions, allowing researchers to discern the combined effects of different variables. This is crucial to accurately predict and optimize membrane performance.In this work, ANOVA analysis has been carried out in order to determine the influence of membrane cut-off, solution pH, and feed concentration, as well as their interactions, in permeate flux and the rejection index. The results obtained show similar behavior for Ibuprofen, Naproxen, Diclofenac, and Trimethoprim, being the pH the most important factor. However, no significant factors were found for the acetaminophen and the Caffeine.