Membrane techniques constitute an interesting alternative to conventional activated sludge systems (CAS). In membrane bioreactors (MBR), the biomass separated on membranes is retained independently of sludge sedimentation properties. As a consequence, a high biomass concentration as well as low food to microorganisms ratio can be obtained. Moreover, the development of a characteristic activated sludge population is stimulated by the specific conditions prevailing in MBRs. In the study, the operation and treatment efficiency of the MBR and CAS processes were examined and compared. Simulation was performed with the use of GPS-X software. The effluent quality obtained for the MBR system was either better or comparable to that of CAS. The most significant difference concerned the elimination of total suspended solids, which amounted to 99.8% in the MBR. Regarding nutrients, a low concentration of total phosphorus in the effluent from CAS and MBR was obtained (0.67 gP m⁻³ and 0.50 gP m⁻³, respectively). Greater differences were achieved in the case of total nitrogen. Although almost complete nitrification took place in both systems, a lower concentration of nitrate in the effluent from MBR in comparison to CAS, i.e., 11.2 gN m⁻³ and 14.1 gN m⁻³, respectively, allowed us to obtain a higher removal of total nitrogen (80.8% and 76.1%, respectively).

Traffic-related emissions, apart from emissions from fuel combustion for heating purposes, significantly deteriorate air quality in cities. The above mainly concerns areas located close to busy traffic routes. According to epidemiological studies, traffic-related emissions have an adverse health effect. This specifically affects commuters (drivers and car passengers) as well as pedestrians. The aim of this study was to determine the variations of particle number and mass concentrations along a busy road in Lublin, Poland and their impact on the particle exposure for commuters and pedestrians. On-mute and fixed-site measurements were performed in the summer (June) with a focus on peak and off-peak traffic hours and road sections with low and high traffic intensity. During peak hours, the average number concentration of ultrafine particles (PN0.1) in the road section near 4-way traffic intersections (TIs) was about 2 times higher than during off-peak hours. The average mass concentration of fine particles (PM2.5) was also approximately twice as high than in off-peak hours. Similar relations were found for other measured aerosol particles as well as with respect to particle exposure. The obtained results indicate the need for further extended research on traffic-related emissions and exposure and the ways of limiting them.

Currently, e-noses are used for measuring odorous compounds at wastewater treatment plants. These devices mimic the mammalian olfactory sense, comprising an array of multiple non-specific gas sensors. An array of sensors creates a unique set of signals called a “gas fingerprint”, which enables it to differentiate between the analyzed samples of gas mixtures. However, appropriate advanced analyses of multidimensional data need to be conducted for this purpose. The failures of the wastewater treatment process are directly connected to the odor nuisance of bioreactors and are reflected in the level of pollution indicators. Thus, it can be assumed that using the appropriately selected methods of data analysis from a gas sensors array, it will be possible to distinguish and classify the operating states of bioreactors (i.e., phases of normal operation), as well as the occurrence of malfunction. This work focuses on developing a complete protocol for analyzing and interpreting multidimensional data from a gas sensor array measuring the properties of the air headspace in a bioreactor. These methods include dimensionality reduction and visualization in two-dimensional space using the principal component analysis (PCA) method, application of data clustering using an unsupervised method by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, and at the last stage, application of extra trees as a supervised machine learning method to achieve the best possible accuracy and precision in data classification.