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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.

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Reduction of the greenhouse effect is primarily associated with the reduction of greenhouse gas (GHG) emissions. Carbon dioxide (CO2) is one of the gases that increases the greenhouse effect - it is responsible for about half of the greenhouse effect. Significant sources of CO2 are wastewater treatment plants (WWTPs) and waste management, with about 3% contribution to global emissions. CO2 is produced mainly in the aerobic stage of wastewater purification and is a consequence of activated sludge activity. Although the roles of activated sludge components in the purification process have been studied quite well, their quantitative contribution to CO2 emissions is still unknown. The emission of CO2 caused by prokaryotes and eukaryotes over the course of a year (taking into account subsequent seasons) in model sequencing batch reactors (SBR) is presented in this study. In this work, for the first time, we aimed to quantify this contribution of eukaryotic organisms to total CO2 emissions during the WWTP process. It is of the order of several or more ppm. The contribution of CO2 produced by different components of activated sludge in WWTPs can improve estimation of the emissions of GHGs in this area of human activity.

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This work presents the results of studies on the impact of spent drilling fluids cotreated with municipal wastewater on the rate of the wastewater treatment process and the structure of the community of eukaryotic organisms inhabiting an activated sludge. The studies were conducted under laboratory conditions in sequencing batch reactors. The effect of added polymer-potassium drilling fluid (DF1) and polymer drilling fluid (DF2) at dosages of 1 and 3% of wastewater volume on the rate of removal of total suspended solids, turbidity, chemical oxygen demand, and the content of total and ammonium nitrogen were analyzed, taking into account the values of these parameters measured at the end of each operating cycle. In addition to the impacts on the aforementioned physicochemical indices, the influence of drilling fluid on the biomass of various groups of eukaryotes in activated sludge was analyzed. The impact of the drilling fluid was highly dependent on its type and dosage. A noticeable slowdown in the rate of the wastewater treatment process and a negative effect on the organisms were observed after the addition of DF2. This effect intensified after an increase in fluid dose. However, no statistically significant negative changes were observed after the introduction of DF1. Conversely, the removal rate of some of the analyzed pollutant increased.

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From the sanitary and epidemiological aspects, information concerning the developmental forms of intestinal parasites, especially the eggs of helminths present in our environment in: water, soil, sandpits, sewage sludge, crops watered with wastewater are very important. The methods described in the relevant literature may be classified in various ways, primarily according to the methodology of the preparation of samples from environmental matrices prepared for analysis, and the sole methods of counting and chambers/instruments used for this purpose. In addition, there is a possibility to perform the classification of the research methods analyzed from the aspect of the method and time of identification of the individuals counted, or the necessity for staining them. Standard methods for identification of helminths' eggs from environmental matrices are usually characterized by low efficiency, i.e. from 30% to approximately 80%. The efficiency of the method applied may be measured in a dual way, either by using the method of internal standard or the 'Split/Spike' method. While measuring simultaneously in an examined object the efficiency of the method and the number of eggs, the 'actual' number of eggs may be calculated by multiplying the obtained value of the discovered eggs of helminths by inverse efficiency.

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Spent drilling muds are the liquid residues of rock drilling operations. Due to a high concentration of suspended solids and potentially detrimental chemical properties, they can negatively affect microorganisms participating in wastewater treatment processes. We evaluated the addition of a potassium-polymer drilling fluid (DF) to activated sludge in laboratory sequencing batch reactors (SBRs) for municipal wastewater treatment. Ciliate assemblage, the most dynamic component of eukaryotes in activated sludge, and which is highly sensitive to changes in the system, was evaluated. The average ciliate abundance dropped by about 51% (SBR 2; 1% DF added) and 33% (SBR 3; 3% DF added) in comparison to the control (SBR 1; wastewater only). A decrease in the total number of ciliate species during the experiment was observed, from 25 to 24 in SBR 2 and from 17 to 13 in SBR 3. Moreover, a drop in the number of dominant (>100 individuals mL) ciliate species was observed during the experiment-from eight in the control to five in SBR 2 and four in SBR 3-signaling noticeable changes in the quantitative structure of ciliate species. The species analyzed showed different responses to DF addition. The most sensitive was , which is bacteriovorus. In contrast, two predators, and , showed no reaction to DF addition. Our results indicate that addition of potassium-polymer DF, in doses of 1 to 3% of the treated wastewater volume, had no toxic effects on ciliates, but qualitative and quantitative changes in their community were observed.

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The possibility of distinguishing different soil moisture levels by electronic nose (e-nose) was studied. Ten arable soils of various types were investigated. The measurements were performed for air-dry (AD) soils stored for one year, then moistened to field water capacity and finally dried within a period of 180 days. The volatile fingerprints changed during the course of drying. At the end of the drying cycle, the fingerprints were similar to those of the initial AD soils. Principal component analysis (PCA) and artificial neural network (ANN) analysis showed that e-nose results can be used to distinguish soil moisture. It was also shown that different soils can give different e-nose signals at the same moistures.

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A gas sensor array consisting of eight metal oxide semiconductor (MOS) type gas sensors was evaluated for its ability for assessment of the selected wastewater parameters. Municipal wastewater was collected in a wastewater treatment plant (WWTP) in a primary sedimentation tank and was treated in a laboratory-scale sequential batch reactor (SBR). A comparison of the gas sensor array (electronic nose) response to the standard physical-chemical parameters of treated wastewater was performed. To analyze the measurement results, artificial neural networks were used. E-nose-gas sensors array and artificial neural networks proved to be a suitable method for the monitoring of treated wastewater quality. Neural networks used for data validation showed high correlation between the electronic nose readouts and: (I) chemical oxygen demand (COD) (r = 0.988); (II) total suspended solids (TSS) (r = 0.938); (III) turbidity (r = 0.940); (IV) pH (r = 0.554); (V) nitrogen compounds: N-NO3 (r = 0.958), N-NO2 (r = 0.869) and N-NH3 (r = 0.978); (VI) and volatile organic compounds (VOC) (r = 0.987). Good correlation of the abovementioned parameters are observed under stable treatment conditions in a laboratory batch reactor.