One of the important factors determining the biochemical processes in bioreactors is the quality of the wastewater inflow to the wastewater treatment plant (WWTP). Information on the quality of wastewater, sufficiently in advance, makes it possible to properly select bioreactor settings to obtain optimal process conditions. This paper presents the use of classification models to predict the variability of wastewater quality at the inflow to wastewater treatment plants, the values of which depend only on the amount of inflowing wastewater. The methodology of an expert system to predict selected indicators of wastewater quality at the inflow to the treatment plant (biochemical oxygen demand, chemical oxygen demand, total suspended solids, and ammonium nitrogen) on the example of a selected WWTP—Sitkówka Nowiny, was presented. In the considered system concept, a division of the values of measured wastewater quality indices into lower (reduced values of indicators in relation to average), average (typical and most common values), and upper (increased values) were adopted. On the basis of the calculations performed, it was found that the values of the selected wastewater quality indicators can be identified with sufficient accuracy by means of the determined statistical models based on the support vector machines and boosted trees methods.

One of the most dangerous pests of cereals is Zabrus tenebrioides and, in Poland, it is becoming a serious pest. Entomopathogenic nematodes (EPNs) seem to be a very promising, biological control agent for this pest. Native EPN populations are well adapted to local environmental conditions. The current study characterized three Polish isolates of the EPN Steinernema feltiae, which differed in their effectiveness against Z. tenebrioides. In the field, isolate iso1Lon reduced the pest population by 37%, compared with 30% by isolate iso1Dan and 0% by the iso1Obl isolate; the number of plants damaged by Z. tenebrioides in the presence of the different isolates reflected the results in terms of the decrease in pest population size. After incubation in the soil for 60 days, recovered EPN juveniles of all three isolates were able to infect 93–100% of the test insects, with isolate iso1Obl again showing the lowest effectiveness. The juveniles of isolate iso1Obl were also morphometrically distinct from the other two isolates, as revealed by principal component analysis (PCA), which helped to distinguish the EPN isolates. These findings showed the value of using locally adapted isolates of EPNs; two of the three isolates randomly selected from Polish soil outperformed a commercial population of S. feltiae.

An innovative method was proposed to facilitate the analyses of meteorological conditions and selected air pollution indices’ influence on visibility, air quality index and mortality. The constructed calculation algorithm is dedicated to simulating the visibility in a single episode, first of all. It was derived after applying logistic regression methodology. It should be stressed that eight visibility thresholds (Vis) were adopted in order to build proper classification models with a number of relevant advantages. At first, there exists the possibility to analyze the impact of independent variables on visibility with the consideration of its’ real variability. Secondly, through the application of the Monte Carlo method and the assumed classification algorithms, it was made possible to model the number of days during a precipitation and no-precipitation periods in a yearly cycle, on which the visibility ranged practically: Vis < 8; Vis = 8–12 km, Vis = 12–16 km, Vis = 16–20 km, Vis = 20–24 km, Vis = 24–28 km, Vis = 28–32 km, Vis > 32 km. The derived algorithm proved a particular role of precipitation and no-precipitation periods in shaping the air visibility phenomena. Higher visibility values and a lower number of days with increased visibility were found for the precipitation period contrary to no-precipitation one. The air quality index was lower for precipitation days, and moreover, strong, non-linear relationships were found between mortality and visibility, considering precipitation and seasonality effects.

Various techniques of scale parameter estimation have been proposed in the case of alpha stable distributions. In the paper, the authors present an estimation technique that involves the k-th record theory. Although this theory is over 40 years old, its implementation in the classical extreme value theory – being the other cornerstone of the presented approach – is quite new, and tempting. Several theoretical properties of the introduced scale parameter estimators are presented. With the use of Monte Carlo methods, a comparative analysis is performed between the approach based on k-th records and approaches based on Hill’s and Pickands’ estimators. Additionally, the paper uses a real-life data set to illustrate how to effectively apply the k-th record estimator of the scale parameter. The research indicates several advantages of the k-th record approach over its other counterparts, especially when dealing with incomplete information about the underlying sample.